Sadiku, S., Wagner, M., Nagarajan, S. G., and Pokutta, S. (2024). S-CFE: Simple Counterfactual Explanations. Preprint.[arXiv]
Głuch, G., Turan, B., Nagarajan, S. G., and Pokutta, S. (2024). The Good, the Bad and the Ugly: Watermarks, Transferable Attacks and Adversarial Defenses. Preprint.[arXiv][poster]
Braun, G., Pokutta, S., and Woodstock, Z. (2024). Flexible block-iterative analysis for the Frank-Wolfe algorithm. Preprint.[arXiv]
Designolle, S., Vértesi, T., and Pokutta, S. (2024). Better bounds on Grothendieck constants of finite orders. Preprint.[arXiv]
Wirth, E., Besançon, M., and Pokutta, S. (2024). The Pivoting Framework: Frank-Wolfe Algorithms with Active Set Size Control. Preprint.[arXiv]
Wirth, E., Peña, J., and Pokutta, S. (2024). Fast Convergence of Frank-Wolfe algorithms on polytopes. Preprint.[arXiv]
Göß, A., Martin, A., Pokutta, S., and Sharma, K. (2024). Norm-induced Cuts: Optimization with Lipschitzian Black-box Functions. Preprint.[arXiv]
Roux, C., Zimmer, M., and Pokutta, S. (2024). On the Byzantine-Resilience of Distillation-Based Federated Learning. Preprint.[arXiv][code]
Sharma, K., Hendrych, D., Besançon, M., and Pokutta, S. (2024). Network Design for the Traffic Assignment Problem with Mixed-Integer Frank-Wolfe. Preprint.[arXiv][code]
Zimmer, M., Andoni, M., Spiegel, C., and Pokutta, S. (2023). PERP: Rethinking the Prune-Retrain Paradigm in the Era of LLMs. Preprint.[arXiv][code]
Kiem, A., Pokutta, S., and Spiegel, C. (2023). The Four-Color Ramsey Multiplicity of Triangles. Preprint.[arXiv][code]
Sadiku, S., Wagner, M., and Pokutta, S. (2023). Group-wise Sparse and Explainable Adversarial Attacks. Preprint.[arXiv][poster]
Woodstock, Z., and Pokutta, S. (2023). Splitting the Conditional Gradient Algorithm. Preprint.[arXiv]
Wirth, E., Peña, J., and Pokutta, S. (2023). Accelerated Affine-Invariant Convergence Rates of the Frank-Wolfe Algorithm with Open-Loop Step-Sizes. Preprint.[arXiv]
Braun, G., Pokutta, S., and Weismantel, R. (2022). Alternating Linear Minimization: Revisiting von Neumann’s alternating projections. Preprint.[arXiv][slides][video]
Braun, G., Carderera, A., Combettes, C. W., Hassani, H., Karbasi, A., Mokthari, A., and Pokutta, S. (2022). Conditional Gradient Methods. Preprint.[arXiv]
Hendrych, D., Troppens, H., Besançon, M., and Pokutta, S. (2022). Convex integer optimization with Frank-Wolfe methods. Preprint.[arXiv][slides][poster][code]
Zimmer, M., Spiegel, C., and Pokutta, S. (2022). Compression-aware Training of Neural Networks using Frank-Wolfe. Preprint.[arXiv]
Gelß, P., Klus, S., Shakibaei, Z., and Pokutta, S. (2022). Low-rank tensor decompositions of quantum circuits. Preprint.[arXiv]
Kerdreux, T., Scieur, D., Martinez-Rubio, D., d’Aspremont, A., and Pokutta, S. (2022). Strong Convexity of Sets in Riemannian Manifolds. Preprint.
Roux, C., Wirth, E., Pokutta, S., and Kerdreux, T. (2021). Efficient Online-Bandit Strategies for Minimax Learning Problems. Preprint.[arXiv]
Kerdreux, T., d’Aspremont, A., and Pokutta, S. (2021). Local and Global Uniform Convexity Conditions. Preprint.[arXiv]
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]
Pokutta, S., Spiegel, C., and Zimmer, M. (2020). Deep Neural Network Training with Frank-Wolfe. Preprint.[arXiv][summary][code]
Combettes, C. W., Spiegel, C., and Pokutta, S. (2020). Projection-Free Adaptive Gradients for Large-Scale Optimization. Preprint.[arXiv][summary][code]
Carderera, A., and Pokutta, S. (2020). Second-order Conditional Gradient Sliding. Preprint.[arXiv][summary][code]
Bärmann, A., Martin, A., Pokutta, S., and Schneider, O. (2018). An Online-Learning Approach to Inverse Optimization. Submitted.[arXiv][summary][slides]
Refereed Conference Proceedings
Kiem, A., Pokutta, S., and Spiegel, C. (2024). Categorification of Flag Algebras. Proceedings of Discrete Mathematics Days.[arXiv]
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. To Appear in Proceedings of ICML.[arXiv][code]
Kiem, A., Pokutta, S., and Spiegel, C. (2024). The Four-Color Ramsey Multiplicity of Triangles. Proceedings of Discrete Mathematics Days.[PDF][arXiv][code]
Mundinger, K., Pokutta, S., Spiegel, C., and Zimmer, M. (2024). Extending the Continuum of Six-Colorings. Proceedings of Discrete Mathematics Days.[arXiv][summary][slides]
Martinez-Rubio, D., Roux, C., and Pokutta, S. (2024). Convergence and Trade-Offs in Riemannian Gradient Descent and Riemannian Proximal Point. To Appear in Proceedings of ICML.[arXiv]
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 (LIPIcs).[PDF][arXiv][code]
Mundinger, K., Zimmer, M., and Pokutta, S. (2024). Neural Parameter Regression for Explicit Representations of PDE Solution Operators. ICLR 2024 Workshop on AI4DifferentialEquations In Science.[PDF][arXiv][slides][poster]
Wäldchen, S., Sharma, K., Turan, B., Zimmer, M., and Pokutta, S. (2024). Interpretability Guarantees with Merlin-Arthur Classifiers. To Appear in Proceedings of AISTATS.[arXiv]
Zimmer, M., Spiegel, C., and Pokutta, S. (2024). Sparse Model Soups: A Recipe for Improved Pruning via Model Averaging. To Appear in Proceedings of ICLR.[arXiv]
Thuerck, D., Sofranac, B., Pfetsch, M., and Pokutta, S. (2023). Learning Cuts via Enumeration Oracles. Proceedings of NeurIPS.[arXiv]
Martinez-Rubio, D., Roux, C., Criscitiello, C., and Pokutta, S. (2023). Accelerated Methods for Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties. NeurIPS OPT 2023 Workshop.[arXiv]
Martinez-Rubio, D., Wirth, E., and Pokutta, S. (2023). Accelerated and Sparse Algorithms for Approximate Personalized PageRank and Beyond. Proceedings of COLT.[arXiv][slides][poster]
Martinez-Rubio, D., and Pokutta, S. (2023). Accelerated Riemannian Optimization: Handling Constraints to Bound Geometric Penalties. Proceedings of COLT.[arXiv][poster]
Zimmer, M., Spiegel, C., and Pokutta, S. (2023). How I Learned to Stop Worrying and Love Retraining. Proceedings of ICLR.[arXiv][poster][code]
Chmiela, A., Gleixner, A., Lichocki, P., and Pokutta, S. (2023). Online Learning for Scheduling MIP Heuristics. Proceedings of CPAIOR.[arXiv]
Wirth, E., Kera, H., and Pokutta, S. (2023). Approximate Vanishing Ideal Computations at Scale. Proceedings of ICLR.[arXiv][slides][poster]
Wirth, E., Kerdreux, T., and Pokutta, S. (2023). Acceleration of Frank-Wolfe algorithms with open loop step-sizes. Proceedings of AISTATS.[arXiv][poster]
Parczyk, O., Pokutta, S., Spiegel, C., and Szabó, T. (2022). Fully Computer-Assisted Proofs in Extremal Combinatorics. Proceedings of AAAI.[arXiv][slides]
Martinez-Rubio, D., and Pokutta, S. (2022). Accelerated Riemannian Optimization: Handling Constraints to Bound Geometric Penalties. NeurIPS OPT 2022 Workshop.[arXiv][poster]
Criado, F., Martinez-Rubio, D., and Pokutta, S. (2022). Fast Algorithms for Packing Proportional Fairness and its Dual. Proceedings of NeurIPS.[PDF][arXiv][poster][video]
Parczyk, O., Pokutta, S., Spiegel, C., and Szabó, T. (2022). New Ramsey Multiplicity Bounds and Search Heuristics. Proceedings of Discrete Mathematics Days.[arXiv][slides][code]
Wäldchen, S., Huber, F., and Pokutta, S. (2022). Training Characteristic Functions with Reinforcement Learning: XAI-methods play Connect Four. Proceedings of ICML.[arXiv][poster][video]
Tsuji, K., Tanaka, K., and Pokutta, S. (2022). Pairwise Conditional Gradients without Swap Steps and Sparser Kernel Herding. Proceedings of ICML.[arXiv][summary][slides][code][video]
Macdonald, J., Besançon, M., and Pokutta, S. (2022). Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings. Proceedings of ICML.[arXiv][poster][video]
Gasse, M., 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., Morris, C., Papageorgiou, D. J., Parjadis, A., Pokutta, S., Prouvost, A., … Kun, M. (2022). The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights. Proceedings of Machine Learning Research, 176, 220–231.[arXiv]
Wirth, E., and Pokutta, S. (2022). Conditional Gradients for the Approximate Vanishing Ideal. Proceedings of AISTATS.[arXiv][summary][poster][code]
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.[arXiv][video]
Carderera, A., Besançon, M., and Pokutta, S. (2021). Simple steps are all you need: Frank-Wolfe and generalized self-concordant functions. Proceedings of NeurIPS.[arXiv][summary][slides][poster][code]
Chmiela, A., Khalil, E., Gleixner, A., Lodi, A., and Pokutta, S. (2021). Learning to Schedule Heuristics in Branch-and-Bound. Proceedings of NeurIPS.[arXiv][summary][poster]
Carderera, A., Diakonikolas, J., Lin, C. Y., and Pokutta, S. (2021). Parameter-free Locally Accelerated Conditional Gradients. Proceedings of ICML.[arXiv][slides]
Kerdreux, T., d’Aspremont, A., and Pokutta, S. (2021). Projection-Free Optimization on Uniformly Convex Sets. Proceedings of AISTATS.[arXiv][summary][slides]
Pokutta, S. (2020). Restarting Algorithms: Sometimes there is Free Lunch. Proceedings of CPAIOR.[arXiv][slides][video]
Sofranac, B., Gleixner, A., and Pokutta, S. (2020). Accelerating Domain Propagation: an Efficient GPU-Parallel Algorithm over Sparse Matrices. Proceedings of IA3 at SC20.[arXiv][summary][slides][video]
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]
Pfetsch, M., and Pokutta, S. (2020). IPBoost – Non-Convex Boosting via Integer Programming. Proceedings of ICML.[arXiv][summary][slides][code]
Pokutta, S., Singh, M., and Torrico, A. (2020). On the Unreasonable Effectiveness of the Greedy Algorithm: Greedy Adapts to Sharpness. Proceedings of ICML.[arXiv][summary][slides][poster][video]
Kerdreux, T., d’Aspremont, A., and Pokutta, S. (2019). Restarting Frank-Wolfe. Proceedings of AISTATS.[PDF][arXiv][summary][slides]
Pokutta, S., Singh, M., and Torrico, A. (2019). On the Unreasonable Effectiveness of the Greedy Algorithm: Greedy Adapts to Sharpness. OPTML Workshop Paper.[PDF][summary][poster]
Braun, G., Pokutta, S., Tu, D., and Wright, S. (2019). Blended Conditional Gradients: the unconditioning of conditional gradients. Proceedings of ICML.[PDF][arXiv][summary][slides][poster][code]
Anari, N., Haghtalab, N., Naor, S., Pokutta, S., Singh, M., and Torrico, A. (2019). Structured Robust Submodular Maximization: Offline and Online Algorithms. Proceedings of AISTATS.[PDF][arXiv]
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]
Pokutta, S., Singh, M., and Torrico, A. (2018). Efficient algorithms for robust submodular maximization under matroid constraints. ICML Workshop Paper.[PDF][arXiv]
Inanlouganji, A., Pedrielli, G., Fainekos, G., and Pokutta, S. (2018). Continuous Simulation Optimization with Model Mismatch Using Gaussian Process Regression. Proceedings of the 2018 Winter Simulation Conference.
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]
Arumugam, K., Kadampot, I., Tahmasbi, M., Shah, S., Bloch, M., and Pokutta, S. (2017). Modulation Recognition Using Side Information and Hybrid Learning. Proceedings of IEEE DySPAN.
Roy, A., Xu, H., and Pokutta, S. (2017). Reinforcement Learning under Model Mismatch. Proceedings of NIPS.[arXiv]
Braun, G., Pokutta, S., and Zink, D. (2017). Lazifying Conditional Gradient Algorithms. Proceedings of the International Conference on Machine Learning (ICML).[PDF][arXiv][slides][poster]
Lan, G., Pokutta, S., Zhou, Y., and Zink, D. (2017). Conditional Accelerated Lazy Stochastic Gradient Descent. Proceedings of the International Conference on Machine Learning (ICML).[PDF][arXiv][poster]
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 SODA 2016.[PDF][arXiv]
Roy, A., and Pokutta, S. (2016). Hierarchical Clustering via Spreading Metrics. Proceedings of NIPS.[PDF][arXiv]
Braun, G., Roy, A., and Pokutta, S. (2016). Stronger Reductions for Extended Formulations. Proceedings of IPCO.[arXiv]
Xie, Y., Li, Q., and Pokutta, S. (2015). Supervised Online Subspace Tracking. Proceedings of Asilomar Conference on Signals, Systems, and Computers.
Pokutta, S. (2015). Information Theory and Polyhedral Combinatorics. Proceedings of 53rd Annual Allerton Conference on Communication, Control, and Computing.[PDF]
Braun, G., Pokutta, S., and Zink, D. (2015). Inapproximability of combinatorial problems via small LPs and SDPs. Proceeedings of STOC.[arXiv][video]
Braun, G., and Pokutta, S. (2015). The matching polytope does not admit fully-polynomial size relaxation schemes. Proceeedings of SODA.[arXiv]
Bazzi, A., Fiorini, S., Pokutta, S., and Svensson, O. (2015). Small linear programs cannot approximate Vertex Cover within a factor of 2 - epsilon. Proceedings of FOCS.[arXiv][slides]
Song, R., Xie, Y., and Pokutta, S. (2015). Sequential Sensing with Model Mismatch. Proceedings of ISIT.
Braun, G., Pokutta, S., and Xie, Y. (2014). Info-Greedy Sequential Adaptive Compressed Sensing. Proceedings of 52nd Annual Allerton Conference on Communication, Control, and Computing.[arXiv]
Braun, G., Fiorini, S., and Pokutta, S. (2014). Average case polyhedral complexity of the maximum stable set problem. Proceedings of RANDOM.[PDF][arXiv]
Briët, J., Dadush, D., and Pokutta, S. (2013). On the existence of 0/1 polytopes with high semidefinite extension complexity. Proceedings of ESA.[arXiv]
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.[arXiv]
Braun, G., and Pokutta, S. (2013). Common information and unique disjointness. Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium, 688–697.[arXiv]
Fiorini, S., Massar, S., Pokutta, S., Tiwary, H. R., and de Wolf, R. (2012). Linear vs. Semidefinite Extended Formulations: Exponential Separation and Strong Lower Bounds. Proceedings of STOC.[arXiv]
Braun, G., Fiorini, S., Pokutta, S., and Steurer, D. (2012). Approximation Limits of Linear Programs (Beyond Hierarchies). Proceedings of FOCS.[arXiv]
Braun, G., and Pokutta, S. (2012). An algebraic view on symmetric extended formulations. Proceedings of ISCO, Lecture Notes in Computer Science, 7422(141–152).[arXiv]
Dey, S. S., and Pokutta, S. (2011). Design and verify: a new scheme for generating cutting-planes. Proceedings of IPCO, Lecture Notes in Computer Science, 6655, 143–155.[arXiv]
Pokutta, S., and Schmaltz, C. (2011). Optimal Planning under Basel III Regulations. Proceedings of 24th Australasian Finance and Banking Conference.[arXiv]
Pokutta, S., and Schmaltz, C. (2011). A network model for bank lending capacity. Proceedings of Systemic Risk, Basel III, Financial Stability and Regulation.[arXiv]
Helmke, H., Gluchshenko, O., Martin, A., Peter, A., Pokutta, S., and Siebert, U. (2011). Optimal Mixed-Mode Runway Scheduling. Proceedings of DACS.[arXiv]
Braun, G., and Pokutta, S. (2010). Rank of random half-integral polytopes. Electronic Notes in Discrete Mathematics, 36, 415–422.[PDF][arXiv]
Pokutta, S., and Schulz, A. S. (2010). On the rank of generic cutting-plane proof systems. Proceedings of IPCO, Lecture Notes in Computer Science, 6080, 450–463.[PDF][arXiv]
Drewes, S., and Pokutta, S. (2010). Cutting-planes for weakly-coupled 0/1 second order cone programs. Electronic Notes in Discrete Mathematics, 36, 735–742.[PDF][arXiv]
Drewes, S., and Pokutta, S. (2010). Geometric mean maximization in the presence of discrete decisions. Proceedings of 23rd Australasian Finance and Banking Conference.
Martin, A., Müller, J., and Pokutta, S. (2010). On clearing coupled day-ahead electricity markets. Proceedings of 23rd Australasian Finance and Banking Conference.[arXiv]
Pokutta, S., and Schmaltz, C. (2009). Optimal degree of centralization of liquidity management. Proceedings of 22nd Australasian Finance and Banking Conference.[arXiv]
Refereed Journals
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. (2024). Quantum Optimization: Potential, Challenges, and the Path Forward. To Appear in Nature Reviews Physics.[arXiv]
Stengl, M., Gelß, P., Klus, S., and Pokutta, S. (2024). Existence and Uniqueness of Solutions of the Koopman–von Neumann Equation on Bounded Domains. To Appear in Journal of Physics A.[arXiv]
Deza, A., Onn, S., Pokutta, S., and Pournin, L. (2024). Kissing polytopes. To Appear in SIAM Journal on Discrete Mathematics.[PDF][arXiv]
Parczyk, O., Pokutta, S., Spiegel, C., and Szabó, T. (2024). New Ramsey Multiplicity Bounds and Search Heuristics. Foundations of Computational Mathematics.[PDF][arXiv][slides][code]
Mundinger, K., Pokutta, S., Spiegel, C., and Zimmer, M. (2024). Extending the Continuum of Six-Colorings. Geombinatorics Quarterly.[arXiv][summary][slides]
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).[PDF][arXiv][summary][slides][poster][code]
Designolle, S., Vértesi, T., and Pokutta, S. (2024). Symmetric multipartite Bell inequalities via Frank-Wolfe algorithms. Physical Review A, 109(2).[PDF][arXiv]
Pokutta, S. (2024). The Frank-Wolfe algorithm: a short introduction. Jahresbericht Der Deutschen Mathematiker-Vereinigung, 126, 3–35.[PDF][arXiv]
Kreimeier, T., Pokutta, S., Walther, A., and Woodstock, Z. (2023). On a Frank-Wolfe Approach for Abs-smooth Functions. To Appear in Optimization Methods and Software.[arXiv][poster]
Deza, A., Pokutta, S., and Pournin, L. (2023). The complexity of geometric scaling. To Appear in Operations Research Letters.[PDF][arXiv]
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 Reviews Research.[PDF][arXiv][slides]
Aigner, K., Bärmann, A., Braun, K., Liers, F., Pokutta, S., Schneider, O., Sharma, K., and Tschuppik, S. (2023). Data-driven Distributionally Robust Optimization over Time. To Appear in INFORMS Journal on Optimization.[arXiv]
Bienstock, D., Muñoz, G., and Pokutta, S. (2023). Principled Deep Neural Network Training through Linear Programming. To Appear in Discrete Optimization.[PDF][arXiv][summary]
Combettes, C. W., and Pokutta, S. (2023). Revisiting the Approximate Carathéodory Problem via the Frank-Wolfe Algorithm. Mathematical Programming A, 197, 191—214.[PDF][arXiv][summary][slides][code][video]
Sofranac, B., Gleixner, A., and Pokutta, S. (2022). Accelerating Domain Propagation: an Efficient GPU-Parallel Algorithm over Sparse Matrices. Parallel Computing, 109.[PDF][arXiv][summary][slides][video]
Sofranac, B., Gleixner, A., and Pokutta, S. (2022). An Algorithm-Independent Measure of Progress for Linear Constraint Propagation. Constraints, 27, 432–455.[PDF][arXiv][video]
Hunkenschröder, C., Pokutta, S., and Weismantel, R. (2022). Optimizing a low-dimensional convex function over a high-dimensional cube. To Appear in SIAM Journal on Optimization.[arXiv]
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.[PDF]
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.[PDF][arXiv][summary][slides][code]
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]
Kerdreux, T., d’Aspremont, A., and Pokutta, S. (2022). Restarting Frank-Wolfe: Faster Rates under Hölderian Error Bounds. Journal of Optimization Theory and Applications, 192, 799–829.[PDF][arXiv][summary][slides]
Combettes, C. W., and Pokutta, S. (2021). Complexity of Linear Minimization and Projection on Some Sets. Operations Research Letters, 49(4).[arXiv][code]
Kerdreux, T., Roux, C., d’Aspremont, A., and Pokutta, S. (2021). Linear Bandits on Uniformly Convex Sets. Journal of Machine Learning Research (JMLR), 22(284), 1–23.[PDF][arXiv][summary]
Anari, N., Haghtalab, N., Naor, S., Pokutta, S., Singh, M., and Torrico, A. (2021). Structured Robust Submodular Maximization: Offline and Online Algorithms. INFORMS Journal on Computing, 33(4), 1259–1684.[PDF][arXiv]
Gatzert, N., Pokutta, S., and Vogl, N. (2019). Convergence of Capital and Insurance Markets: Pricing Aspects of Index-Linked Catastrophic Loss Instruments. Journal of Risk and Insurance, 86, 39–72.[arXiv]
Braun, G., Pokutta, S., and Zink, D. (2019). Affine Reductions for LPs and SDPs. Mathematical Programming A, 173(1), 281–312.[PDF][arXiv]
Bazzi, A., Fiorini, S., Pokutta, S., and Svensson, O. (2019). Small linear programs cannot approximate Vertex Cover within a factor of 2 - epsilon. Mathematics of Operations Research, 44(1), 1–375.[arXiv][slides]
Braun, G., Pokutta, S., and Zink, D. (2019). Lazifying Conditional Gradient Algorithms. Journal of Machine Learning Research (JMLR), 20(71), 1–42.[PDF][arXiv][slides]
Knueven, B., Ostrowski, J., and Pokutta, S. (2018). Detecting Almost Symmetries in Graphs. Mathematical Programming C, 10, 143–185.[PDF][arXiv]
Le Bodic, P., Pfetsch, M., Pavelka, J., and Pokutta, S. (2018). Solving MIPs via Scaling-based Augmentation. Discrete Optimization, 27, 1–25.[PDF][arXiv]
Bodur, M., Del Pia, A., Dey, S. S., Molinaro, M., and Pokutta, S. (2018). Aggregation-based cutting-planes for packing and covering Integer Programs. Mathematical Programming A, 171, 331–359.[PDF][arXiv]
Braun, G., Roy, A., and Pokutta, S. (2018). Stronger Reductions for Extended Formulations. Mathematical Programming B, 172, 591–620.[arXiv]
Song, R., Xie, Y., and Pokutta, S. (2018). On the effect of model mismatch for sequential Info-Greedy Sensing. EURASIP Journal on Advances in Signal Processing.[PDF]
Christensen, H., Khan, A., Pokutta, S., and Tetali, P. (2017). Multidimensional Bin Packing and Other Related Problems: A survey. Computer Science Review, 24, 63–79.[PDF]
Roy, A., and Pokutta, S. (2017). Hierarchical Clustering via Spreading Metrics. Journal of Machine Learning Research (JMLR), 18, 1–35.[PDF][arXiv]
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 A, 165(2), 643–662.[PDF][arXiv]
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]
Braun, G., Guzmán, C., and Pokutta, S. (2017). Unifying Lower Bounds on the Oracle Complexity of Nonsmooth Convex Optimization. IEEE Transactions of Information Theory, 63(7), 4709–4724.[PDF][arXiv]
Braun, G., Jain, R., Lee, T., and Pokutta, S. (2017). Information-theoretic approximations of the nonnegative rank. Computational Complexity, 26(1), 147–197.[arXiv]
Braun, G., and Pokutta, S. (2016). A polyhedral characterization of Border Bases. SIAM Journal on Discrete Mathematics, 30(1), 239–265.[arXiv]
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]
Braun, G., Fiorini, S., and Pokutta, S. (2016). Average case polyhedral complexity of the maximum stable set problem. Mathematical Programming A, 160(1), 407–431.[PDF][arXiv]
Braun, G., and Pokutta, S. (2016). Common information and unique disjointness. Algorithmica, 76(3), 597–629.[PDF][arXiv]
Braun, G., and Pokutta, S. (2015). The matching polytope does not admit fully-polynomial size relaxation schemes. IEEE Transactions on Information Theory, 61(10), 1–11.[PDF][arXiv]
Braun, G., Fiorini, S., Pokutta, S., and Steurer, D. (2015). Approximation Limits of Linear Programs (Beyond Hierarchies). Mathematics of Operations Research, 40(3), 179–199.[arXiv]
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]
Fiorini, S., Massar, S., Pokutta, S., Tiwary, H. R., and de Wolf, R. (2015). Exponential Lower Bounds for Polytopes in Combinatorial Optimization. Journal of the ACM, 62(2), 1–17.[PDF][arXiv]
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.[arXiv]
Drewes, S., and Pokutta, S. (2014). Symmetry-exploiting cuts for a class of mixed-0/1 second order cone programs. Discrete Optimization, 13, 23–35.[arXiv]
Drewes, S., and Pokutta, S. (2014). Computing discrete expected utility maximizing portfolios. Journal of Investing, 23(4), 121–132.[arXiv]
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]
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, 307–310.[arXiv]
Martin, A., Müller, J., and Pokutta, S. (2014). Strict linear prices in non-convex European day-ahead electricity markets. Optimization Methods and Software, 29(1), 189–221.[PDF][arXiv]
Dey, S. S., and Pokutta, S. (2014). Design and verify: a new scheme for generating cutting-planes. Mathematical Programming A, 145, 199–222.[arXiv]
Kroll, C., and Pokutta, S. (2013). Just a perfect day: developing a happiness optimised day schedule. Journal of Economic Psychology, 34, 210–217.[PDF][video]
Pokutta, S., and Van Vyve, M. (2013). A note on the extension complexity of the knapsack polytope. Operations Research Letters, 41, 347–350.[PDF][arXiv]
Pokutta, S., and Schmaltz, C. (2012). Optimal Planning under Basel III Regulations. Cass-Capco Institute Paper Series on Risk, 34.[PDF][arXiv]
Braun, G., and Pokutta, S. (2012). Rigid abelian groups and the probabilistic method. Contemporary Mathematics, 576, 17–30.[PDF][arXiv]
Göbel, R., and Pokutta, S. (2012). Absolutely rigid fields and Shelah’s absolutely rigid trees. Contemporary Mathematics, 576, 105–128.[PDF][arXiv]
Braun, G., and Pokutta, S. (2011). Random half-integral polytopes. Operations Research Letters, 39(3), 204–207.[arXiv]
Haus, U. U., Hemmecke, R., and Pokutta, S. (2011). Reconstructing biochemical cluster networks. Journal of Mathematical Chemistry, 49(10), 2441–2456.[PDF][arXiv]
Letchford, A. N., Pokutta, S., and Schulz, A. S. (2011). On the membership problem for the 0,1/2-closure. Operations Research Letters, 39(5), 301–304.[PDF][arXiv]
Pokutta, S., and Schmaltz, C. (2011). Managing liquidity: Optimal degree of centralization. Journal of Banking and Finance, 35, 627–638.[PDF][arXiv]
Pokutta, S., and Schulz, A. S. (2011). Integer-empty polytopes in the 0/1-cube with maximal Gomory-Chvátal rank. Operations Research Letters, 39(6), 457–460.[PDF][arXiv]
Pokutta, S., and Stauffer, G. (2011). Lower bounds for the Chvátal-Gomory rank in the 0/1 cube. Operations Research Letters, 39(3), 200–203.[PDF][arXiv]
Pokutta, S., and Stauffer, G. (2009). France Telecom Workforce Scheduling Problem: a challenge. RAIRO-Operations Research, 43, 375–386.[PDF]
Heldt, D., Kreuzer, M., Pokutta, S., and Poulisse, H. (2009). Approximate Computation of zero-dimensional polynomial ideals. Journal of Symbolic Computation, 44, 1566–1591.[PDF]
Droste, M., Göbel, R., and Pokutta, S. (2008). Absolute graphs with prescribed endomorphism monoid. Semigroup Forum, 76, 256–267.[PDF]
Göbel, R., and Pokutta, S. (2008). Construction of dual modules using Martin’s axiom. Journal of Algebra, 320, 2388–2404.[PDF]
Pokutta, S., and Strüngmann, L. (2007). The Chase radical and reduced products. Journal of Pure and Applied Algebra, 211, 532–540.[PDF]
Unpublished Manuscripts
Braun, G., and Pokutta, S. (2021). Dual Prices for Frank-Wolfe Algorithms. Preprint.[arXiv]
Pokutta, S., and Xu, H. (2021). Adversaries in Online Learning Revisited: with applications in Robust Optimization and Adversarial training. Preprint.[arXiv]
Braun, G., and Pokutta, S. (2016). An efficient high-probability algorithm for Linear Bandits. Preprint.[arXiv]
Braun, G., and Pokutta, S. (2015). An information diffusion Fano inequality. Preprint.[arXiv]
Pokutta, S., and Schulz, A. S. (2013). On the rank of cutting-plane proof systems. Preprint.[arXiv]
Braun, G., and Pokutta, S. (2012). An algebraic view on symmetric extended formulations. Preprint.[arXiv]
Pokutta, S. (2011). Lower bounds for Chvátal-Gomory style operators. Preprint.[arXiv]
Pokutta, S., Schmaltz, C., and Stiller, S. (2011). Measuring Systemic Risk and Contagion in Financial Networks. Preprint.[arXiv]
Pokutta, S., and Schulz, A. S. (2009). On the connection of the Sherali-Adams closure and border bases. Preprint.[arXiv]
Pokutta, S. (2008). Stowage optimization for inland vessels. Preprint.
Other
Pokutta, S. (2021). Mathematik, Machine Learning und Artificial Intelligence. Mitteilungen Der DMV (German).[PDF]
Lee, D., and Pokutta, S. (2015). Toward a Science of Autonomy for Physical Systems: Transportation. Computing Community Consortium White Paper.[PDF]
Alf, M., and Pokutta, S. (2006). How logistics service providers can make use of the real options concept. Symposium Mathematik & Logistik, Bad Honnef 2005, Conference Proceedings.
Heldt, D., Kreuzer, M., Pokutta, S., and Poulisse, H. (2006). Algebraische Modellierung mit Methoden der approximativen Computer Algebra und Anwendungen in der Ölindustrie. OR News, 15–18.
Pokutta, S. (2005). Products over countable domains [PhD thesis]. In PhD thesis. University of Duisburg-Essen.
Pokutta, S., and Törner, G. (2005). Fixpunktminimierung bei Binnenschiffen. OR News, 23, 13–17.
Pokutta, S. (2003). Generalizations of the Chase radical and direct products [Master's thesis]. In Diploma thesis. University of Duisburg-Essen.