@inproceedings{ZimmerSpiegelPokutta, author = {Zimmer, Max and Spiegel, Christoph and Pokutta, Sebastian}, title = {How I Learned to Stop Worrying and Love Retraining}, series = {Proceedings of International Conference on Learning Representations}, booktitle = {Proceedings of International Conference on Learning Representations}, language = {en} } @article{Pokutta, author = {Pokutta, Sebastian}, title = {The Frank-Wolfe algorithm: a short introduction}, series = {Jahresbericht der Deutschen Mathematiker-Vereinigung}, journal = {Jahresbericht der Deutschen Mathematiker-Vereinigung}, language = {en} } @inproceedings{ZimmerSpiegelPokutta, author = {Zimmer, Max and Spiegel, Christoph and Pokutta, Sebastian}, title = {Sparse Model Soups}, series = {Proceedings of International Conference on Learning Representations}, booktitle = {Proceedings of International Conference on Learning Representations}, language = {en} } @article{DesignolleIommazzoBesanconetal., author = {Designolle, S{\´e}bastien and Iommazzo, Gabriele and Besan{\c{c}}on, Mathieu and Knebel, Sebastian and Gelß, Patrick and Pokutta, Sebastian}, title = {Improved local models and new Bell inequalities via Frank-Wolfe algorithms}, series = {Physical Review Research}, volume = {5}, journal = {Physical Review Research}, doi = {10.1103/PhysRevResearch.5.043059}, pages = {043059}, abstract = {In Bell scenarios with two outcomes per party, we algorithmically consider the two sides of the membership problem for the local polytope: Constructing local models and deriving separating hyperplanes, that is, Bell inequalities. We take advantage of the recent developments in so-called Frank-Wolfe algorithms to significantly increase the convergence rate of existing methods. First, we study the threshold value for the nonlocality of two-qubit Werner states under projective measurements. Here, we improve on both the upper and lower bounds present in the literature. Importantly, our bounds are entirely analytical; moreover, they yield refined bounds on the value of the Grothendieck constant of order three: 1.4367⩽KG(3)⩽1.4546. Second, we demonstrate the efficiency of our approach in multipartite Bell scenarios, and present local models for all projective measurements with visibilities noticeably higher than the entanglement threshold. We make our entire code accessible as a julia library called BellPolytopes.jl.}, language = {en} } @inproceedings{GasseBowlyCappartetal., author = {Gasse, Maxime and Bowly, Simon and Cappart, Quentin and Charfreitag, Jonas and Charlin, Laurent and Ch{\´e}telat, 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}, series = {Proceedings of Conference on Neural Information Processing Systems}, booktitle = {Proceedings of Conference on Neural Information Processing Systems}, language = {en} } @inproceedings{WirthKeraPokutta, author = {Wirth, Elias and Kera, and Pokutta, Sebastian}, title = {Approximate Vanishing Ideal Computations at Scale}, series = {Proceedings of International Conference on Learning Representations}, booktitle = {Proceedings of International Conference on Learning Representations}, language = {en} } @inproceedings{MartinezRubioRouxCriscitielloetal., author = {Mart{\´i}nez-Rubio, David and Roux, Christophe and Criscitiello, Christopher and Pokutta, Sebastian}, title = {Accelerated Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties}, series = {Proceedings of Optimization for Machine Learning (NeurIPS Workshop OPT 2023)}, booktitle = {Proceedings of Optimization for Machine Learning (NeurIPS Workshop OPT 2023)}, language = {en} } @inproceedings{MartinezRubioPokutta, author = {Mart{\´i}nez-Rubio, David and Pokutta, Sebastian}, title = {Accelerated Riemannian optimization: Handling constraints with a prox to bound geometric penalties}, series = {Proceedings of Optimization for Machine Learning (NeurIPS Workshop OPT 2022)}, booktitle = {Proceedings of Optimization for Machine Learning (NeurIPS Workshop OPT 2022)}, language = {en} } @article{HunkenschroederPokuttaWeismantel, author = {Hunkenschr{\"o}der, Christoph and Pokutta, Sebastian and Weismantel, Robert}, title = {Optimizing a low-dimensional convex function over a high-dimensional cube}, series = {SIAM Journal on Optimization}, journal = {SIAM Journal on Optimization}, language = {en} } @inproceedings{ThuerckSofranacPfetschetal., author = {Thuerck, Daniel and Sofranac, Boro and Pfetsch, Marc and Pokutta, Sebastian}, title = {Learning cuts via enumeration oracles}, series = {Proceedings of Conference on Neural Information Processing Systems}, booktitle = {Proceedings of Conference on Neural Information Processing Systems}, language = {en} }