Sebastian Pokutta

My group is interested in Artificial Intelligence, Optimization, and Machine Learning. We develop new methodologies (e.g., new optimization and learning algorithms), work on combining learning and decision-making, as well as design AI Systems for real-world deployment in various application contexts.

📬 Contact

office: Room 3026 at ZIB
Room MA 606 at TUB
e-mail: pokutta (at) zib.de
pokutta (at) math.tu-berlin.de
homepage: pokutta.com

🎓 Curriculum vitae

since 2019: Professor at TUB
since 2019: Department Head at ZIB
since 2019: Vice President at ZIB
2005: Ph.D. in Mathematics at DUE
2003: Diploma in Mathematics at DUE

📝 Publications and preprints

Preprints

Wirth, E., Pena, J., and Pokutta, S. (2024). Fast Convergence of Frank-Wolfe Algorithms on Polytopes. [arXiv]

[BibTeX]

@misc{FastFWPolytope2024,
  archiveprefix = {arXiv},
  eprint = {2406.18789},
  primaryclass = {math.OC},
  year = {2024},
  author = {Wirth, Elias and Pena, Javier and Pokutta, Sebastian},
  title = {Fast Convergence of Frank-Wolfe Algorithms on Polytopes}
}

Mundinger, K., Zimmer, M., and Pokutta, S. (2024). Neural Parameter Regression for Explicit Representations of PDE Solution Operators. [arXiv]

[BibTeX]

@misc{NeuralRegressionPDE2024,
  archiveprefix = {arXiv},
  eprint = {2403.12764},
  primaryclass = {cs.LG},
  year = {2024},
  author = {Mundinger, Konrad and Zimmer, Max and Pokutta, Sebastian},
  title = {Neural Parameter Regression for Explicit Representations of PDE Solution Operators}
}

Roux, C., Zimmer, M., and Pokutta, S. (2024). On the Byzantine-resilience of Distillation-based Federated Learning. [arXiv]

[BibTeX]

@misc{byzantinefederated2024,
  archiveprefix = {arXiv},
  eprint = {2402.12265},
  primaryclass = {cs.LG},
  year = {2024},
  author = {Roux, Christophe and Zimmer, Max and Pokutta, Sebastian},
  title = {On the Byzantine-resilience of Distillation-based Federated Learning}
}

Göß, A., Martin, A., Pokutta, S., and Sharma, K. (2024). Norm-induced Cuts: Optimization with Lipschitzian Black-box Functions. [URL] [arXiv]

[BibTeX]

@misc{gmps_nic_23,
  url = {https://opus4.kobv.de/opus4-trr154/files/518/nic_preprint.pdf},
  archiveprefix = {arXiv},
  eprint = {2403.11546},
  primaryclass = {math.OC},
  year = {2024},
  author = {Göß, Adrian and Martin, Alexander and Pokutta, Sebastian and Sharma, Kartikey},
  title = {Norm-induced Cuts: Optimization with Lipschitzian Black-box Functions}
}

Wirth, E., Besançon, M., and Pokutta, S. (2024). The Pivoting Framework: Frank-Wolfe Algorithms with Active Set Size Control. [arXiv]

[BibTeX]

@misc{pivotingFW2024,
  archiveprefix = {arXiv},
  eprint = {2407.11760},
  primaryclass = {math.OC},
  year = {2024},
  author = {Wirth, Elias and Besançon, Mathieu and Pokutta, Sebastian},
  title = {The Pivoting Framework: Frank-Wolfe Algorithms with Active Set Size Control}
}

Wirth, E., Pena, J., and Pokutta, S. (2023). Accelerated Affine-invariant Convergence Rates of the Frank-Wolfe Algorithm with Open-loop Step-sizes. [arXiv]

[BibTeX]

@misc{FWopenLoopAccelerate2023,
  archiveprefix = {arXiv},
  eprint = {2310.04096},
  primaryclass = {math.OC},
  year = {2023},
  author = {Wirth, Elias and Pena, Javier and Pokutta, Sebastian},
  title = {Accelerated Affine-invariant Convergence Rates of the Frank-Wolfe Algorithm with Open-loop Step-sizes}
}

Stengl, S.-M., Gelß, P., Klus, S., and Pokutta, S. (2023). Existence and Uniqueness of Solutions of the Koopman–von Neumann Equation on Bounded Domains. [arXiv]

[BibTeX]

@misc{KoopmanNeumann2023,
  archiveprefix = {arXiv},
  eprint = {2306.13504},
  primaryclass = {math.AP},
  year = {2023},
  author = {Stengl, Steven-Marian and Gelß, Patrick and Klus, Stefan and Pokutta, Sebastian},
  title = {Existence and Uniqueness of Solutions of the Koopman--von Neumann Equation on Bounded Domains}
}

Sadiku, S., Wagner, M., and Pokutta, S. (2023). Group-wise Sparse and Explainable Adversarial Attacks. [arXiv]

[BibTeX]

@misc{groupadversarialattack2023,
  archiveprefix = {arXiv},
  eprint = {2311.17434},
  primaryclass = {cs.CV},
  year = {2023},
  author = {Sadiku, Shpresim and Wagner, Moritz and Pokutta, Sebastian},
  title = {Group-wise Sparse and Explainable Adversarial Attacks}
}

Martínez-Rubio, D., Roux, C., Criscitiello, C., and Pokutta, S. (2023). Accelerated Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties. [arXiv]

[BibTeX]

@misc{mrp_accelerated_minmax_riemannian_23,
  archiveprefix = {arXiv},
  eprint = {2305.16186},
  primaryclass = {math.OC},
  year = {2023},
  author = {Martínez-Rubio, David and Roux, Christophe and Criscitiello, Christopher and Pokutta, Sebastian},
  title = {Accelerated Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties}
}

Abbas, A., Ambainis, A., Augustino, B., Bärtschi, A., Buhrman, H., Coffrin, C., Cortiana, G., Dunjko, V., Egger, D. J., Elmegreen, B. G., Franco, N., Fratini, F., Fuller, B., Gacon, J., Gonciulea, C., Gribling, S., Gupta, S., Hadfield, S., Heese, R., … Zoufal, C. (2023). Quantum Optimization: Potential, Challenges, and the Path Forward. [arXiv]

[BibTeX]

@misc{qopc_path2023,
  archiveprefix = {arXiv},
  eprint = {2312.02279},
  primaryclass = {quant-ph},
  year = {2023},
  author = {Abbas, Amira and Ambainis, Andris and Augustino, Brandon and Bärtschi, Andreas and Buhrman, Harry and Coffrin, Carleton and Cortiana, Giorgio and Dunjko, Vedran and Egger, Daniel J. and Elmegreen, Bruce G. and Franco, Nicola and Fratini, Filippo and Fuller, Bryce and Gacon, Julien and Gonciulea, Constantin and Gribling, Sander and Gupta, Swati and Hadfield, Stuart and Heese, Raoul and Kircher, Gerhard and Kleinert, Thomas and Koch, Thorsten and Korpas, Georgios and Lenk, Steve and Marecek, Jakub and Markov, Vanio and Mazzola, Guglielmo and Mensa, Stefano and Mohseni, Naeimeh and Nannicini, Giacomo and O'Meara, Corey and Tapia, Elena Peña and Pokutta, Sebastian and Proissl, Manuel and Rebentrost, Patrick and Sahin, Emre and Symons, Benjamin C. B. and Tornow, Sabine and Valls, Victor and Woerner, Stefan and Wolf-Bauwens, Mira L. and Yard, Jon and Yarkoni, Sheir and Zechiel, Dirk and Zhuk, Sergiy and Zoufal, Christa},
  title = {Quantum Optimization: Potential, Challenges, and the Path Forward}
}

Scieur, D., Kerdreux, T., Martínez-Rubio, D., d’Aspremont, A., and Pokutta, S. (2023). Strong Convexity of Sets in Riemannian Manifolds. [arXiv]

[BibTeX]

@misc{skmap_strong_convexity_of_sets_in_riemannian_manifolds_23,
  archiveprefix = {arXiv},
  eprint = {2312.03583},
  primaryclass = {math.OC},
  year = {2023},
  author = {Scieur, Damien and Kerdreux, Thomas and Martínez-Rubio, David and d'Aspremont, Alexandre and Pokutta, Sebastian},
  title = {Strong Convexity of Sets in Riemannian Manifolds}
}

Woodstock, Z., and Pokutta, S. (2023). Splitting the Conditional Gradient Algorithm. [arXiv]

[BibTeX]

@misc{splitcg2023,
  archiveprefix = {arXiv},
  eprint = {2311.05381},
  primaryclass = {math.OC},
  year = {2023},
  author = {Woodstock, Zev and Pokutta, Sebastian},
  title = {Splitting the Conditional Gradient Algorithm}
}

Zimmer, M., Andoni, M., Spiegel, C., and Pokutta, S. (2023). PERP: Rethinking the Prune-Retrain Paradigm in the Era of LLMs. [arXiv] [code]

[BibTeX]

@misc{zasp_perp_23,
  archiveprefix = {arXiv},
  eprint = {2312.15230},
  primaryclass = {cs.LG},
  year = {2023},
  author = {Zimmer, Max and Andoni, Megi and Spiegel, Christoph and Pokutta, Sebastian},
  title = {PERP: Rethinking the Prune-Retrain Paradigm in the Era of LLMs},
  code = {https://github.com/ZIB-IOL/PERP}
}

Braun, G., Pokutta, S., and Weismantel, R. (2022). Alternating Linear Minimization: Revisiting von Neumann’s Alternating Projections. [arXiv] [slides] [video]

[BibTeX]

@misc{alternating_lmo_2022,
  archiveprefix = {arXiv},
  eprint = {2212.02933},
  primaryclass = {math.OC},
  year = {2022},
  author = {Braun, Gábor and Pokutta, Sebastian and Weismantel, Robert},
  title = {Alternating Linear Minimization: Revisiting von Neumann’s Alternating Projections},
  slides = {https://pokutta.com/slides/20230327-icerm.pdf},
  video = {https://icerm.brown.edu/programs/sp-s23/w2/#schedule-item-4945}
}

Braun, G., Carderera, A., Combettes, C., Hassani, H., Karbasi, A., Mokhtari, A., and Pokutta, S. (2022). Conditional Gradient Methods. [arXiv]

[BibTeX]

@misc{fw_survey_2022,
  archiveprefix = {arXiv},
  eprint = {2211.14103},
  primaryclass = {math.OC},
  year = {2022},
  author = {Braun, Gábor and Carderera, Alejandro and Combettes, Cyrille and Hassani, Hamed and Karbasi, Amin and Mokhtari, Aryan and Pokutta, Sebastian},
  title = {Conditional Gradient Methods}
}

Gelß, P., Klus, S., Shakibaei, Z., and Pokutta, S. (2022). Low-rank Tensor Decompositions of Quantum Circuits. [arXiv]

[BibTeX]

@misc{gksp_lowrankqc_22,
  archiveprefix = {arXiv},
  eprint = {2205.09882},
  primaryclass = {quant-ph},
  year = {2022},
  author = {Gelß, Patrick and Klus, Stefan and Shakibaei, Zarin and Pokutta, Sebastian},
  title = {Low-rank Tensor Decompositions of Quantum Circuits}
}

Hendrych, D., Troppens, H., Besançon, M., and Pokutta, S. (2022). Convex Integer Optimization with Frank-Wolfe Methods. [arXiv] [slides] [code]

[BibTeX]

@misc{htbp_cio_22,
  archiveprefix = {arXiv},
  eprint = {2208.11010},
  primaryclass = {math.OC},
  year = {2022},
  author = {Hendrych, Deborah and Troppens, Hannah and Besançon, Mathieu and Pokutta, Sebastian},
  title = {Convex Integer Optimization with Frank-Wolfe Methods},
  code = {https://github.com/ZIB-IOL/Boscia.jl},
  slides = {https://pokutta.com/slides/20220915_boscia.pdf}
}

Zimmer, M., Spiegel, C., and Pokutta, S. (2022). Compression-aware Training of Neural Networks Using Frank-Wolfe. [arXiv]

[BibTeX]

@misc{zsp_deepsparsefw_22,
  archiveprefix = {arXiv},
  eprint = {2205.11921},
  primaryclass = {cs.LG},
  year = {2022},
  author = {Zimmer, Max and Spiegel, Christoph and Pokutta, Sebastian},
  title = {Compression-aware Training of Neural Networks Using Frank-Wolfe}
}

Kerdreux, T., d’Aspremont, A., and Pokutta, S. (2021). Local and Global Uniform Convexity Conditions. Preprint. [arXiv]

[BibTeX]

@article{KAP2021,
  arxiv = {https://arxiv.org/abs/2102.05134},
  author = {Kerdreux, Thomas and d'Aspremont, Alexandre and Pokutta, Sebastian},
  month = feb,
  journal = {preprint},
  title = {{Local and Global Uniform Convexity Conditions}},
  year = {2021}
}

Carderera, A., Pokutta, S., Schütte, C., and Weiser, M. (2021). CINDy: Conditional gradient-based Identification of Non-linear Dynamics – Noise-robust recovery. Preprint. [arXiv] [summary]

[BibTeX]

@article{PSZ2020,
  arxiv = {https://arxiv.org/abs/2101.02630},
  author = {Carderera, Alejandro and Pokutta, Sebastian and Schütte, Christof and Weiser, Martin},
  month = jan,
  journal = {preprint},
  summary = {http://www.pokutta.com/blog/research/2021/01/16/cindy.html},
  title = {{CINDy: Conditional gradient-based Identification of Non-linear Dynamics -- Noise-robust recovery}},
  year = {2021}
}

Braun, G., and Pokutta, S. (2021). Dual Prices for Frank–Wolfe Algorithms. [arXiv]

[BibTeX]

@misc{dual_prices_FW_2021,
  archiveprefix = {arXiv},
  eprint = {2101.02087},
  primaryclass = {math.OC},
  year = {2021},
  author = {Braun, Gábor and Pokutta, Sebastian},
  title = {Dual Prices for Frank–Wolfe Algorithms}
}

Roux, C., Wirth, E., Pokutta, S., and Kerdreux, T. (2021). Efficient Online-bandit Strategies for Minimax Learning Problems. [arXiv]

[BibTeX]

@misc{online.bandit-minimax_2021,
  archiveprefix = {arXiv},
  eprint = {2105.13939},
  primaryclass = {cs.LG},
  year = {2021},
  author = {Roux, Christophe and Wirth, Elias and Pokutta, Sebastian and Kerdreux, Thomas},
  title = {Efficient Online-bandit Strategies for Minimax Learning Problems}
}

Combettes, C., Spiegel, C., and Pokutta, S. (2020). Projection-free Adaptive Gradients for Large-scale Optimization. [arXiv] [summary] [code]

[BibTeX]

@misc{csp_adafw_20,
  archiveprefix = {arXiv},
  eprint = {2009.14114},
  primaryclass = {math.OC},
  year = {2020},
  author = {Combettes, Cyrille and Spiegel, Christoph and Pokutta, Sebastian},
  title = {Projection-free Adaptive Gradients for Large-scale Optimization},
  code = {https://github.com/ZIB-IOL/StochasticFrankWolfe},
  summary = {https://pokutta.com/blog/research/2020/10/21/adasfw.html}
}

Pokutta, S., Spiegel, C., and Zimmer, M. (2020). Deep Neural Network Training with Frank-Wolfe. [arXiv] [summary] [code]

[BibTeX]

@misc{zsp_deepfw_20,
  archiveprefix = {arXiv},
  eprint = {2010.07243},
  primaryclass = {cs.LG},
  year = {2020},
  author = {Pokutta, Sebastian and Spiegel, Christoph and Zimmer, Max},
  title = {Deep Neural Network Training with Frank-Wolfe},
  code = {https://github.com/ZIB-IOL/StochasticFrankWolfe},
  summary = {https://pokutta.com/blog/research/2020/11/11/NNFW.html}
}

Carderera, A., and Pokutta, S. (2020). Second-order Conditional Gradient Sliding. Preprint. [arXiv] [summary] [code]

[BibTeX]

@article{CaP2020,
  arxiv = {https://arxiv.org/abs/2002.08907},
  author = {Carderera, Alejandro and Pokutta, Sebastian},
  code = {https://github.com/pokutta/Second-order-Conditional-Gradients},
  journal = {{preprint}},
  summary = {http://www.pokutta.com/blog/research/2020/06/20/socgs.html},
  title = {{Second-order Conditional Gradient Sliding}},
  year = {2020}
}

Bärmann, A., Martin, A., Pokutta, S., and Schneider, O. (2018). An Online-Learning Approach to Inverse Optimization. Submitted. [arXiv] [summary] [slides]

[BibTeX]

@article{BPS2017jour,
  arxiv = {https://arxiv.org/abs/1810.12997},
  author = {B{\"a}rmann, Andreas and Martin, Alexander and Pokutta, Sebastian and Schneider, Oskar},
  journal = {Submitted},
  slides = {https://app.box.com/s/7ti8sz8mf5s2znn1qo32in4jxy5gjnn1},
  summary = {http://www.pokutta.com/blog/research/2018/11/25/expertLearning-abstract.html},
  title = {{An Online-Learning Approach to Inverse Optimization}},
  year = {2018}
}

Braun, G., and Pokutta, S. (2016). An Efficient High-probability Algorithm for Linear Bandits. [arXiv]

[BibTeX]

@misc{Braun2016bandit,
  archiveprefix = {arXiv},
  eprint = {1610.02072},
  primaryclass = {cs.DS},
  year = {2016},
  author = {Braun, Gábor and Pokutta, Sebastian},
  title = {An Efficient High-probability Algorithm for Linear Bandits}
}

Braun, G., and Pokutta, S. (2015). An Information Diffusion Fano Inequality. [arXiv]

[BibTeX]

@misc{Braun2015Fano,
  archiveprefix = {arXiv},
  eprint = {1504.05492},
  primaryclass = {cs.IT},
  year = {2015},
  author = {Braun, Gábor and Pokutta, Sebastian},
  title = {An Information Diffusion Fano Inequality}
}

Conference proceedings

Sharma, K., Hendrych, D., Besançon, M., and Pokutta, S. (2024). Network Design for the Traffic Assignment Problem with Mixed-Integer Frank-Wolfe. Proceedings of INFORMS Optimization Society Conference. [arXiv]

[BibTeX]

@inproceedings{SharmaNetworkDesignTrafficFrankWolfe24,
  year = {2024},
  booktitle = {Proceedings of INFORMS Optimization Society Conference},
  archiveprefix = {arXiv},
  eprint = {2402.00166},
  primaryclass = {math.OC},
  author = {Sharma, Kartikey and Hendrych, Deborah and Besançon, Mathieu and Pokutta, Sebastian},
  title = {Network Design for the Traffic Assignment Problem with Mixed-Integer Frank-Wolfe}
}

Pauls, J., Zimmer, M., Kelly, U. M., Schwartz, M., Saatchi, S., Ciais, P., Pokutta, S., Brandt, M., and Gieseke, F. (2024). Estimating Canopy Height at Scale. Proceedings of International Conference on Machine Learning. [arXiv] [code]

[BibTeX]

@inproceedings{canopy2024,
  year = {2024},
  booktitle = {Proceedings of International Conference on Machine Learning},
  archiveprefix = {arXiv},
  eprint = {2406.01076},
  primaryclass = {cs.CV},
  author = {Pauls, Jan and Zimmer, Max and Kelly, Una M and Schwartz, Martin and Saatchi, Sassan and Ciais, Philippe and Pokutta, Sebastian and Brandt, Martin and Gieseke, Fabian},
  title = {Estimating Canopy Height at Scale},
  code = {https://github.com/AI4Forest/Global-Canopy-Height-Map}
}

Hendrych, D., Besançon, M., and Pokutta, S. (2024). Solving the Optimal Experiment Design Problem with Mixed-integer Convex Methods. Proceedings of Symposium on Experimental Algorithms. DOI: 10.4230/LIPIcs.SEA.2024.16 [arXiv] [code]

[BibTeX]

@inproceedings{design_of_experiments_boscia_23,
  year = {2024},
  booktitle = {Proceedings of Symposium on Experimental Algorithms},
  doi = {10.4230/LIPIcs.SEA.2024.16},
  archiveprefix = {arXiv},
  eprint = {2312.11200},
  primaryclass = {math.OC},
  author = {Hendrych, Deborah and Besançon, Mathieu and Pokutta, Sebastian},
  title = {Solving the Optimal Experiment Design Problem with Mixed-integer Convex Methods},
  code = {https://github.com/ZIB-IOL/OptimalDesignWithBoscia}
}

Kiem, A., Pokutta, S., and Spiegel, C. (2024). Categorification of Flag Algebras. Proceedings of Discrete Mathematics Days. [URL]

[BibTeX]

@inproceedings{kps_flagalgebracategory_24,
  year = {2024},
  booktitle = {Proceedings of Discrete Mathematics Days},
  url = {https://dmd2024.web.uah.es/files/abstracts/paper_47.pdf},
  author = {Kiem, Aldo and Pokutta, Sebastian and Spiegel, Christoph},
  title = {Categorification of Flag Algebras}
}

Kiem, A., Pokutta, S., and Spiegel, C. (2024). The 4-color Ramsey Multiplicity of Triangles. Proceedings of Discrete Mathematics Days. [URL] [arXiv] [code]

[BibTeX]

@inproceedings{kps_flagalgebrasymmetries_22,
  year = {2024},
  booktitle = {Proceedings of Discrete Mathematics Days},
  url = {https://dmd2024.web.uah.es/files/abstracts/paper_3.pdf},
  archiveprefix = {arXiv},
  eprint = {2312.08049},
  primaryclass = {math.CO},
  author = {Kiem, Aldo and Pokutta, Sebastian and Spiegel, Christoph},
  title = {The 4-color Ramsey Multiplicity of Triangles},
  code = {https://github.com/FordUniver/kps_trianglemult}
}

Mundinger, K., Pokutta, S., Spiegel, C., and Zimmer, M. (2024). Extending the Continuum of Six-Colorings. Proceedings of Discrete Mathematics Days. [URL] [arXiv]

[BibTeX]

@inproceedings{mpsz_hadwigernelsonspectrum_24:1,
  year = {2024},
  booktitle = {Proceedings of Discrete Mathematics Days},
  url = {https://dmd2024.web.uah.es/files/abstracts/paper_27.pdf},
  archiveprefix = {arXiv},
  eprint = {2404.05509},
  author = {Mundinger, Konrad and Pokutta, Sebastian and Spiegel, Christoph and Zimmer, Max},
  title = {Extending the Continuum of Six-Colorings}
}

Martínez-Rubio, D., Roux, C., and Pokutta, S. (2024). Convergence and Trade-offs in Riemannian Gradient Descent and Riemannian Proximal Point. Proceedings of International Conference on Machine Learning. [arXiv]

[BibTeX]

@inproceedings{mrp_tradeoffs_riemannian_gradient_descent_23,
  year = {2024},
  booktitle = {Proceedings of International Conference on Machine Learning},
  archiveprefix = {arXiv},
  eprint = {2403.10429},
  primaryclass = {math.OC},
  author = {Martínez-Rubio, David and Roux, Christophe and Pokutta, Sebastian},
  title = {Convergence and Trade-offs in Riemannian Gradient Descent and Riemannian Proximal Point}
}

Wäldchen, S., Sharma, K., Turan, B., Zimmer, M., and Pokutta, S. (2024). Interpretability Guarantees with Merlin-Arthur Classifiers. Proceedings of International Conference on Artificial Intelligence and Statistics. [arXiv]

[BibTeX]

@inproceedings{wszp_merlinarthur_22,
  year = {2024},
  booktitle = {Proceedings of International Conference on Artificial Intelligence and Statistics},
  archiveprefix = {arXiv},
  eprint = {2206.00759},
  primaryclass = {cs.LG},
  author = {Wäldchen, Stephan and Sharma, Kartikey and Turan, Berkant and Zimmer, Max and Pokutta, Sebastian},
  title = {Interpretability Guarantees with Merlin-Arthur Classifiers}
}

Zimmer, M., Spiegel, C., and Pokutta, S. (2024). Sparse Model Soups: A Recipe for Improved Pruning Via Model Averaging. Proceedings of International Conference on Learning Representations. [URL] [arXiv]

[BibTeX]

@inproceedings{zsp_modelsoup_23,
  year = {2024},
  booktitle = {Proceedings of International Conference on Learning Representations},
  url = {https://iclr.cc/virtual/2024/poster/17433},
  archiveprefix = {arXiv},
  eprint = {2306.16788},
  primaryclass = {cs.LG},
  author = {Zimmer, Max and Spiegel, Christoph and Pokutta, Sebastian},
  title = {Sparse Model Soups: A Recipe for Improved Pruning Via Model Averaging}
}

Chmiela, A., Gleixner, A., Lichocki, P., and Pokutta, S. (2023). Online Learning for Scheduling MIP Heuristics. Proceedings of International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, 114–123. DOI: 10.1007/978-3-031-33271-5_8

[BibTeX]

@inproceedings{ChmielaGleixnerLichockiPokutta2023_OnlineLearning,
  year = {2023},
  booktitle = {Proceedings of International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research},
  pages = {114-123},
  doi = {10.1007/978-3-031-33271-5_8},
  author = {Chmiela, Antonia and Gleixner, Ambros and Lichocki, Pawel and Pokutta, Sebastian},
  title = {Online Learning for Scheduling MIP Heuristics}
}

Thuerck, D., Sofranac, B., Pfetsch, M., and Pokutta, S. (2023). Learning Cuts Via Enumeration Oracles. Proceedings of Conference on Neural Information Processing Systems. [arXiv]

[BibTeX]

@inproceedings{learn_cut_oracle_2023,
  year = {2023},
  booktitle = {Proceedings of Conference on Neural Information Processing Systems},
  archiveprefix = {arXiv},
  eprint = {2305.12197},
  primaryclass = {math.OC},
  author = {Thuerck, Daniel and Sofranac, Boro and Pfetsch, Marc and Pokutta, Sebastian},
  title = {Learning Cuts Via Enumeration Oracles}
}

Martínez-Rubio, D., and Pokutta, S. (2023). Accelerated Riemannian Optimization: Handling Constraints with a Prox to Bound Geometric Penalties. Proceedings of Annual Conference on Learning Theory. [URL] [arXiv] [poster]

[BibTeX]

@inproceedings{mp_acceleratedriemannian_22,
  year = {2023},
  booktitle = {Proceedings of Annual Conference on Learning Theory},
  url = {https://proceedings.mlr.press/v195/martinez-rubio23a/martinez-rubio23a.pdf},
  archiveprefix = {arXiv},
  eprint = {2211.14645},
  primaryclass = {math.OC},
  author = {Martínez-Rubio, David and Pokutta, Sebastian},
  title = {Accelerated Riemannian Optimization: Handling Constraints with a Prox to Bound Geometric Penalties},
  poster = {https://pokutta.com/slides/20221203_poster_neurips_riemannian.pdf}
}

Martínez-Rubio, D., Wirth, E., and Pokutta, S. (2023). Accelerated and Sparse Algorithms for Approximate Personalized PageRank and Beyond. Proceedings of Annual Conference on Learning Theory. [arXiv]

[BibTeX]

@inproceedings{mwp_accelerated_sparse_pagerank_23,
  year = {2023},
  booktitle = {Proceedings of Annual Conference on Learning Theory},
  archiveprefix = {arXiv},
  eprint = {2303.12875},
  primaryclass = {math.OC},
  author = {Martínez-Rubio, David and Wirth, Elias and Pokutta, Sebastian},
  title = {Accelerated and Sparse Algorithms for Approximate Personalized PageRank and Beyond}
}

Parczyk, O., Pokutta, S., Spiegel, C., and Szabó, T. (2023). Fully Computer-assisted Proofs in Extremal Combinatorics. Proceedings of AAAI Conference on Artificial Intelligence. DOI: 10.1609/aaai.v37i10.26470 [URL] [arXiv] [code]

[BibTeX]

@inproceedings{ppss_ramsey_22:1,
  year = {2023},
  booktitle = {Proceedings of AAAI Conference on Artificial Intelligence},
  doi = {10.1609/aaai.v37i10.26470},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26470},
  archiveprefix = {arXiv},
  eprint = {2206.04036},
  primaryclass = {math.CO},
  author = {Parczyk, Olaf and Pokutta, Sebastian and Spiegel, Christoph and Szabó, Tibor},
  title = {Fully Computer-assisted Proofs in Extremal Combinatorics},
  code = {https://zenodo.org/record/6602512#.YyvFhi8Rr5g}
}

Wirth, E., Kerdreux, T., and Pokutta, S. (2023). Acceleration of Frank-Wolfe Algorithms with Open Loop Step-sizes. Proceedings of International Conference on Artificial Intelligence and Statistics. [arXiv]

[BibTeX]

@inproceedings{wkp_openloop_22,
  year = {2023},
  booktitle = {Proceedings of International Conference on Artificial Intelligence and Statistics},
  archiveprefix = {arXiv},
  eprint = {2205.12838},
  primaryclass = {math.OC},
  author = {Wirth, Elias and Kerdreux, Thomas and Pokutta, Sebastian},
  title = {Acceleration of Frank-Wolfe Algorithms with Open Loop Step-sizes}
}

Wirth, E., Kera, H., and Pokutta, S. (2023). Approximate Vanishing Ideal Computations at Scale. Proceedings of International Conference on Learning Representations. [arXiv] [slides]

[BibTeX]

@inproceedings{wkp_vanishingideal_22,
  year = {2023},
  booktitle = {Proceedings of International Conference on Learning Representations},
  archiveprefix = {arXiv},
  eprint = {2207.01236},
  primaryclass = {cs.LG},
  author = {Wirth, Elias and Kera, Hiroshi and Pokutta, Sebastian},
  title = {Approximate Vanishing Ideal Computations at Scale},
  slides = {https://pokutta.com/slides/20220915_avi_at_scale.pdf}
}

Zimmer, M., Spiegel, C., and Pokutta, S. (2023). How I Learned to Stop Worrying and Love Retraining. Proceedings of International Conference on Learning Representations. [URL] [arXiv] [code]

[BibTeX]

@inproceedings{zsp_retrain_21,
  year = {2023},
  booktitle = {Proceedings of International Conference on Learning Representations},
  url = {https://iclr.cc/virtual/2023/poster/10914},
  archiveprefix = {arXiv},
  eprint = {2111.00843},
  primaryclass = {cs.LG},
  author = {Zimmer, Max and Spiegel, Christoph and Pokutta, Sebastian},
  title = {How I Learned to Stop Worrying and Love Retraining},
  code = {https://github.com/ZIB-IOL/BIMP}
}

Gasse, M., Bowly, S., Cappart, Q., Charfreitag, J., Charlin, L., Chételat, D., Chmiela, A., Dumouchelle, J., Gleixner, A., Kazachkov, A. M., Khalil, E., Lichocki, P., Lodi, A., Lubin, M., Maddison, C. J., Christopher, M., Papageorgiou, D. J., Parjadis, A., Pokutta, S., … Kun, M. (2022). The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights. Proceedings of Conference on Neural Information Processing Systems, 176, 220–231. [URL] [arXiv]

[BibTeX]

@inproceedings{GasseEtal2022_ML4CO,
  year = {2022},
  booktitle = {Proceedings of Conference on Neural Information Processing Systems},
  month = jun,
  volume = {176},
  pages = {220–231},
  url = {https://proceedings.mlr.press/v176/gasse22a.html},
  archiveprefix = {arXiv},
  eprint = {2203.02433},
  primaryclass = {cs.LG},
  author = {Gasse, Maxime and Bowly, Simon and Cappart, Quentin and Charfreitag, Jonas and Charlin, Laurent and Chételat, Didier and Chmiela, Antonia and Dumouchelle, Justin and Gleixner, Ambros and Kazachkov, Aleksandr M. and Khalil, Elias and Lichocki, Pawel and Lodi, Andrea and Lubin, Miles and Maddison, Chris J. and Christopher, Morris and Papageorgiou, Dimitri J. and Parjadis, Augustin and Pokutta, Sebastian and Prouvost, Antoine and Scavuzzo, Lara and Zarpellon, Giulia and Yang, Linxin and Lai, Sha and Wang, Akang and Luo, Xiaodong and Zhou, Xiang and Huang, Haohan and Shao, Shengcheng and Zhu, Yuanming and Zhang, Dong and Quan, Tao and Cao, Zixuan and Xu, Yang and Huang, Zhewei and Zhou, Shuchang and Binbin, Chen and Minggui, He and Hao, Hao and Zhiyu, Zhang and Zhiwu, An and Kun, Mao},
  title = {The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights}
}

Criado, F., Martínez-Rubio, D., and Pokutta, S. (2022). Fast Algorithms for Packing Proportional Fairness and Its Dual. Proceedings of Conference on Neural Information Processing Systems. [arXiv] [poster]

[BibTeX]

@inproceedings{cmp_packing_proportional_fairness_22,
  year = {2022},
  booktitle = {Proceedings of Conference on Neural Information Processing Systems},
  archiveprefix = {arXiv},
  eprint = {2109.03678},
  primaryclass = {math.OC},
  author = {Criado, Francisco and Martínez-Rubio, David and Pokutta, Sebastian},
  title = {Fast Algorithms for Packing Proportional Fairness and Its Dual},
  poster = {https://pokutta.com/slides/20211105_fairpacking-poster.pdf}
}

Macdonald, J., Besançon, M., and Pokutta, S. (2022). Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings. Proceedings of International Conference on Machine Learning. [arXiv] [poster] [video]

[BibTeX]

@inproceedings{mbp_interpretfw_22,
  year = {2022},
  booktitle = {Proceedings of International Conference on Machine Learning},
  archiveprefix = {arXiv},
  eprint = {2110.08105},
  primaryclass = {cs.lG},
  author = {Macdonald, Jan and Besançon, Mathieu and Pokutta, Sebastian},
  title = {Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings},
  poster = {https://pokutta.com/slides/20220712_icml_poster_interpretable_rde.pdf},
  video = {https://slideslive.com/38983588}
}

Parczyk, O., Pokutta, S., Spiegel, C., and Szabó, T. (2022). New Ramsey Multiplicity Bounds and Search Heuristics. Proceedings of Discrete Mathematics Days. [arXiv] [code]

[BibTeX]

@inproceedings{ppss_ramsey_22:2,
  year = {2022},
  booktitle = {Proceedings of Discrete Mathematics Days},
  archiveprefix = {arXiv},
  eprint = {2206.04036},
  primaryclass = {math.CO},
  author = {Parczyk, Olaf and Pokutta, Sebastian and Spiegel, Christoph and Szabó, Tibor},
  title = {New Ramsey Multiplicity Bounds and Search Heuristics},
  code = {https://zenodo.org/record/6602512#.YyvFhi8Rr5g}
}

Tsuji, K., Tanaka, K., and Pokutta, S. (2022). Pairwise Conditional Gradients without Swap Steps and Sparser Kernel Herding. Proceedings of International Conference on Machine Learning. [arXiv] [summary] [slides] [code] [video]

[BibTeX]

@inproceedings{ttp_pairwise_22,
  year = {2022},
  booktitle = {Proceedings of International Conference on Machine Learning},
  archiveprefix = {arXiv},
  eprint = {2110.12650},
  primaryclass = {math.OC},
  author = {Tsuji, Kazuma and Tanaka, Ken'ichiro and Pokutta, Sebastian},
  title = {Pairwise Conditional Gradients without Swap Steps and Sparser Kernel Herding},
  code = {https://github.com/ZIB-IOL/FrankWolfe.jl},
  slides = {https://pokutta.com/slides/20220624_ICML2022_BPCG.pdf},
  summary = {https://pokutta.com/blog/research/2022/05/21/bpcg-abstract.html},
  video = {https://slideslive.com/38983561}
}

Wäldchen, S., Huber, F., and Pokutta, S. (2022). Training Characteristic Functions with Reinforcement Learning: XAI-methods Play Connect Four. Proceedings of International Conference on Machine Learning. [arXiv] [poster] [video]

[BibTeX]

@inproceedings{whp_connectfour_22,
  year = {2022},
  booktitle = {Proceedings of International Conference on Machine Learning},
  archiveprefix = {arXiv},
  eprint = {2202.11797},
  primaryclass = {cs.LG},
  author = {Wäldchen, Stephan and Huber, Felix and Pokutta, Sebastian},
  title = {Training Characteristic Functions with Reinforcement Learning: XAI-methods Play Connect Four},
  poster = {https://pokutta.com/slides/20220712_icml_poster_conn4.pdf},
  video = {https://slideslive.com/38983111}
}

Wirth, E., and Pokutta, S. (2022). Conditional Gradients for the Approximately Vanishing Ideal. Proceedings of International Conference on Artificial Intelligence and Statistics. [arXiv] [summary] [poster] [code]

[BibTeX]

@inproceedings{wp_approxvanideal_22,
  year = {2022},
  booktitle = {Proceedings of International Conference on Artificial Intelligence and Statistics},
  archiveprefix = {arXiv},
  eprint = {2202.03349},
  primaryclass = {cs.LG},
  author = {Wirth, Elias and Pokutta, Sebastian},
  title = {Conditional Gradients for the Approximately Vanishing Ideal},
  code = {https://github.com/ZIB-IOL/cgavi/},
  poster = {https://pokutta.com/slides/20220223_CGAVI_poster.pdf},
  summary = {https://pokutta.com/blog/research/2022/02/20/CGAVI.html}
}

Carderera, A., Besançon, M., and Pokutta, S. (2021). Simple Steps Are All You Need: Frank-Wolfe and Generalized Self-concordant Functions. Proceedings of Conference on Neural Information Processing Systems, 34, 5390–5401. [URL] [summary] [slides] [poster] [code]

[BibTeX]

@inproceedings{CBP2021:1,
  year = {2021},
  booktitle = {Proceedings of Conference on Neural Information Processing Systems},
  month = may,
  volume = {34},
  pages = {5390–5401},
  url = {https://proceedings.neurips.cc/paper_files/paper/2021/file/2b323d6eb28422cef49b266557dd31ad-Paper.pdf},
  author = {Carderera, Alejandro and Besançon, Mathieu and Pokutta, Sebastian},
  title = {Simple Steps Are All You Need: Frank-Wolfe and Generalized Self-concordant Functions},
  code = {https://doi.org/10.5281/zenodo.4836009},
  poster = {https://pokutta.com/slides/20211120_poster_NeurIPS21_lSimple_steps_are_all_you_need.pdf},
  slides = {https://pokutta.com/slides/20210710_FW-simpleSteps-SelfConcordance.pdf},
  summary = {https://pokutta.com/blog/research/2021/10/09/self-concordant-abstract.html}
}

Chmiela, A., Khalil, E. B., Gleixner, A., Lodi, A., and Pokutta, S. (2021). Learning to Schedule Heuristics in Branch-and-bound. Proceedings of Conference on Neural Information Processing Systems, 34, 24235–24246. [URL] [arXiv] [poster]

[BibTeX]

@inproceedings{CKGLP2021,
  year = {2021},
  booktitle = {Proceedings of Conference on Neural Information Processing Systems},
  month = mar,
  volume = {34},
  pages = {24235–24246},
  url = {https://proceedings.neurips.cc/paper_files/paper/2021/file/cb7c403aa312160380010ee3dd4bfc53-Paper.pdf},
  archiveprefix = {arXiv},
  eprint = {2103.10294},
  primaryclass = {cs.LG},
  author = {Chmiela, Antonia and Khalil, Elias B. and Gleixner, Ambros and Lodi, Andrea and Pokutta, Sebastian},
  title = {Learning to Schedule Heuristics in Branch-and-bound},
  poster = {https://pokutta.com/slides/20211120_poster_NeurIPS21_learningheuristics.pdf}
}

Carderera, A., Diakonikolas, J., Lin, C. Y., and Pokutta, S. (2021). Parameter-free Locally Accelerated Conditional Gradients. Proceedings of ICML. [arXiv] [slides]

[BibTeX]

@article{CDLP2021,
  arxiv = {https://arxiv.org/abs/2102.06806},
  author = {Carderera, Alejandro and Diakonikolas, Jelena and Lin, Cheuk Yin and Pokutta, Sebastian},
  month = feb,
  journal = {Proceedings of ICML},
  title = {{Parameter-free Locally Accelerated Conditional Gradients}},
  slides = {http://www.pokutta.com/slides/20210716_PF_LaCG_Poster.pdf},
  year = {2021}
}

Sofranac, B., Gleixner, A., and Pokutta, S. (2021). An Algorithm-independent Measure of Progress for Linear Constraint Propagation. Proceedings of International Conference on Principles and Practice of Constraint Programming, 52:1–17. DOI: 10.4230/LIPIcs.CP.2021.52 [URL] [arXiv] [video]

[BibTeX]

@inproceedings{SofranacGleixnerPokutta2022_AnAlgorithm-IndependentMeasure:1,
  year = {2021},
  booktitle = {Proceedings of International Conference on Principles and Practice of Constraint Programming},
  pages = {52:1–17},
  doi = {10.4230/LIPIcs.CP.2021.52},
  url = {https://drops.dagstuhl.de/opus/volltexte/2021/15343/},
  archiveprefix = {arXiv},
  eprint = {2106.07573},
  primaryclass = {math.OC},
  author = {Sofranac, Boro and Gleixner, Ambros and Pokutta, Sebastian},
  title = {An Algorithm-independent Measure of Progress for Linear Constraint Propagation},
  video = {https://youtu.be/paZtGYlkBfE}
}

Mortagy, H., Gupta, S., and Pokutta, S. (2020). Walking in the Shadow: A New Perspective on Descent Directions for Constrained Minimization. Proceedings of NeurIPS. [arXiv] [slides] [poster] [code] [video]

[BibTeX]

@article{MGP2020walking,
  arxiv = {https://arxiv.org/abs/2006.08426},
  author = {Mortagy, Hassan and Gupta, Swati and Pokutta, Sebastian},
  code = {https://github.com/pokutta/Walking-in-the-Shadow},
  poster = {https://app.box.com/s/y266djezdjdidsswaopvdcnsnkp8774i},
  journal = {{Proceedings of NeurIPS}},
  slides = {https://app.box.com/s/wjhpe4nh8kv5pw6vl5jbp902v3mksivs},
  video = {https://youtu.be/CRmASevfnmE},
  title = {{Walking in the Shadow: A New Perspective on Descent Directions for Constrained Minimization}},
  month = jun,
  year = {2020}
}

Combettes, C. W., and Pokutta, S. (2020). Boosting Frank-Wolfe by Chasing Gradients. Proceedings of ICML. [PDF] [arXiv] [summary] [slides] [code] [video]

[BibTeX]

@article{CP2020boost,
  annote = {github code: https://github.com/cyrillewcombettes/boostfw},
  arxiv = {http://arxiv.org/abs/2003.06369},
  author = {Combettes, Cyrille W and Pokutta, Sebastian},
  code = {https://colab.research.google.com/drive/1TSOVjDFF1X2ADBo_adHLsUVrblSutRKw},
  journal = {{Proceedings of ICML}},
  slides = {https://app.box.com/s/wwj247r5d456q0778p9b9y1jm6txuifb},
  summary = {http://www.pokutta.com/blog/research/2020/03/16/boostFW.html},
  title = {{Boosting Frank-Wolfe by Chasing Gradients}},
  pdfurl = {http://proceedings.mlr.press/v119/combettes20a.html},
  video = {https://www.youtube.com/watch?v=BfyV0C5FRbE},
  month = mar,
  year = {2020}
}

Sofranac, B., Gleixner, A., and Pokutta, S. (2020). Accelerating Domain Propagation: An Efficient GPU-parallel Algorithm Over Sparse Matrices. Proceedings of 10th IEEE/ACM Workshop on Irregular Applications: Architectures and Algorithms, IA3 2020, 1–11. DOI: 10.1109/IA351965.2020.00007 [arXiv] [summary] [slides] [video]

[BibTeX]

@inproceedings{SofranacGleixnerPokutta2022_Accelerating:1,
  year = {2020},
  booktitle = {Proceedings of 10th IEEE/ACM Workshop on Irregular Applications: Architectures and Algorithms, IA3 2020},
  pages = {1-11},
  doi = {10.1109/IA351965.2020.00007},
  archiveprefix = {arXiv},
  eprint = {2009.07785},
  primaryclass = {cs.DC},
  author = {Sofranac, Boro and Gleixner, Ambros and Pokutta, Sebastian},
  title = {Accelerating Domain Propagation: An Efficient GPU-parallel Algorithm Over Sparse Matrices},
  slides = {https://app.box.com/s/qy0pjmhtbm7shk2ypxjxlh2sj4nudvyu},
  summary = {https://pokutta.com/blog/research/2020/09/20/gpu-prob.html},
  video = {https://youtu.be/7mERPal9pVs}
}

Diakonikolas, J., Carderera, A., and Pokutta, S. (2020). Locally Accelerated Conditional Gradients. Proceedings of AISTATS. [PDF] [arXiv] [summary] [slides] [code] [video]

[BibTeX]

@article{CDP2019,
  arxiv = {https://arxiv.org/abs/1906.07867},
  author = {Diakonikolas, Jelena and Carderera, Alejandro and Pokutta, Sebastian},
  code = {https://colab.research.google.com/drive/1ejjfCan7xnEhWWJXCIzb03CwQRG9iW_O},
  journal = {{Proceedings of AISTATS}},
  slides = {https://app.box.com/s/gphkhapso7d1vrfnzqykkb3vx0agxh8w},
  summary = {http://www.pokutta.com/blog/research/2019/07/04/LaCG-abstract.html},
  title = {{Locally Accelerated Conditional Gradients}},
  pdfurl = {http://proceedings.mlr.press/v108/diakonikolas20a/diakonikolas20a.pdf},
  year = {2020},
  video = {https://slideslive.com/38930107/locally-accelerated-conditional-gradients?ref=account-folder-52123-folders}
}

Braun, G., Pokutta, S., Tu, D., and Wright, S. (2019). Blended Conditional Gradients: the Unconditioning of Conditional Gradients. Proceedings of International Conference on Machine Learning, 97, 735–743. [URL] [arXiv] [summary] [slides] [poster] [code]

[BibTeX]

@inproceedings{Braun2018blendedCG,
  year = {2019},
  booktitle = {Proceedings of International Conference on Machine Learning},
  volume = {97},
  pages = {735–743},
  url = {https://proceedings.mlr.press/v97/braun19a},
  archiveprefix = {arXiv},
  eprint = {1805.07311},
  primaryclass = {math.OC},
  author = {Braun, Gábor and Pokutta, Sebastian and Tu, Dan and Wright, Stephen},
  title = {Blended Conditional Gradients: the Unconditioning of Conditional Gradients},
  code = {https://github.com/pokutta/bcg},
  poster = {https://app.box.com/s/nmmm671jd72i397nysa8emfnzh1hn6hf},
  slides = {https://app.box.com/s/xbx3z7ws6dxvl3rzgj4jp6forigycooe},
  summary = {https://pokutta.com/blog/research/2019/02/18/bcg-abstract.html}
}

Diakonikolas, J., Carderera, A., and Pokutta, S. (2019). Breaking the Curse of Dimensionality (Locally) to Accelerate Conditional Gradients. OPTML Workshop Paper. [PDF] [arXiv] [summary] [slides] [poster] [code]

[BibTeX]

@article{CDPOPTML2019,
  arxiv = {https://arxiv.org/abs/1906.07867},
  author = {Diakonikolas, Jelena and Carderera, Alejandro and Pokutta, Sebastian},
  code = {https://colab.research.google.com/drive/1ejjfCan7xnEhWWJXCIzb03CwQRG9iW_O},
  journal = {{OPTML Workshop Paper}},
  pdfurl = {https://opt-ml.org/papers/2019/paper_26.pdf},
  poster = {https://app.box.com/s/d7p038u7df422q4jsccbmj15mngqv2ws},
  slides = {https://app.box.com/s/gphkhapso7d1vrfnzqykkb3vx0agxh8w},
  summary = {http://www.pokutta.com/blog/research/2019/07/04/LaCG-abstract.html},
  title = {{Breaking the Curse of Dimensionality (Locally) to Accelerate Conditional Gradients}},
  year = {2019}
}

Combettes, C. W., and Pokutta, S. (2019). Blended Matching Pursuit. Proceedings of NeurIPS. [PDF] [arXiv] [summary] [slides] [poster] [code]

[BibTeX]

@article{CP2019,
  arxiv = {https://arxiv.org/abs/1904.12335},
  author = {Combettes, Cyrille W and Pokutta, Sebastian},
  code = {https://colab.research.google.com/drive/17XYIxnCcJjKswba9mAaXFWnNGVZdsaXQ},
  journal = {{Proceedings of NeurIPS}},
  pdfurl = {https://papers.nips.cc/paper/8478-blended-matching-pursuit},
  poster = {https://app.box.com/s/j6lbh49udjd45krhxcypufxwdofw6br8},
  slides = {https://app.box.com/s/8lfktq6h3dqp9t2gqydu2tp8h2uxgz7m},
  summary = {http://www.pokutta.com/blog/research/2019/05/27/bmp-abstract.html},
  title = {{Blended Matching Pursuit}},
  year = {2019}
}

Braun, G., Pokutta, S., and Zink, D. (2017). Lazifying Conditional Gradient Algorithms. Proceedings of International Conference on Machine Learning, 70, 566–575. [URL] [arXiv] [slides] [poster]

[BibTeX]

@inproceedings{Braun2017lazyCG:1,
  year = {2017},
  booktitle = {Proceedings of International Conference on Machine Learning},
  volume = {70},
  pages = {566–575},
  url = {https://proceedings.mlr.press/v70/braun17a},
  archiveprefix = {arXiv},
  eprint = {1610.05120},
  primaryclass = {cs.DS},
  author = {Braun, Gábor and Pokutta, Sebastian and Zink, Daniel},
  title = {Lazifying Conditional Gradient Algorithms},
  poster = {https://app.box.com/s/lysscdg17ytpz7mqr0tu2djffyqvkl6a},
  slides = {https://app.box.com/s/zsp0hixjz2ha23u1vuyosijjkjdh8k}
}

Roy, A., Xu, H., and Pokutta, S. (2017). Reinforcement Learning under Model Mismatch. Proceedings of NIPS. [arXiv]

[BibTeX]

@article{AXP2016,
  annote = {https://papers.nips.cc/paper/by-source-2016-1199
  
  https://arxiv.org/abs/1610.09269
  
  },
  arxiv = {https://arxiv.org/abs/1706.04711},
  author = {Roy, Aurko and Xu, Huan and Pokutta, Sebastian},
  journal = {{Proceedings of NIPS}},
  title = {{Reinforcement Learning under Model Mismatch}},
  year = {2017}
}

Bärmann, A., Pokutta, S., and Schneider, O. (2017). Emulating the Expert: Inverse Optimization through Online Learning. Proceedings of the International Conference on Machine Learning (ICML). [PDF] [arXiv] [summary] [slides] [poster] [video]

[BibTeX]

@article{BPS2017,
  arxiv = {https://arxiv.org/abs/1810.12997},
  author = {B{\"a}rmann, Andreas and Pokutta, Sebastian and Schneider, Oskar},
  journal = {{Proceedings of the International Conference on Machine Learning (ICML)}},
  pdfurl = {http://proceedings.mlr.press/v70/barmann17a.html},
  poster = {https://app.box.com/s/h3ychkv99il7oyzw7sym8la5lhit3elg},
  slides = {https://app.box.com/s/7ti8sz8mf5s2znn1qo32in4jxy5gjnn1},
  summary = {http://www.pokutta.com/blog/research/2018/11/25/expertLearning-abstract.html},
  title = {{Emulating the Expert: Inverse Optimization through Online Learning}},
  video = {https://vimeo.com/channels/1301905/237243668},
  year = {2017}
}

Braun, G., Pokutta, S., and Roy, A. (2016). Strong Reductions for Extended Formulations. Proceedings of Conference on Integer Programming and Combinatorial Optimization, 9682, 350–361. DOI: 10.1007/978-3-319-33461-5_29 [arXiv]

[BibTeX]

@inproceedings{BPR2015:1,
  year = {2016},
  booktitle = {Proceedings of Conference on Integer Programming and Combinatorial Optimization},
  month = jun,
  volume = {9682},
  pages = {350–361},
  doi = {10.1007/978-3-319-33461-5_29},
  archiveprefix = {arXiv},
  eprint = {1512.04932},
  primaryclass = {cs.CC},
  author = {Braun, Gábor and Pokutta, Sebastian and Roy, Aurko},
  title = {Strong Reductions for Extended Formulations}
}

Braun, G., Brown-Cohen, J., Huq, A., Pokutta, S., Raghavendra, P., Roy, A., Weitz, B., and Zink, D. (2016). The Matching Problem Has No Small Symmetric SDP. Proceedings of Symposium on Discrete Algorithms, 1067–1078. DOI: 10.1137/1.9781611974331.ch75 [arXiv]

[BibTeX]

@inproceedings{Braun2016matching-symmetricSDP:1,
  year = {2016},
  booktitle = {Proceedings of Symposium on Discrete Algorithms},
  pages = {1067–1078},
  doi = {10.1137/1.9781611974331.ch75},
  archiveprefix = {arXiv},
  eprint = {1504.00703},
  primaryclass = {cs.CC},
  author = {Braun, Gábor and Brown-Cohen, Jonah and Huq, Arefin and Pokutta, Sebastian and Raghavendra, Prasad and Roy, Aurko and Weitz, Benjamin and Zink, Daniel},
  title = {The Matching Problem Has No Small Symmetric SDP}
}

Roy, A., and Pokutta, S. (2016). Hierarchical Clustering via Spreading Metrics. Proceedings of NIPS. [URL] [arXiv]

[BibTeX]

@article{AP2016,
  arxiv = {https://arxiv.org/abs/1610.09269},
  author = {Roy, Aurko and Pokutta, Sebastian},
  journal = {{Proceedings of NIPS}},
  url = {https://papers.nips.cc/paper/by-source-2016-1199},
  special = {{Oral Presentation + Conference Proceedings}},
  title = {{Hierarchical Clustering via Spreading Metrics}},
  year = {2016}
}

Braun, G., Pokutta, S., and Zink, D. (2015). Inapproximability of Combinatorial Problems Via Small LPs and SDPs. Proceedings of Annual Symposium on Theory of Computing, 107–116. DOI: 10.1145/2746539.2746550 [arXiv] [video]

[BibTeX]

@inproceedings{Braun2015LP-SDP:1,
  year = {2015},
  booktitle = {Proceedings of Annual Symposium on Theory of Computing},
  month = jun,
  pages = {107–116},
  doi = {10.1145/2746539.2746550},
  archiveprefix = {arXiv},
  eprint = {1410.8816},
  primaryclass = {cs.CC},
  author = {Braun, Gábor and Pokutta, Sebastian and Zink, Daniel},
  title = {Inapproximability of Combinatorial Problems Via Small LPs and SDPs},
  video = {https://youtu.be/MxLEticZ8RY}
}

Braun, G., and Pokutta, S. (2015). The Matching Polytope Does Not Admit Fully-polynomial Size Relaxation Schemes. Proceedings of Symposium on Discrete Algorithms, 837–846. DOI: 10.1137/1.9781611973730.57 [arXiv]

[BibTeX]

@inproceedings{Braun2014matching,
  year = {2015},
  booktitle = {Proceedings of Symposium on Discrete Algorithms},
  pages = {837-846},
  doi = {10.1137/1.9781611973730.57},
  archiveprefix = {arXiv},
  eprint = {1403.6710},
  primaryclass = {cs.CC},
  author = {Braun, Gábor and Pokutta, Sebastian},
  title = {The Matching Polytope Does Not Admit Fully-polynomial Size Relaxation Schemes}
}

Braun, G., Firorini, S., and Pokutta, S. (2014). Average Case Polyhedral Complexity of the Maximum Stable Set Problem. Proceedings of Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, 28, 515–530. DOI: 10.4230/LIPIcs.APPROX-RANDOM.2014.515 [URL] [arXiv]

[BibTeX]

@inproceedings{Braun2013stable:1,
  year = {2014},
  booktitle = {Proceedings of Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques},
  month = sep,
  volume = {28},
  pages = {515–530},
  doi = {10.4230/LIPIcs.APPROX-RANDOM.2014.515},
  url = {https://drops.dagstuhl.de/opus/volltexte/2014/4720},
  archiveprefix = {arXiv},
  eprint = {1311.4001},
  primaryclass = {cs.CC},
  author = {Braun, Gábor and Firorini, Samuel and Pokutta, Sebastian},
  title = {Average Case Polyhedral Complexity of the Maximum Stable Set Problem}
}

Braun, G., Pokutta, S., and Xie, Y. (2014). Info-greedy Sequential Adaptive Compressed Sensing. Proceedings of Allerton Conference on Communication, Control, and Computing (Allerton), 858–865. DOI: 10.1109/ALLERTON.2014.7028544 [arXiv]

[BibTeX]

@inproceedings{Braun2014info-greedy:1,
  year = {2014},
  booktitle = {Proceedings of Allerton Conference on Communication, Control, and Computing (Allerton)},
  pages = {858–865},
  doi = {10.1109/ALLERTON.2014.7028544},
  archiveprefix = {arXiv},
  eprint = {1407.0731},
  primaryclass = {cs.IT},
  author = {Braun, Gábor and Pokutta, Sebastian and Xie, Yao},
  title = {Info-greedy Sequential Adaptive Compressed Sensing}
}

Braun, G., and Pokutta, S. (2013). Common Information and Unique Disjointness. Proceedings of Annual Symposium on Foundations of Computer Science, 688–697. [URL]

[BibTeX]

@inproceedings{Braun2013info-disjoint:1,
  year = {2013},
  booktitle = {Proceedings of Annual Symposium on Foundations of Computer Science},
  pages = {688–697},
  url = {https://eccc.weizmann.ac.il/report/2013/056},
  author = {Braun, Gábor and Pokutta, Sebastian},
  title = {Common Information and Unique Disjointness}
}

Schmaltz, C., Pokutta, S., Heidorn, T., and Andrae, S. (2013). How to make regulators and shareholders happy under Basel III. Proceedings of the 26th Australasian Finance and Banking Conference. DOI: 10.1016/j.jbankfin.2014.05.031 [URL]

[BibTeX]

@article{basel2012,
  doi = {10.1016/j.jbankfin.2014.05.031},
  url = {http://ssrn.com/abstract=2179490},
  author = {Schmaltz, Christian and Pokutta, Sebastian and Heidorn, Thomas and Andrae, Silvio},
  journal = {{Proceedings of the 26th Australasian Finance and Banking Conference}},
  title = {{How to make regulators and shareholders happy under Basel III}},
  year = {2013}
}

Briët, J., Dadush, D., and Pokutta, S. (2013). On the existence of 0/1 polytopes with high semidefinite extension complexity. Proceedings of ESA. [arXiv]

[BibTeX]

@article{BDP2013,
  arxiv = {http://arxiv.org/abs/1305.3268},
  author = {Bri\"et, Jop and Dadush, Daniel and Pokutta, Sebastian},
  journal = {{Proceedings of ESA}},
  title = {{On the existence of 0/1 polytopes with high semidefinite extension complexity}},
  year = {2013}
}

Braun, G., and Pokutta, S. (2012). An Algebraic Approach to Symmetric Extended Formulations. Proceedings of International Symposium on Combinatorial Optimization, 7422, 141–152. DOI: 10.1007/978-3-642-32147-4_14 [arXiv]

[BibTeX]

@inproceedings{Braun2012symEF,
  year = {2012},
  booktitle = {Proceedings of International Symposium on Combinatorial Optimization},
  month = apr,
  volume = {7422},
  pages = {141–152},
  doi = {10.1007/978-3-642-32147-4_14},
  archiveprefix = {arXiv},
  eprint = {1206.6318},
  primaryclass = {cs.CC},
  author = {Braun, Gábor and Pokutta, Sebastian},
  title = {An Algebraic Approach to Symmetric Extended Formulations}
}

Braun, G., Firorini, S., Pokutta, S., and Steurer, D. (2012). Approximation Limits of Linear Programs (beyond Hierarchies). Proceedings of Annual Symposium on Foundations of Computer Science, 480–489. DOI: 10.1109/FOCS.2012.10 [arXiv]

[BibTeX]

@inproceedings{Braun2012UDISJ:1,
  year = {2012},
  booktitle = {Proceedings of Annual Symposium on Foundations of Computer Science},
  pages = {480–489},
  doi = {10.1109/FOCS.2012.10},
  archiveprefix = {arXiv},
  eprint = {1204.0957},
  primaryclass = {c.CC},
  author = {Braun, Gábor and Firorini, Samuel and Pokutta, Sebastian and Steurer, David},
  title = {Approximation Limits of Linear Programs (beyond Hierarchies)}
}

Braun, G., and Pokutta, S. (2010). Rank of Random Half-integral Polytopes. Proceedings of Electronic Notes in Discrete Mathematics, 36, 415–422. DOI: 10.1016/j.endm.2010.05.053 [URL]

[BibTeX]

@inproceedings{Braun2011half:1,
  year = {2010},
  booktitle = {Proceedings of Electronic Notes in Discrete Mathematics},
  month = aug,
  volume = {36},
  pages = {415–422},
  doi = {10.1016/j.endm.2010.05.053},
  url = {https://optimization-online.org/DB_HTML/2010/11/2813.html},
  author = {Braun, Gábor and Pokutta, Sebastian},
  title = {Rank of Random Half-integral Polytopes}
}

Full articles

Carderera, A., Besançon, M., and Pokutta, S. (2024). Scalable Frank-Wolfe on Generalized Self-concordant Functions Via Simple Steps. SIAM Journal on Optimization, 34(3), 2231–2258. DOI: 10.1137/23M1616789 [summary] [slides] [poster] [code]

[BibTeX]

@article{CBP2021,
  year = {2024},
  journal = {SIAM Journal on Optimization},
  month = sep,
  volume = {34},
  number = {3},
  pages = {2231-2258},
  doi = {10.1137/23M1616789},
  author = {Carderera, Alejandro and Besançon, Mathieu and Pokutta, Sebastian},
  title = {Scalable Frank-Wolfe on Generalized Self-concordant Functions Via Simple Steps},
  code = {https://doi.org/10.5281/zenodo.4836009},
  poster = {https://pokutta.com/slides/20211120_poster_NeurIPS21_lSimple_steps_are_all_you_need.pdf},
  slides = {https://pokutta.com/slides/20210710_FW-simpleSteps-SelfConcordance.pdf},
  summary = {https://pokutta.com/blog/research/2021/10/09/self-concordant-abstract.html}
}

Braun, G., Guzmán, C., and Pokutta, S. (2024). Corrections to “Lower Bounds on the Oracle Complexity of Nonsmooth Convex Optimization Via Information Theory.” IEEE Transactions on Information Theory, 70, 5408–5409. DOI: 10.1109/TIT.2024.3357200

[BibTeX]

@article{Braun2013lowerCO_corr,
  year = {2024},
  journal = {IEEE Transactions on Information Theory},
  month = jul,
  volume = {70},
  pages = {5408-5409},
  doi = {10.1109/TIT.2024.3357200},
  author = {Braun, Gábor and Guzmán, Cristóbal and Pokutta, Sebastian},
  title = {Corrections to “Lower Bounds on the Oracle Complexity of Nonsmooth Convex Optimization Via Information Theory”}
}

Pokutta, S. (2024). The Frank-Wolfe Algorithm: a Short Introduction. Jahresbericht Der Deutschen Mathematiker-Vereinigung, 126(1), 3–35. DOI: 10.1365/s13291-023-00275-x [URL] [arXiv]

[BibTeX]

@article{fwshortintro2023,
  year = {2024},
  journal = {Jahresbericht der Deutschen Mathematiker-Vereinigung},
  month = mar,
  volume = {126},
  number = {1},
  pages = {3–35},
  doi = {10.1365/s13291-023-00275-x},
  url = {https://link.springer.com/article/10.1365/s13291-023-00275-x/fulltext.html},
  archiveprefix = {arXiv},
  eprint = {2311.05313},
  primaryclass = {math.OC},
  author = {Pokutta, Sebastian},
  title = {The Frank-Wolfe Algorithm: a Short Introduction}
}

Deza, A., Pokutta, S., and Pournin, L. (2024). The Complexity of Geometric Scaling. Operations Research Letters, 52. DOI: 10.1016/j.orl.2023.11.010 [arXiv]

[BibTeX]

@article{dpp_geometric_scaling_22,
  year = {2024},
  journal = {Operations Research Letters},
  volume = {52},
  doi = {10.1016/j.orl.2023.11.010},
  archiveprefix = {arXiv},
  eprint = {2205.04063},
  primaryclass = {math.OC},
  author = {Deza, Antoine and Pokutta, Sebastian and Pournin, Lionel},
  title = {The Complexity of Geometric Scaling}
}

Deza, A., Onn, S., Pokutta, S., and Pournin, L. (2024). Kissing Polytopes. SIAM Journal on Discrete Mathematics. [arXiv]

[BibTeX]

@article{kissing_polytopes_2023,
  year = {2024},
  journal = {SIAM Journal on Discrete Mathematics},
  archiveprefix = {arXiv},
  eprint = {2305.18597},
  primaryclass = {math.MG},
  author = {Deza, Antoine and Onn, Shmuel and Pokutta, Sebastian and Pournin, Lionel},
  title = {Kissing Polytopes}
}

Mundinger, K., Pokutta, S., Spiegel, C., and Zimmer, M. (2024). Extending the Continuum of Six-Colorings. Geombinatorics Quarterly, XXXIV. [URL] [arXiv]

[BibTeX]

@article{mpsz_hadwigernelsonspectrum_24,
  year = {2024},
  journal = {Geombinatorics Quarterly},
  volume = {XXXIV},
  url = {https://geombina.uccs.edu/past-issues/volume-xxxiv},
  archiveprefix = {arXiv},
  eprint = {2404.05509},
  author = {Mundinger, Konrad and Pokutta, Sebastian and Spiegel, Christoph and Zimmer, Max},
  title = {Extending the Continuum of Six-Colorings}
}

Parczyk, O., Pokutta, S., Spiegel, C., and Szabó, T. (2024). New Ramsey Multiplicity Bounds and Search Heuristics. Foundations of Computational Mathematics. [arXiv] [code]

[BibTeX]

@article{ppss_ramsey_22,
  year = {2024},
  journal = {Foundations of Computational Mathematics},
  archiveprefix = {arXiv},
  eprint = {2206.04036},
  primaryclass = {math.CO},
  author = {Parczyk, Olaf and Pokutta, Sebastian and Spiegel, Christoph and Szabó, Tibor},
  title = {New Ramsey Multiplicity Bounds and Search Heuristics},
  code = {https://zenodo.org/record/6602512#.YyvFhi8Rr5g}
}

Designolle, S., Vértesi, T., and Pokutta, S. (2024). Symmetric Multipartite Bell Inequalities Via Frank-Wolfe Algorithms. Physics Review A. [arXiv]

[BibTeX]

@article{symmetricBell2023,
  year = {2024},
  journal = {Physics Review A},
  archiveprefix = {arXiv},
  eprint = {2310.20677},
  primaryclass = {quant-ph},
  author = {Designolle, Sébastien and Vértesi, Tamás and Pokutta, Sebastian},
  title = {Symmetric Multipartite Bell Inequalities Via Frank-Wolfe Algorithms}
}

Designolle, S., Iommazzo, G., Besançon, M., Knebel, S., Gelß, P., and Pokutta, S. (2023). Improved Local Models and New Bell Inequalities Via Frank-Wolfe Algorithms. Physical Review Research, 5(4). DOI: 10.1103/PhysRevResearch.5.043059 [arXiv] [slides] [code]

[BibTeX]

@article{dibkgp_bell_23,
  year = {2023},
  journal = {Physical Review Research},
  month = oct,
  volume = {5},
  number = {4},
  doi = {10.1103/PhysRevResearch.5.043059},
  archiveprefix = {arXiv},
  eprint = {2302.04721},
  primaryclass = {quant-ph},
  author = {Designolle, Sébastien and Iommazzo, Gabriele and Besançon, Mathieu and Knebel, Sebastian and Gelß, Patrick and Pokutta, Sebastian},
  title = {Improved Local Models and New Bell Inequalities Via Frank-Wolfe Algorithms},
  code = {https://github.com/ZIB-IOL/BellPolytopes.jl},
  slides = {https://www.pokutta.com/slides/20230808-tokyo-bell.pdf}
}

Kevin-Martin, A., Bärmann, A., Braun, K., Liers, F., Pokutta, S., Schneider, O., Sharma, K., and Tschuppik, S. (2023). Data-driven Distributionally Robust Optimization Over Time. INFORMS Journal on Optimization. [arXiv]

[BibTeX]

@article{abblpsst_distributionally_ro_23,
  year = {2023},
  journal = {INFORMS Journal on Optimization},
  archiveprefix = {arXiv},
  eprint = {2304.05377},
  primaryclass = {math.OC},
  author = {Kevin-Martin, Aigner and Bärmann, Andreas and Braun, Kristin and Liers, Frauke and Pokutta, Sebastian and Schneider, Oskar and Sharma, Kartikey and Tschuppik, Sebastian},
  title = {Data-driven Distributionally Robust Optimization Over Time}
}

Kreimeier, T., Pokutta, S., Walther, A., and Woodstock, Z. (2023). On a Frank-Wolfe Approach for Abs-smooth Functions. Optimization Methods and Software. [arXiv]

[BibTeX]

@article{abssmooth-fw_2023,
  year = {2023},
  journal = {Optimization Methods and Software},
  archiveprefix = {arXiv},
  eprint = {2303.09881},
  primaryclass = {math.OC},
  author = {Kreimeier, Timo and Pokutta, Sebastian and Walther, Andrea and Woodstock, Zev},
  title = {On a Frank-Wolfe Approach for Abs-smooth Functions}
}

Bienstock, D., Muñoz, G., and Pokutta, S. (2023). Principled Deep Neural Network Training Through Linear Programming. Discrete Optimization. [URL] [arXiv] [summary]

[BibTeX]

@article{principledDNN_LP_2018,
  year = {2023},
  journal = {Discrete Optimization},
  url = {https://www.sciencedirect.com/science/article/abs/pii/S1572528623000373},
  archiveprefix = {arXiv},
  eprint = {1810.03218},
  primaryclass = {cs.LG},
  author = {Bienstock, Daniel and Muñoz, Gonzalo and Pokutta, Sebastian},
  title = {Principled Deep Neural Network Training Through Linear Programming},
  summary = {https://www.pokutta.com/blog/research/2018/10/12/DNN-learning-lp-abstract.html}
}

Combettes, C. W., and Pokutta, S. (2023). Revisiting the Approximate Carathéodory Problem via the Frank-Wolfe Algorithm. Mathematical Programming A, 197, 191–214. DOI: 10.1007/s10107-021-01735-x [PDF] [arXiv] [summary] [slides] [code] [video]

[BibTeX]

@article{cp2019approxCara,
  arxiv = {https://arxiv.org/abs/1911.04415},
  doi = {10.1007/s10107-021-01735-x},
  author = {Combettes, Cyrille W and Pokutta, Sebastian},
  code = {https://colab.research.google.com/drive/1GLGRTc2jFYy9CqqoVnZgIFVQgAC0c3aZ},
  journal = {Mathematical Programming A},
  volume = {197},
  pages = {191–-214},
  slides = {https://app.box.com/s/f0zuvr45qa6etidd06i1wart58o7bc8t},
  summary = {http://www.pokutta.com/blog/research/2019/11/30/approxCara-abstract.html},
  title = {{Revisiting the Approximate Carath{\'e}odory Problem via the Frank-Wolfe Algorithm}},
  video = {https://www.youtube.com/watch?v=VB1e0HrDmVo},
  pdfurl = {https://rdcu.be/cCnPL},
  year = {2023}
}

Besançon, M., Carderera, A., and Pokutta, S. (2022). FrankWolfe.jl: A High-Performance and Flexible Toolbox for Frank-Wolfe Algorithms and Conditional Gradients. INFORMS Journal on Computing. [arXiv] [summary] [slides] [code]

[BibTeX]

@article{besanccon2022frankwolfe,
  year = {2022},
  journal = {INFORMS Journal on Computing},
  month = feb,
  archiveprefix = {arXiv},
  eprint = {2104.06675},
  primaryclass = {math.OC},
  author = {Besançon, Mathieu and Carderera, Alejandro and Pokutta, Sebastian},
  title = {FrankWolfe.jl: A High-Performance and Flexible Toolbox for Frank-Wolfe Algorithms and Conditional Gradients},
  code = {https://github.com/ZIB-IOL/FrankWolfe.jl},
  slides = {https://pokutta.com/slides/20210710_FW-simpleSteps-SelfConcordance.pdf},
  summary = {https://pokutta.com/blog/research/2021/04/20/FrankWolfejl.html}
}

Sofranac, B., Gleixner, A., and Pokutta, S. (2022). Accelerating Domain Propagation: An Efficient GPU-parallel Algorithm Over Sparse Matrices. Parallel Computing, 109, 102874. DOI: 10.1016/j.parco.2021.102874 [arXiv] [summary]

[BibTeX]

@article{SofranacGleixnerPokutta2022_Accelerating,
  year = {2022},
  journal = {Parallel Computing},
  volume = {109},
  pages = {102874},
  doi = {10.1016/j.parco.2021.102874},
  archiveprefix = {arXiv},
  eprint = {2009.07785},
  primaryclass = {cs.DC},
  author = {Sofranac, Boro and Gleixner, Ambros and Pokutta, Sebastian},
  title = {Accelerating Domain Propagation: An Efficient GPU-parallel Algorithm Over Sparse Matrices},
  summary = {https://pokutta.com/blog/research/2020/09/20/gpu-prob.html}
}

Sofranac, B., Gleixner, A., and Pokutta, S. (2022). An Algorithm-independent Measure of Progress for Linear Constraint Propagation. Constraints, 27, 432–455. DOI: 10.1007/s10601-022-09338-9 [arXiv]

[BibTeX]

@article{SofranacGleixnerPokutta2022_AnAlgorithm-IndependentMeasure,
  year = {2022},
  journal = {Constraints},
  volume = {27},
  pages = {432-455},
  doi = {10.1007/s10601-022-09338-9},
  archiveprefix = {arXiv},
  eprint = {2106.07573},
  primaryclass = {math.OC},
  author = {Sofranac, Boro and Gleixner, Ambros and Pokutta, Sebastian},
  title = {An Algorithm-independent Measure of Progress for Linear Constraint Propagation}
}

Hunkenschröder, C., Pokutta, S., and Weismantel, R. (2022). Optimizing a Low-dimensional Convex Function Over a High-dimensional Cube. SIAM Journal on Optimization. [arXiv]

[BibTeX]

@article{hpw_lowhigh_22,
  year = {2022},
  journal = {SIAM Journal on Optimization},
  archiveprefix = {arXiv},
  eprint = {2204.05266},
  primaryclass = {math.OC},
  author = {Hunkenschröder, Christoph and Pokutta, Sebastian and Weismantel, Robert},
  title = {Optimizing a Low-dimensional Convex Function Over a High-dimensional Cube}
}

Kossen, T., Hirzel, M. A., Madai, V. I., Boenisch, F., Hennemuth, A., Hildebrand, K., Pokutta, S., Sharma, K., Hilbert, A., Sobesky, J., Galinovic, I., Khalil, A. A., Fiebach, J. B., and Frey, D. (2022). Towards Sharing Brain Images: Differentially Private TOF-MRA Images with Segmentation Labels Using Generative Adversarial Networks. Frontiers in Artificial Intelligence. DOI: 10.3389/frai.2022.813842

[BibTeX]

@article{khmbhhpshsgkff_22,
  year = {2022},
  journal = {Frontiers in Artificial Intelligence},
  doi = {10.3389/frai.2022.813842},
  author = {Kossen, Tabea and Hirzel, Manuel A. and Madai, Vince I. and Boenisch, Franziska and Hennemuth, Anja and Hildebrand, Kristian and Pokutta, Sebastian and Sharma, Kartikey and Hilbert, Adam and Sobesky, Jan and Galinovic, Ivana and Khalil, Ahmed A. and Fiebach, Jochen B. and Frey, Dietmar},
  title = {Towards Sharing Brain Images: Differentially Private TOF-MRA Images with Segmentation Labels Using Generative Adversarial Networks}
}

Faenza, Y., Muñoz, G., and Pokutta, S. (2022). New Limits of Treewidth-based tractability in Optimization. Mathematical Programming A, 191, 559–594. [PDF] [arXiv] [summary]

[BibTeX]

@article{FMP2018,
  arxiv = {https://arxiv.org/abs/1807.02551},
  author = {Faenza, Yuri and Mu{\~n}oz, Gonzalo and Pokutta, Sebastian},
  journal = {{Mathematical Programming A}},
  pdfurl = {http://link.springer.com/article/10.1007/s10107-020-01563-5},
  summary = {http://www.pokutta.com/blog/research/2018/09/22/treewidth-abstract.html},
  title = {{New Limits of Treewidth-based tractability in Optimization}},
  volume = {191},
  pages = {559--594},
  year = {2022}
}

Combettes, C. W., and Pokutta, S. (2021). Complexity of Linear Minimization and Projection on Some Sets. Operations Research Letters, 49(4). [arXiv] [code]

[BibTeX]

@article{CP2021,
  arxiv = {https://arxiv.org/abs/2101.10040},
  author = {Combettes, Cyrille W and Pokutta, Sebastian},
  code = {https://github.com/cyrillewcombettes/complexity},
  journal = {Operations Research Letters},
  month = jul,
  volume = {49},
  number = {4},
  title = {{Complexity of Linear Minimization and Projection on Some Sets}},
  year = {2021}
}

Kerdreux, T., Roux, C., d’Aspremont, A., and Pokutta, S. (2021). Linear Bandits on Uniformly Convex Sets. Journal of Machine Learning Research, 22(284), 1–23. [URL] [arXiv] [summary]

[BibTeX]

@article{KRAP2021,
  year = {2021},
  journal = {Journal of Machine Learning Research},
  month = mar,
  volume = {22},
  number = {284},
  pages = {1–23},
  url = {http://jmlr.org/papers/v22/21-0277.html},
  archiveprefix = {arXiv},
  eprint = {2103.05907},
  primaryclass = {cs.LG},
  author = {Kerdreux, Thomas and Roux, Christophe and d'Aspremont, Alexandre and Pokutta, Sebastian},
  title = {Linear Bandits on Uniformly Convex Sets},
  summary = {https://www.pokutta.com/blog/research/2021/04/03/linearBandits.html}
}

Braun, G., Pokutta, S., and Zink, D. (2019). Affine Reductions for LPs and SDPs. Mathematical Programming, 173, 281–312. DOI: 10.1007/s10107-017-1221-9 [arXiv]

[BibTeX]

@article{Braun2015LP-SDP,
  year = {2019},
  journal = {Mathematical Programming},
  volume = {173},
  pages = {281–312},
  doi = {10.1007/s10107-017-1221-9},
  archiveprefix = {arXiv},
  eprint = {1410.8816},
  primaryclass = {cs.CC},
  author = {Braun, Gábor and Pokutta, Sebastian and Zink, Daniel},
  title = {Affine Reductions for LPs and SDPs}
}

Braun, G., Pokutta, S., and Zink, D. (2019). Lazifying Conditional Gradient Algorithms. The Journal of Machine Learning Research, 20(71), 1–42. [URL] [arXiv]

[BibTeX]

@article{Braun2017lazyCG,
  year = {2019},
  journal = {The Journal of Machine Learning Research},
  volume = {20},
  number = {71},
  pages = {1–42},
  url = {https://jmlr.org/papers/v20/18-114.html},
  archiveprefix = {arXiv},
  eprint = {1610.05120},
  primaryclass = {cs.DS},
  author = {Braun, Gábor and Pokutta, Sebastian and Zink, Daniel},
  title = {Lazifying Conditional Gradient Algorithms}
}

Braun, G., Pokutta, S., and Roy, A. (2018). Strong Reductions for Extended Formulations. Mathematical Programming, 172, 591–620. DOI: 10.1007/s10107-018-1316-y [arXiv]

[BibTeX]

@article{BPR2015,
  year = {2018},
  journal = {Mathematical Programming},
  month = nov,
  volume = {172},
  pages = {591–620},
  doi = {10.1007/s10107-018-1316-y},
  archiveprefix = {arXiv},
  eprint = {1512.04932},
  primaryclass = {cs.CC},
  author = {Braun, Gábor and Pokutta, Sebastian and Roy, Aurko},
  title = {Strong Reductions for Extended Formulations}
}

Braun, G., Brown-Cohen, J., Huq, A., Pokutta, S., Raghavendra, P., Roy, A., Weitz, B., and Zink, D. (2017). The Matching Problem Has No Small Symmetric SDP. Mathematical Programming, 165, 643–662. DOI: 10.1007/s10107-016-1098-z [arXiv]

[BibTeX]

@article{Braun2016matching-symmetricSDP,
  year = {2017},
  journal = {Mathematical Programming},
  month = oct,
  volume = {165},
  pages = {643–662},
  doi = {10.1007/s10107-016-1098-z},
  archiveprefix = {arXiv},
  eprint = {1504.00703},
  primaryclass = {cs.CC},
  author = {Braun, Gábor and Brown-Cohen, Jonah and Huq, Arefin and Pokutta, Sebastian and Raghavendra, Prasad and Roy, Aurko and Weitz, Benjamin and Zink, Daniel},
  title = {The Matching Problem Has No Small Symmetric SDP}
}

Braun, G., Guzmán, C., and Pokutta, S. (2017). Unifying Lower Bounds on the Oracle Complexity of Nonsmooth Convex Optimization. IEEE Transactions on Information Theory, 63(7), 4709–4724. DOI: 10.1109/TIT.2017.2701343 [arXiv]

[BibTeX]

@article{Braun2013lowerCO,
  year = {2017},
  journal = {IEEE Transactions on Information Theory},
  month = jul,
  volume = {63},
  number = {7},
  pages = {4709-4724},
  doi = {10.1109/TIT.2017.2701343},
  archiveprefix = {arXiv},
  eprint = {1407.5144},
  primaryclass = {math.OC},
  author = {Braun, Gábor and Guzmán, Cristóbal and Pokutta, Sebastian},
  title = {Unifying Lower Bounds on the Oracle Complexity of Nonsmooth Convex Optimization}
}

Braun, G., Jain, R., Lee, T., and Pokutta, S. (2017). Information-theoretic Approximations of the Nonnegative Rank. Computational Complexity, 26, 147–197. DOI: 10.1007/s00037-016-0125-z [URL]

[BibTeX]

@article{Braun2017info-nonnegative-rank,
  year = {2017},
  journal = {Computational Complexity},
  volume = {26},
  pages = {147–197},
  doi = {10.1007/s00037-016-0125-z},
  url = {https://eccc.weizmann.ac.il/report/2013/158},
  author = {Braun, Gábor and Jain, Rahul and Lee, Troy and Pokutta, Sebastian},
  title = {Information-theoretic Approximations of the Nonnegative Rank}
}

Roy, A., and Pokutta, S. (2017). Hierarchical Clustering via Spreading Metrics. Journal of Machine Learning Research (JMLR), 18, 1–35. [URL] [arXiv]

[BibTeX]

@article{AP2016jour,
  annote = {https://papers.nips.cc/paper/by-source-2016-1199
  },
  arxiv = {https://arxiv.org/abs/1610.09269},
  author = {Roy, Aurko and Pokutta, Sebastian},
  journal = {{Journal of Machine Learning Research (JMLR)}},
  pages = {1--35},
  url = {http://jmlr.org/papers/v18/17-081.html},
  title = {{Hierarchical Clustering via Spreading Metrics}},
  volume = {18},
  year = {2017}
}

Bodur, M., Del Pia, A., Dey, S. S., Molinaro, M., and Pokutta, S. (2017). Aggregation-based cutting-planes for packing and covering Integer Programs. To Appear in Mathematical Programming A. DOI: 10.1007/s10107-017-1192-x [arXiv]

[BibTeX]

@article{BDDMP2016,
  doi = {10.1007/s10107-017-1192-x},
  arxiv = {http://arxiv.org/abs/1606.08951},
  author = {Bodur, Merve and Del~Pia, Alberto and Dey, Santanu~Sabush and Molinaro, Marco and Pokutta, Sebastian},
  journal = {to appear in Mathematical Programming A},
  title = {{Aggregation-based cutting-planes for packing and covering Integer Programs}},
  year = {2017}
}

Martin, A., Müller, J., Pape, S., Peter, A., Pokutta, S., and Winter, T. (2017). Pricing and clearing combinatorial markets with singleton and swap orders. Mathematical Methods of Operations Research, 85(2), 155–177. [arXiv]

[BibTeX]

@article{futMar2014,
  arxiv = {http://arxiv.org/abs/1404.6546},
  author = {Martin, Alexander and M{\"u}ller, J. and Pape, S. and Peter, A. and Pokutta, Sebastian and Winter, T.},
  journal = {{Mathematical Methods of Operations Research}},
  number = {2},
  pages = {155--177},
  title = {{Pricing and clearing combinatorial markets with singleton and swap orders}},
  volume = {85},
  year = {2017}
}

Gatzert, N., Pokutta, S., and Vogl, N. (2016+). Convergence of Capital and Insurance Markets: Pricing Aspects of Index-Linked Catastrophic Loss Instruments. To Appear in Journal of Risk and Insurance. [arXiv]

[BibTeX]

@article{gpv2013,
  arxiv = {http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2320370},
  author = {Gatzert, Nadine and Pokutta, Sebastian and Vogl, Nikolai},
  journal = {to appear in Journal of Risk and Insurance},
  title = {{Convergence of Capital and Insurance Markets: Pricing Aspects of Index-Linked Catastrophic Loss Instruments}},
  special = {{Best paper award at European Group of Risk and Insurance Economists Annual Meeting 2014 for conference version}},
  year = {2016+}
}

Braun, G., Firorini, S., and Pokutta, S. (2016). Average Case Polyhedral Complexity of the Maximum Stable Set Problem. Mathematical Programming, 160(1), 407–431. DOI: 10.1007/s10107-016-0989-3 [arXiv]

[BibTeX]

@article{Braun2013stable,
  year = {2016},
  journal = {Mathematical Programming},
  month = mar,
  volume = {160},
  number = {1},
  pages = {407–431},
  doi = {10.1007/s10107-016-0989-3},
  archiveprefix = {arXiv},
  eprint = {1311.4001},
  primaryclass = {cs.CC},
  author = {Braun, Gábor and Firorini, Samuel and Pokutta, Sebastian},
  title = {Average Case Polyhedral Complexity of the Maximum Stable Set Problem}
}

Braun, G., and Pokutta, S. (2016). Common Information and Unique Disjointness. Algorithmica, 76(3), 597–629. DOI: 10.1007/s00453-016-0132-0 [URL]

[BibTeX]

@article{Braun2013info-disjoint,
  year = {2016},
  journal = {Algorithmica},
  month = feb,
  volume = {76},
  number = {3},
  pages = {597–629},
  doi = {10.1007/s00453-016-0132-0},
  url = {https://eccc.weizmann.ac.il/report/2013/056},
  author = {Braun, Gábor and Pokutta, Sebastian},
  title = {Common Information and Unique Disjointness}
}

Braun, G., and Pokutta, S. (2016). A Polyhedral Characterization of Border Bases. SIAM Journal on Discrete Mathematics, 30(1), 239–265. DOI: 10.1137/140977990 [arXiv]

[BibTeX]

@article{Braun2009borderbaseis,
  year = {2016},
  journal = {SIAM Journal on Discrete Mathematics},
  volume = {30},
  number = {1},
  pages = {239–265},
  doi = {10.1137/140977990},
  archiveprefix = {arXiv},
  eprint = {0912.1502},
  primaryclass = {math.AC},
  author = {Braun, Gábor and Pokutta, Sebastian},
  title = {A Polyhedral Characterization of Border Bases}
}

Bärmann, A., Heidt, A., Martin, A., Pokutta, S., and Thurner, C. (2016). Polyhedral Approximation of Ellipsoidal Uncertainty Sets via Extended Formulations - a computational case study. Computational Management Science, 13(2), 151–193. [PDF] [arXiv]

[BibTeX]

@article{robopt2014,
  annote = {http://link.springer.com/article/10.1007/s10287-015-0243-0
  
  DOI:10.1007/s10287-015-0243-0},
  arxiv = {http://www.optimization-online.org/DB_HTML/2014/02/4245.html},
  author = {B{\"a}rmann, Andreas and Heidt, Andreas and Martin, Alexander and Pokutta, Sebastian and Thurner, Christoph},
  journal = {{Computational Management Science}},
  number = {2},
  pages = {151--193},
  pdfurl = {http://link.springer.com/article/10.1007/s10287-015-0243-0},
  title = {{Polyhedral Approximation of Ellipsoidal Uncertainty Sets via Extended Formulations - a computational case study}},
  volume = {13},
  year = {2016}
}

Braun, G., Firorini, S., Pokutta, S., and Steurer, D. (2015). Approximation Limits of Linear Programs (beyond Hierarchies). Mathematics of Operations Research, 40(3), 756–772. DOI: 10.1287/moor.2014.0694 [arXiv]

[BibTeX]

@article{Braun2012UDISJ,
  year = {2015},
  journal = {Mathematics of Operations Research},
  month = aug,
  volume = {40},
  number = {3},
  pages = {756-772},
  doi = {10.1287/moor.2014.0694},
  archiveprefix = {arXiv},
  eprint = {1204.0957},
  primaryclass = {c.CC},
  author = {Braun, Gábor and Firorini, Samuel and Pokutta, Sebastian and Steurer, David},
  title = {Approximation Limits of Linear Programs (beyond Hierarchies)}
}

Braun, G., Pokutta, S., and Xie, Y. (2015). Info-greedy Sequential Adaptive Compressed Sensing. IEEE Journal of Selected Topics in Signal Processing, 9(4), 601–611. DOI: 10.1109/JSTSP.2015.2400428 [arXiv]

[BibTeX]

@article{Braun2014info-greedy,
  year = {2015},
  journal = {IEEE Journal of Selected Topics in Signal Processing},
  month = jun,
  volume = {9},
  number = {4},
  pages = {601–611},
  doi = {10.1109/JSTSP.2015.2400428},
  archiveprefix = {arXiv},
  eprint = {1407.0731},
  primaryclass = {cs.IT},
  author = {Braun, Gábor and Pokutta, Sebastian and Xie, Yao},
  title = {Info-greedy Sequential Adaptive Compressed Sensing}
}

Briët, J., Dadush, D., and Pokutta, S. (2015). On the existence of 0/1 polytopes with high semidefinite extension complexity. Mathematical Programming B, 153(1), 179–199. [arXiv]

[BibTeX]

@article{BDP2013jour,
  arxiv = {http://arxiv.org/abs/1305.3268},
  author = {Bri\"et, Jop and Dadush, Daniel and Pokutta, Sebastian},
  journal = {Mathematical Programming B},
  number = {1},
  pages = {179--199},
  title = {{On the existence of 0/1 polytopes with high semidefinite extension complexity}},
  volume = {153},
  year = {2015}
}

Braun, G., and Pokutta, S. (2014). A Short Proof for the Polyhedrality of the Chvátal-Gomory Closure of a Compact Convex Set. Operations Research Letters, 42(5), 307–310. DOI: 10.1016/j.orl.2014.05.004 [arXiv]

[BibTeX]

@article{Braun2012ChvatalGomory-compact,
  year = {2014},
  journal = {Operations Research Letters},
  month = jul,
  volume = {42},
  number = {5},
  pages = {307–310},
  doi = {10.1016/j.orl.2014.05.004},
  archiveprefix = {arXiv},
  eprint = {1207.4884},
  primaryclass = {math.CO},
  author = {Braun, Gábor and Pokutta, Sebastian},
  title = {A Short Proof for the Polyhedrality of the Chvátal-Gomory Closure of a Compact Convex Set}
}

Schmaltz, C., Pokutta, S., Heidorn, T., and Andrae, S. (2014). How to make regulators and shareholders happy under Basel III. Journal of Banking and Finance, 311–325. [PDF] [arXiv]

[BibTeX]

@article{basel2012jour,
  annote = {10.1016/j.jbankfin.2014.05.031},
  arxiv = {http://ssrn.com/abstract=2179490},
  author = {Schmaltz, Christian and Pokutta, Sebastian and Heidorn, Thomas and Andrae, Silvio},
  journal = {{Journal of Banking and Finance}},
  pages = {311--325},
  pdfurl = {http://dx.doi.org/10.1016/j.jbankfin.2014.05.031},
  title = {{How to make regulators and shareholders happy under Basel III}},
  year = {2014}
}

Braun, G., and Pokutta, S. (2012). Rigid Abelian Groups and the Probabilistic Method. Contemporary Mathematcs, 576, 17–30. DOI: 10.1090/conm/576 [arXiv]

[BibTeX]

@article{Braun2011rigid,
  year = {2012},
  journal = {Contemporary Mathematcs},
  volume = {576},
  pages = {17–30},
  doi = {10.1090/conm/576},
  archiveprefix = {arXiv},
  eprint = {1107.2325},
  primaryclass = {math.GR},
  author = {Braun, Gábor and Pokutta, Sebastian},
  title = {Rigid Abelian Groups and the Probabilistic Method}
}

Braun, G., and Pokutta, S. (2011). Random Half-integral Polytopes. Operations Research Letters, 39(3), 204–207. DOI: 10.1016/j.orl.2011.03.003 [URL]

[BibTeX]

@article{Braun2011half,
  year = {2011},
  journal = {Operations Research Letters},
  month = may,
  volume = {39},
  number = {3},
  pages = {204–207},
  doi = {10.1016/j.orl.2011.03.003},
  url = {https://optimization-online.org/DB_HTML/2010/11/2813.html},
  author = {Braun, Gábor and Pokutta, Sebastian},
  title = {Random Half-integral Polytopes}
}

🔬 Projects

AI-Based High-Resolution Forest Monitoring

Preserving forests is crucial for climate adaptation and mitigation. Accurate, up-to-date forest health data is essential. AI4Forest aims to develop advanced AI methods to monitor forests using satellite, radar, and LiDAR data. The project will create scalable techniques for detailed, high-resolution forest maps, updated weekly across Europe and globally.

AI4Forest

Jun 2023 to May 2027

Decomposition Methods for Mixed-integer Optimal Control

In this project, we study domain decomposition approaches for optimal control in gas transport networks. Our goal is to couple space-time-domain decomposition with machine learning and mixed-integer programming. We will develop NeTI (Network Tearing and Interconnection), a data-driven and physics-informed algorithm combining mixed-integer nonlinear programming, surrogate model learning, and graph decomposition strategies.

TRR-154 A05

Jan 2022 to Jun 2026

Expanding Merlin-Arthur Classifiers: Interpretable Neural Networks Through Interactive Proof Systems

Existing approaches for interpreting Neural Network classifiers that highlight features relevant for a decision are based solely on heuristics. We introduce a theory that allows us to bound the quality of the features without assumptions on the classifier model by relating classification to Interactive Proof Systems.

MATH+ EF1-24

Apr 2023 to Mar 2026

On a Frank-Wolfe Approach for Abs-smooth Optimization

Motivated by nonsmooth problems in machine learning, we solve the problem of minimizing an abs-smooth function subject to closed convex constraints. New theory and algorithms are developed using linear minimization oracles to enforce constraints and abs-linearization methods to handle nonsmoothness.

MATH+ EF1-23

Apr 2023 to Mar 2026

Convex Solver Adaptivity for Mixed-integer Optimization

We will investigate mixed-integer optimization with convex objectives using error-adaptive convex solvers in branch-and-bound. Focusing on improving lower bounds and balancing computational costs, we aim to develop a faster branch-and-bound methodology by leveraging modern MILP techniques and error-adaptive methods. Key aspects include warm-starting and controlled inexactness in early termination.

MATH+ AA3-15

Apr 2023 to Mar 2026

Entanglement Detection Via Frank-Wolfe Algorithms

We will apply a Frank-Wolfe-based approach for separability certification and entanglement detection of multipartite quantum states. The method will be further exploited to derive entanglement witnesses in the case, often encountered in experiments, of incomplete characterisation of the quantum state.

MATH+ AA2-19

Jan 2024 to Dec 2025

LEAN on Me: Transforming Mathematics Through Formal Verification, Improved Tactics, and Machine Learning

Formal proof verification can both ensure proof correctness and provide new tools and insights to mathematicians. The goals of this project include creating resources for students and researchers, verifying relevant results, improving proof tactics, and exploring Machine Learning approaches.

MATH+ AA5-9

Jan 2024 to Dec 2025

Scaling Up Flag Algebras in Combinatorics

This project aims to obtain new bounds in Extremal Combinatorics through an application of flag algebras. The goal is to both improve the underlying computational aspects for existing problems as well as to further develop the theory of flag algebras to extend it to new areas of application.

MATH+ EF1-21

Oct 2022 to Sep 2025

Quantum Algorithms for Optimization

Computational devices and quantum mechanics revolutionized the 20th century. Quantum computation merges these fields to solve optimization problems faster than classical computers. This project aims to develop new quantum algorithms for general-purpose and specific applications, including optimization, mixed-integer programs, and uses in machine learning, logistics, big data, and physics. We will explore quantum dynamic programming, graph sparsification, and QAOA-type algorithms for small quantum computers.

QOPT

May 2022 to Apr 2025

Approximating Combinatorial Optimization Problems

This project will explore to what extent near-term quantum computing may potentially provide better approximations to combinatorial optimization problems, bringing together expertise from quantum computing and applied mathematics. It is a collaborative effort within the Einstein Research Unit on Quantum Devices, involving the Freie Universität Berlin (FU Berlin), the Weierstrass Institute for Applied Analysis and Stochastics (WIAS), and the Zuse Institute Berlin (ZIB).

ACOP

Jan 2022 to Dec 2024

Learning Extremal Structures in Combinatorics

Extremal Combinatorics focuses on the maximum or minimum sizes of discrete structures with specific properties, posing significant challenges due to their complexity. Traditional computational approaches often fail due to exponential growth in search spaces, but recent AI advancements, especially in Reinforcement Learning, offer new potential. Applying these AI methods could provide insights into combinatorial problems while also enhancing the understanding of AI techniques in complex, sparse reward environments.

MATH+ EF1-12

Jan 2021 to May 2024

Decision-making for Energy Network Dynamics

We develop theory and algorithms for 0-1 decision making in optimization problems constrained by partial differential equations. By exploring extended formulations, we achieve new stationarity concepts through sequential exact and approximative relaxation of adjoint-based primal-dual optimality conditions.

MATH+ AA4-7

Jun 2021 to May 2024

MiniMIP: a Faster, More Reliable, and Easier Way to Maintain and Solve MIP Problems

MiniMIP is an open source, machine learning oriented Mixed-Integer Programming (MIP) solver. We provide a range of interfaces for all aspects of solving MIPs (e.g. heuristics, cut generators, LP solvers), supplying users with a constant view of the internal state and allowing them to propose modifications that are integrated into the global state internally.

miniMIP

Jan 2021 to Dec 2023

Sparsity and Sample-size Efficiency in Structured Learning

In this project, we study algorithms that promote sparsity. We develop PageRank optimization algorithms that scale with solution sparsity and investigate Riemannian optimization using manifold geometry. Additionally, we develop algorithms for efficient fair resource allocation based on established fairness axioms.

MATH+ AA5-1

Jan 2022 to Dec 2023

Learning to Schedule Heuristics in IP

Heuristics play a crucial role in exact solvers for Mixed Integer Programming (MIP). However, the question of how to manage multiple MIP heuristics in a solver has not received sufficient attention. This project addresses the strategic management of primal heuristics in MIP solvers, aiming to replace static, hard-coded rules with dynamic, self-improving procedures.

HLEARN

Nov 2021 to Oct 2023

Theoretical Foundations of Deep Learning

Deep learning is revolutionizing real-world applications and science, replacing or complementing classical model-based methods in solving mathematical problems. Despite successes, deep neural networks lack strong theoretical-mathematical foundations. This program aims to develop a comprehensive theoretical foundation of deep learning from three perspectives: statistical, application, and mathematical-methodological. The research is interdisciplinary, combining mathematics, statistics, and theoretical computer science to address complex questions.

SPP 2298, project number 441826958

May 2020 to May 2023

Adaptive Algorithms Through Machine Learning: Exploiting Interactions in Integer Programming

The performance of modern mixed-integer program solvers is highly dependent on a number of interdependent individual components. Using tools from machine learning, we intend to develop an integrated framework that is able to capture interactions of individual decisions made in these components with the ultimate goal to improve performance.

MATH+ EF1-9

Jan 2021 to Dec 2022

Beyond the Worst-case: Data-dependent Rates in Learning and Optimization

Worst-case complexity bounds are increasingly insufficient to explain the (often superior) real-world performance of optimization and learning algorithms. We consider data-dependent rates, approximation guarantees, and complexity bounds to provide guarantees much more in line with actual performance.

MATH+ AA3-7

Jan 2021 to Dec 2022

Daten- Und Prozessbasierte Zustandsermittlung Für Eine Wertorientierte Komponentenbewirtschaftung in Imperfekter Umgebung

Airplane data quality is uneven due to varied sources and sensor limitations. This project aims to create data processing services for Component Spotting to help airlines optimize business processes and improve customer satisfaction. We will develop methods for modeling and evaluating data quality, creating a semantic and uniform description for processing models. Using AI algorithms, we will handle inaccuracies and uncertainties to form an analysis-friendly information model.

DaProko

Jul 2020 to Dec 2022

Globally Optimal Neural Network Training

Training artificial neural networks is a key optimization task in deep learning. To improve generalization, robustness, and explainability, we aim to compute globally optimal solutions. We will use integer programming methods, exploiting mixed-integer nonlinear programming and enhancing solving techniques like spatial branch-and-cut. Additionally, we'll leverage symmetry to reduce computational burden and ensure symmetry in solutions, and incorporate true sparsity using a mixed-integer nonlinear programming framework.

GONNT

Mar 2021 to Feb 2022

Quantum Computing and Integer Programming

Quantum computing has the potential to occupy an important position in the field of computing in the long term. In the short to medium term, however, we can expect the emergence of so-called "Noisy Intermediate-Scale Quantum algorithms" and "Quantum Approximate Optimization Algorithms" in an initial phase. These algorithms can often solve a special subclass of problems approximately and very quickly using quantum computing and are significantly easier to implement technically. In this pilot project, a workflow will be tested, ranging from the design of hybrid quantum-classical algorithms to their simulation using quantum simulators on HPC hardware.

QCIP

Apr 2021 to Dec 2021

💬 Talks and posters

Conference and workshop talks

Jul 2024: German <-> Japanese Supercomputing - a Success Story
16th JHPCN Symposium, Tokyo [PDF]
Jul 2024: Extending the Continuum of Six-Colorings
Conference on Discrete Optimization and Machine Learning, Tokyo [PDF]
Jun 2024: Cargo-Kult Trifft Auf Große Sprachmodelle: Entmystifizierung von KI?
Lange Nacht Der Wissenschaften, Berlin
Mar 2024: The Cargo Cult Meets Large Language Models: Demystifying AI Sentiments?
Hong Kong Analytics Community Meeting, Hong Kong
Mar 2024: Alternating Linear Minimization: Revisiting von Neumann's Alternating Projections
CUHK-Shenzhen Seminar, Shenzhen [PDF]

View More / Less

Mar 2024: Conditional Gradients in Machine Learning
International Workshop on Image Processing and Machine Learning, Beijing [PDF]
Feb 2024: Conditional Gradients in Machine Learning
NUS Math Seminar, Singapore [PDF]
Jan 2024: The New AI World Order
Conti AI Vision
Nov 2023: Conditional Gradients in Machine Learning
Workshop on Geometry and Machine Learning, Leipzig [PDF]
Sep 2023: The Approximate Carathéodory Problem and an Application to Quantum Mechanics
7th Workshop on Future Algorithms and Applications, Berlin
Aug 2023: Alternating Linear Minimization: Revisiting von Neumann's Alternating Projections
ICIAM 2023 Minisymposium: Advances in Optimization I, Tokyo [PDF]
Aug 2023: Improved Local Models and New Bell Inequalities Via Frank-Wolfe Algorithms
5th DOxML Conference, Tokyo [PDF]
Jul 2023: Alternating Linear Minimization: Revisiting von Neumann's Alternating Projections
Jon-Shmuel Halfway to Twelfty [PDF]
Jun 2023: Möglichkeiten Und Grenzen Künstlicher Intelligenz
Lange Nacht Der Wissenschaften, Berlin
Mar 2023: Alternating Linear Minimization: Revisiting von Neumann's Alternating Projections
ICERM Workshop: Combinatorics and Optimization, Providence [PDF]
Mar 2023: Conditional Gradients -- an Overview
2nd Vienna Workshop on Computational Optimization, Vienna [PDF]
Mar 2023: Alternating Linear Minimization: Revisiting von Neumann's Alternating Projections
Optimization and ML Workshop, Waischenfeld [PDF]
Dec 2022: Alternating Linear Minimization: Revisiting von Neumann's Alternating Projections
Fields Workshop on Recent Advances in Optimization, Toronto [PDF]
Sep 2022: Convex Integer Optimization with Frank-Wolfe Methods
Advances in Classical and Quantum Algorithms for Optimization and Machine Learning, Tokyo [PDF]
Jul 2022: Structured ML Training Via Conditional Gradients
Workshop on Algorithmic Optimization and Data Science, Trier [PDF]
Nov 2021: Discrete Optimization in Machine Learning - an (informal) Overview
Oberwolfach Workshop on Combinatorial Optimization, Oberwolfach [PDF]
Oct 2021: Fast Algorithms for 1-fair Packing (and Its Dual)
HIM Workshop: Continuous Approaches to Discrete Optimization, Bonn [PDF]
Apr 2021: Learning From Little Data
Google University Visit @ TU Berlin [PDF]
Feb 2021: Structured ML Training Via Conditional Gradients
IPAM Deep Learning and Combinatorial Optimization Workshop [PDF]
Nov 2020: AI And-for-with-against Humanity?
Human-centric Artificial Intelligence: 2nd French-German-Japanese Symposium [PDF]
Sep 2020: Restarting Algorithms: Sometimes There Is Free Lunch
CPAIOR 2020 [PDF]
Sep 2020: Robust ML Training with Conditional Gradients
4th CO@Work, Berlin [PDF]
May 2020: Beyond Worst-case Rates: Data-dependent Rates in Learning and Optimization
MIP Workshop [PDF]
Mar 2020: Künstliche Intelligence Im Arbeitsalltag
Künstliche Intelligenz: Verständlich @ TH Wildau, Wildau [PDF]
Sep 2019: Smooth Constraint Convex Minimization Via Conditional Gradients
19th French-German-Swiss Conference on Optimization, Nice [PDF]
Sep 2019: Mirror Descent and Related Methods in Linear and Discrete Optimization
Cargese Workshop on Combinatorial Optimization, Cargese [PDF]
Jan 2019: Smooth Constraint Convex Minimization Via Conditional Gradients
INFORMS Computing Society Conference, Knoxville [PDF]
Nov 2018: When the AI God Fails: Recent Adventures in AI Hacking, Security, and Fairness
Hong Kong Analytics Community Meeting, Hong Kong [PDF]
Nov 2018: Blended Conditional Gradients
Oberwolfach Workshop on Combinatorial Optimization, Oberwolfach [PDF]
Apr 2018: Emulating the Expert: Inverse Optimization Through Online Learning
Optimization and Discrete Geometry Workshop, Tel Aviv [PDF]

Research seminar talks

May 2023: Alternating Linear Minimization: Revisiting von Neumann's Alternating Projections
University of Magdeburg MathCoRe Lecture, Magdeburg [PDF]
Apr 2023: Conditional Gradients in Machine Learning
Yale Statistics and Data Science Seminar Series, New Haven [PDF]
Mar 2023: Structured ML Training Via Conditional Gradients
Columbia IEOR Seminar Series, New York [PDF]
Apr 2022: Conditional Gradients in Machine Learning and Optimization
IST ELLIS Seminar Series, Klosterneuburg [PDF]
Sep 2021: Conditional Gradients - a Tour D'horizon
AI Campus Berlin Tech Lunch Talk [PDF]

View More / Less

May 2021: Conditional Gradients in Machine Learning and Optimization
Math+ Friday Colloquium
Nov 2020: Conditional Gradients: Overview and Recent Advances
RWTH Aachen Mathematical Colloquium [PDF]
Jul 2019: Locally Accelerated Conditional Gradients
Research Institute for Mathematical Sciences Seminar, Kyoto University, Kyoto [PDF]