Dr. T.A.L. (Tim) van Erven
Faculteit der Natuurwetenschappen, Wiskunde en Informatica
Korteweg-de Vries Instituut
Bezoekadres
- Science Park 107
Postadres
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Postbus 94248
1090 GE Amsterdam
Contactgegevens
Social media
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Publicaties
2025
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 [details]
2023
- Castro, R. M., Hellström, F., & van Erven, T. (2023). Adaptive Selective Sampling for Online Prediction with Experts. 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. 134-154). (Advances in Neural Information Processing Systems; Vol. 36). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper_files/paper/2023/hash/00b67df24009747e8bbed4c2c6f9c825-Abstract-Conference.html [details]
- Fokkema, H., de Heide, R., & van Erven, T. (2023). Attribution-based Explanations that Provide Recourse cannot be Robust. Journal of Machine Learning Research, 24, Article 23-0042. [details]
- 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 [details]
- 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 [details]
- Olkhovskaya, J., Mayo, J., van Erven, T., Neu, G., & Wei, C.-Y. (2023). First- and Second-Order Bounds for Adversarial Linear Contextual Bandits. 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. 61625-61644). (Advances in Neural Information Processing Systems; Vol. 36). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper_files/paper/2023/hash/c2201e444d2b22a10ca50116a522b9a9-Abstract-Conference.html [details]
2022
- 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 [details]
- 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 [details]
2021
- van Erven, T., Koolen, W. M., & van der Hoeven, D. (2021). MetaGrad: Adaptation using Multiple Learning Rates in Online Learning. Journal of Machine Learning Research, 22(161), 1-61. https://jmlr.org/papers/v22/20-1444.html [details]
- van Erven, T., Sachs, S., Koolen, W. M., & Kotłowski, W. (2021). Robust Online Convex Optimization in the Presence of Outliers. Proceedings of Machine Learning Research, 134, 4174-4194. https://proceedings.mlr.press/v134/vanerven21a.html [details]
2007
- van Erven, T. A. L., Grünwald, P. D., & de Rooij, S. (2007). Catching Up Faster in Bayesian Model Selection and Model Averaging. In Advances in Neural Information Processing Systems (pp. 417-424). Neural Information Processing Systems (NIPS) Foundation. [details]
2024
- Fokkema, H. J., Garreau, D., & van Erven, T. A. L. (2024). The Risks of Recourse in Binary Classification. 1-20. Paper presented at Conference on Artificial Intelligence & Statistics, Valencia, Spain.
2023
- Sachs, S., van Erven, T., Hodgkinson, L., Khanna, R., & Şimşekli, U. (2023). Generalization Guarantees via Algorithm-dependent Rademacher Complexity. 4863-4880. Paper presented at 36th Annual Conference on Learning Theory, COLT 2023, Bangalore, India.
Andere
- van Erven, T. A. L. (other) (2007). Lecturer Machine Learning, Vrije Universiteit van Amsterdam (other).
2026
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Nevenwerkzaamheden
Geen nevenwerkzaamheden
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.