Wouter Koolen-Wijkstra
- Full Name
- Dr. W.M. Koolen-Wijkstra
- Function(s)
- Scientific Staff Member
- W.M.Koolen-Wijkstra@cwi.nl
- Telephone
- +31 20 592 4009
- Room
- L132
- Department(s)
- Machine Learning
- Homepage
- http://wouterkoolen.info/
Biography
Wouter M. Koolen works on topics in machine learning theory and related areas, including game theory, information theory, statistics and optimisation. My current interests include pure exploration in multi-armed bandit models, game tree search, and provably accelerated learning in statistical and individual-sequence settings, aka learning faster from easy data. Koolen is a member of the INRIA-CWI associate teams 6PAC with Inria Lille and 4TUNE with Inria Paris and Grenoble. He is an ELLIS Scholar.Research
I work on topics in machine learning and related areas, including game theory, information theory, statistics and optimisation.
My current projects are
- Game tree search
- Learning faster from easy data
- Online learning beyond experts
- Minimax square loss prediction
- Online Finance
- Switching Experts
- Non-uniform regret analysis
- Robust statistics
- Algorithmic information theory
Publications
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Degenne, R.R.B.P, Shao, H, & Koolen-Wijkstra, W.M. (2020). Structure adaptive algorithms for stochastic bandits. In Proceedings of the 37th International Conference on Machine Learning (pp. 2443–2452).
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van Erven, T.A.L, van der Hoeven, D, Kotlowski, W.T, & Koolen-Wijkstra, W.M. (2020). Open problem: Fast and optimal online portfolio selection. In Proceedings of Machine Learning Research (pp. 3864–3869).
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Mhammedi, Z, & Koolen-Wijkstra, W.M. (2020). Lipschitz and comparator-norm adaptivity in online learning. In Proceedings of Machine Learning Research (pp. 2858–2887).
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van Ommen, M, Koolen-Wijkstra, W.M, & Grünwald, P.D. (2019). Efficient algorithms for minimax decisions under tree-structured incompleteness. In Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence (pp. 336–347). doi:10.1007/978-3-030-29765-7_28
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Mhammedi, Z, Koolen-Wijkstra, W.M, & van Erven, T.A.L. (2019). Lipschitz Adaptivity with Multiple Learning Rates in Online Learning. In Proceedings of Machine Learning Research (pp. 1–22).
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Degenne, R.R.B.P, Koolen-Wijkstra, W.M, & Ménard, P. (2019). Non-Asymptotic Pure Exploration by Solving Games. In Advances in meural information processing systems.
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Degenne, R.R.B.P, & Koolen-Wijkstra, W.M. (2019). Pure Exploration with Multiple Correct Answers. In Advances in meural information processing systems.
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Kaufmann, E, Koolen-Wijkstra, W.M, & Garivier, A. (2018). Sequential test for the lowest mean: From Thompson to Murphy sampling. In Advances in Neural Information Processing Systems (pp. 6332–6342).
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Kaufmann, E, & Koolen-Wijkstra, W.M. (2017). Monte-Carlo tree search by best arm identification. In Advances in Neural Information Processing Systems (pp. 4898–4907).
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Kotłowski, W, Koolen-Wijkstra, W.M, & Malek, A. (2017). Random permutation online isotonic regression. In Advances in Neural Information Processing Systems 30 (NIPS 2017) (pp. 4181–4190).
Professional activities
- Committee member: Area Chair for Neural Information Processing Systems (NeurIPS)
- Committee member: Area Chair for International Conference on Machine Learning (ICML)
- Committee member: Program Committee for Conference on Learning Theory (COLT)
Grants
- Veni Learning at the Intrinsic Task Pace 2015 (2015)
- Queensland University of Technology Vice-Chancellor's research fellowship The Versatile Multitask Learner (2013)
- Rubicon - Game-Theoretically Optimal Online Learning: From Conflicting Advice to High-Quality Decisions (2010)