Dr. T.A.L. (Tim) van Erven
Faculty of Science
KDV
Visiting address
- Science Park 107
Postal address
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Postbus 94248
1090 GE Amsterdam
Contact details
Social media
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Publications
2023
- Fokkema, H. J., de Heide, R., & van Erven, T. A. L. (2023). Attribution-based Explanations that Provide Recourse cannot be Robust. Journal of Machine Learning Research, 1-37. Article 23-0042.
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]- Sachs, S., Hadiji, H., van Erven, T., & Guzmán, C. (2022). 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.), Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022 (Advances in Neural Information Processing Systems; Vol. 35). Neural Information Processing Systems Foundation.
- 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.
Others
- van Erven, T. A. L. (other) (2007). Lecturer Machine Learning, Vrije Universiteit van Amsterdam (other).
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Ancillary activities
No ancillary activities
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.