Sebastian Pokutta
department head at ZIB since September 2019professor at TUB since September 2019
vice president at ZIB since September 2019
📬 Contact
office | Room 3026 at
ZIB Room MA 606 at TUB |
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pokutta (at) zib.de pokutta (at) math.tu-berlin.de |
|
homepage | pokutta.com |
🎓 Academic Background
2005 | Ph.D. in Mathematics at DUE |
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2003 | Diploma in Mathematics at DUE |
🔬 Research
Preprints
- Mundinger, K., Zimmer, M., and Pokutta, S. (2024). Neural Parameter Regression for Explicit Representations of PDE Solution Operators.
[arXiv]
[BibTeX]
- Roux, C., Zimmer, M., and Pokutta, S. (2024). On the Byzantine-resilience of Distillation-based Federated Learning.
[arXiv]
[BibTeX]
- Göß, A., Martin, A., Pokutta, S., and Sharma, K. (2024). Norm-induced Cuts: Optimization with Lipschitzian Black-box Functions.
[URL]
[arXiv]
[BibTeX]
- Mundinger, K., Pokutta, S., Spiegel, C., and Zimmer, M. (2024). Extending the Continuum of Six-Colorings.
[arXiv]
[BibTeX]
- MartĂnez-Rubio, D., Roux, C., and Pokutta, S. (2024). Convergence and Trade-offs in Riemannian Gradient Descent and Riemannian Proximal Point.
[arXiv]
[BibTeX]
- 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]
- 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]
- Sadiku, S., Wagner, M., and Pokutta, S. (2023). Group-wise Sparse and Explainable Adversarial Attacks.
[arXiv]
[BibTeX]
- Deza, A., Onn, S., Pokutta, S., and Pournin, L. (2023). Kissing Polytopes.
[arXiv]
[BibTeX]
- Kiem, A., Pokutta, S., and Spiegel, C. (2023). The 4-color Ramsey Multiplicity of Triangles.
[arXiv]
[code]
[BibTeX]
- 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]
- Scieur, D., Kerdreux, T., MartĂnez-Rubio, D., d’Aspremont, A., and Pokutta, S. (2023). Strong Convexity of Sets in Riemannian Manifolds.
[arXiv]
[BibTeX]
- Woodstock, Z., and Pokutta, S. (2023). Splitting the Conditional Gradient Algorithm.
[arXiv]
[BibTeX]
- Zimmer, M., Andoni, M., Spiegel, C., and Pokutta, S. (2023). PERP: Rethinking the Prune-Retrain Paradigm in the Era of LLMs.
[arXiv]
[code]
[BibTeX]
- Braun, G., Pokutta, S., and Weismantel, R. (2022). Alternating Linear Minimization: Revisiting von Neumann’s Alternating Projections.
[arXiv]
[slides]
[video]
[BibTeX]
- Braun, G., Carderera, A., Combettes, C., Hassani, H., Karbasi, A., Mokhtari, A., and Pokutta, S. (2022). Conditional Gradient Methods.
[arXiv]
[BibTeX]
- GelĂź, P., Klus, S., Shakibaei, Z., and Pokutta, S. (2022). Low-rank Tensor Decompositions of Quantum Circuits.
[arXiv]
[BibTeX]
- Hendrych, D., Troppens, H., Besançon, M., and Pokutta, S. (2022). Convex Integer Optimization with Frank-Wolfe Methods.
[arXiv]
[slides]
[code]
[BibTeX]
- Zimmer, M., Spiegel, C., and Pokutta, S. (2022). Compression-aware Training of Neural Networks Using Frank-Wolfe.
[arXiv]
[BibTeX]
- Kerdreux, T., d’Aspremont, A., and Pokutta, S. (2021). Local and Global Uniform Convexity Conditions. Preprint.
[arXiv]
[BibTeX]
- 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]
- Braun, G., and Pokutta, S. (2021). Dual Prices for Frank–Wolfe Algorithms.
[arXiv]
[BibTeX]
- Roux, C., Wirth, E., Pokutta, S., and Kerdreux, T. (2021). Efficient Online-bandit Strategies for Minimax Learning Problems.
[arXiv]
[BibTeX]
- Combettes, C., Spiegel, C., and Pokutta, S. (2020). Projection-free Adaptive Gradients for Large-scale Optimization.
[arXiv]
[summary]
[code]
[BibTeX]
- Pokutta, S., Spiegel, C., and Zimmer, M. (2020). Deep Neural Network Training with Frank-Wolfe.
[arXiv]
[summary]
[code]
[BibTeX]
- Carderera, A., and Pokutta, S. (2020). Second-order Conditional Gradient Sliding. Preprint.
[arXiv]
[summary]
[code]
[BibTeX]
- Bärmann, A., Martin, A., Pokutta, S., and Schneider, O. (2018). An Online-Learning Approach to Inverse Optimization. Submitted.
[arXiv]
[summary]
[slides]
[BibTeX]
- Braun, G., and Pokutta, S. (2016). An Efficient High-probability Algorithm for Linear Bandits.
[arXiv]
[BibTeX]
- Braun, G., and Pokutta, S. (2015). An Information Diffusion Fano Inequality.
[arXiv]
[BibTeX]
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]
- 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.
[arXiv]
[BibTeX]
- Wäldchen, S., Sharma, K., Zimmer, M., Turan, B., and Pokutta, S. (2024). Merlin-Arthur Classifiers: Formal Interpretability with Interactive Black Boxes. Proceedings of International Conference on Artificial Intelligence and Statistics.
[arXiv]
[BibTeX]
- 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.
[arXiv]
[BibTeX]
- 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]
- 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]
- MartĂnez-Rubio, D., and Pokutta, S. (2023). Accelerated Riemannian Optimization: Handling Constraints with a Prox to Bound Geometric Penalties. Proceedings of Annual Workshop on Computational Learning Theory.
[arXiv]
[BibTeX]
- MartĂnez-Rubio, D., Roux, C., Criscitiello, C., and Pokutta, S. (2023). Accelerated Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties. Proceedings of Optimization for Machine Learning (NeurIPS Workshop OPT 2023).
[arXiv]
[BibTeX]
- MartĂnez-Rubio, D., Wirth, E., and Pokutta, S. (2023). Accelerated and Sparse Algorithms for Approximate Personalized PageRank and Beyond. Proceedings of Annual Workshop on Computational Learning Theory.
[arXiv]
[BibTeX]
- Parczyk, O., Pokutta, S., Spiegel, C., and SzabĂł, T. (2023). Fully Computer-assisted Proofs in Extremal Combinatorics. Proceedings of AAAI Conference on Artificial Intelligence.
[arXiv]
[slides]
[code]
[BibTeX]
- 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]
- Wirth, E., Kera, H., and Pokutta, S. (2023). Approximate Vanishing Ideal Computations at Scale. Proceedings of International Conference on Learning Representations.
[arXiv]
[slides]
[BibTeX]
- Zimmer, M., Spiegel, C., and Pokutta, S. (2023). How I Learned to Stop Worrying and Love Retraining. Proceedings of International Conference on Learning Representations.
[arXiv]
[code]
[BibTeX]
- 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]
- Criado, F., MartĂnez-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]
- 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]
- MartĂnez-Rubio, D., and Pokutta, S. (2022). Accelerated Riemannian Optimization: Handling Constraints with a Prox to Bound Geometric Penalties. Proceedings of Optimization for Machine Learning (NeurIPS Workshop OPT 2022).
[URL]
[arXiv]
[poster]
[BibTeX]
- Parczyk, O., Pokutta, S., Spiegel, C., and SzabĂł, T. (2022). New Ramsey Multiplicity Bounds and Search Heuristics. Proceedings of Discrete Mathematics Days.
[arXiv]
[code]
[BibTeX]
- 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]
- 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]
- 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]
- 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]
- 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]
- Carderera, A., Diakonikolas, J., Lin, C. Y., and Pokutta, S. (2021). Parameter-free Locally Accelerated Conditional Gradients. Proceedings of ICML.
[arXiv]
[slides]
[BibTeX]
- 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]
- 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]
- Combettes, C. W., and Pokutta, S. (2020). Boosting Frank-Wolfe by Chasing Gradients. Proceedings of ICML.
[PDF]
[arXiv]
[summary]
[slides]
[code]
[video]
[BibTeX]
- 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]
- Diakonikolas, J., Carderera, A., and Pokutta, S. (2020). Locally Accelerated Conditional Gradients. Proceedings of AISTATS.
[PDF]
[arXiv]
[summary]
[slides]
[code]
[video]
[BibTeX]
- 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]
- 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]
- Combettes, C. W., and Pokutta, S. (2019). Blended Matching Pursuit. Proceedings of NeurIPS.
[PDF]
[arXiv]
[summary]
[slides]
[poster]
[code]
[BibTeX]
- 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]
- Roy, A., Xu, H., and Pokutta, S. (2017). Reinforcement Learning under Model Mismatch. Proceedings of NIPS.
[arXiv]
[BibTeX]
- 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]
- 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]
- 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]
- Roy, A., and Pokutta, S. (2016). Hierarchical Clustering via Spreading Metrics. Proceedings of NIPS.
[URL]
[arXiv]
[BibTeX]
- 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]
- 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]
- 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]
- 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]
- Braun, G., and Pokutta, S. (2013). Common Information and Unique Disjointness. Proceedings of Annual Symposium on Foundations of Computer Science, 688–697.
[URL]
[BibTeX]
- 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]
- 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]
- 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]
- 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]
- 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]
Full articles
- 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]
- Braun, G., Guzmán, C., and Pokutta, S. (2024). Corrigendum in “Lower Bounds on the Oracle Complexity of Nonsmooth Convex Optimization Via Information Theory.” IEEE Transactions on Information Theory.
DOI: 10.1109/TIT.2024.3357200
[BibTeX]
- Carderera, A., Besançon, M., and Pokutta, S. (2024). Scalable Frank-Wolfe on Generalized Self-concordant Functions Via Simple Steps. SIAM Journal on Optimization.
[summary]
[slides]
[poster]
[code]
[BibTeX]
- 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]
- Designolle, S., VĂ©rtesi, T., and Pokutta, S. (2024). Symmetric Multipartite Bell Inequalities Via Frank-Wolfe Algorithms. Physics Review A.
[arXiv]
[BibTeX]
- 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]
- 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]
- 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]
- Bienstock, D., Muñoz, G., and Pokutta, S. (2023). Principled Deep Neural Network Training Through Linear Programming. Discrete Optimization.
[URL]
[arXiv]
[summary]
[BibTeX]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- Combettes, C. W., and Pokutta, S. (2021). Complexity of Linear Minimization and Projection on Some Sets. Operations Research Letters, 49(4).
[arXiv]
[code]
[BibTeX]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- Roy, A., and Pokutta, S. (2017). Hierarchical Clustering via Spreading Metrics. Journal of Machine Learning Research (JMLR), 18, 1–35.
[URL]
[arXiv]
[BibTeX]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
đź’¬ Talks and posters
Conference and workshop talks
- Nov 2023
- Workshop on Geometry and Machine Learning, Leipzig [PDF]
- Sep 2023
- 7th Workshop on Future Algorithms and Applications, Berlin
- Aug 2023
- ICIAM 2023 Minisymposium: Advances in Optimization I, Tokyo [PDF]
- Aug 2023
- 5th DOxML Conference, Tokyo [PDF]
- Jul 2023
- Jon-Shmuel Halfway to Twelfty [PDF]
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- Jun 2023
- Lange Nacht Der Wissenschaften, Berlin
- Mar 2023
- ICERM Workshop: Combinatorics and Optimization, Providence [PDF]
- Mar 2023
- 2nd Vienna Workshop on Computational Optimization, Vienna [PDF]
- Mar 2023
- Optimization and ML Workshop, Waischenfeld [PDF]
- Dec 2022
- Fields Workshop on Recent Advances in Optimization, Toronto [PDF]
- Sep 2022
- Advances in Classical and Quantum Algorithms for Optimiza..., Tokyo [PDF]
- Jul 2022
- Workshop on Algorithmic Optimization and Data Science, Trier [PDF]
- Nov 2021
- Oberwolfach Workshop on Combinatorial Optimization, Oberwolfach [PDF]
- Oct 2021
- HIM Workshop: Continuous Approaches to Discrete Optimization, Bonn [PDF]
- Apr 2021
- Google University Visit @ TU Berlin [PDF]
- Feb 2021
- IPAM Deep Learning and Combinatorial Optimization Workshop [PDF]
- Nov 2020
- Human-centric Artificial Intelligence: 2nd French-German-... [PDF]
- Sep 2020
- CPAIOR 2020 [PDF]
- Sep 2020
- CO@Work 2020 Summer School [PDF]
- May 2020
- MIP Workshop [PDF]
- Mar 2020
- Künstliche Intelligenz: Verständlich @ TH Wildau, Wildau [PDF]
- Sep 2019
- 19th French-German-Swiss Conference on Optimization, Nice [PDF]
- Sep 2019
- Cargese Workshop on Combinatorial Optimization, Cargese [PDF]
- Jan 2019
- INFORMS Computing Society Conference, Knoxville [PDF]
- Nov 2018
- Hong Kong Analytics Community Meeting, Hong Kong [PDF]
- Nov 2018
- Oberwolfach Workshop on Combinatorial Optimization, Oberwolfach [PDF]
- Apr 2018
- Optimization and Discrete Geometry Workshop, Tel Aviv [PDF]