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Results: 5
Number of items: 5
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Sachs, S., Hadiji, H., van Erven, T., & Staudigl, M. (2025). An Online Feasible Point Method for Benign Generalized Nash Equilibrium Problems. Proceedings of Machine Learning Research, 272, 1008-1040. https://proceedings.mlr.press/v272/sachs25a.html -
Hadiji, H., Sachs, S., van Even, T., & Koolen, W. (2023). Towards Characterizing the First-order Query Complexity of Learning (Approximate) Nash Equilibria in Zero-sum Matrix Games. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), 37th Conference on Neural Information Processing Systems (NeurIPS 2023): 10-16 December 2023, New Orleans, Louisana, USA (pp. 13356-13373). (Advances in Neural Information Processing Systems; Vol. 36). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper_files/paper/2023/hash/2af57f909a99113db071672da236a5f2-Abstract-Conference.html
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Guzmán, C., Hadiji, H., Sachs, S., & Van Erven, T. (2023). Between Stochastic and Adversarial Online Convex Optimization: Improved Regret Bounds via Smoothness. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), 36th Conference on Neural Information Processing Systems (NeurIPS 2022): New Orleans, Louisiana, USA, 28 November-9 December 2022 (Vol. 2, pp. 691-702). (Advances in Neural Information Processing Systems; Vol. 35). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper_files/paper/2022/hash/047aa59e51e3ac7a2422a55468feefd5-Abstract-Conference.html
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van der Hoeven, D., Hadiji, H., & van Erven, T. (2022). Distributed Online Learning for Joint Regret with Communication Constraints. Proceedings of Machine Learning Research, 167, 1003-1042. https://proceedings.mlr.press/v167/hoeven22a.html
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Mayo, J. J., Hadiji, H., & van Erven, T. (2022). Scale-free Unconstrained Online Learning for Curved Losses. Proceedings of Machine Learning Research, 178, 4464-4497. https://proceedings.mlr.press/v178/mayo22a.html
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