Elise van der Pol

My research interests lie in structure, symmetry, and equivariance in reinforcement learning and machine learning. I'm a PhD student in the Amsterdam Machine Learning Lab, working with Max Welling (UvA), Frans Oliehoek (TU Delft) and Herke van Hoof (UvA). Before my PhD, I studied Artificial Intelligence at the University of Amsterdam where I obtained my Master's degree (cum laude). My MSc thesis was on the subject of Deep Reinforcement Learning for Coordinated Traffic Light Controllers. I am also involved in Inclusive AI.

News

Organization: We're organizing a workshop on data-centric reinforcement learning at NeurIPS 2021.
Talk: 15 July 2021 I gave a talk on Geometric Deep Learning and Reinforcement Learning for the Geometric Deep Learning course in the African Masters in Machine Intelligence.
Paper: The Impact of Negative Sampling on Contrastive Structured World Models was accepted to the ICML 2021 workshop on Self-Supervised Learning for Reasoning and Perception.
Visit: I'm currently a research scientist intern in DeepMind's Multi-Agent team.
Organization: We organized an ICLR social which took place 6 May 2021.
Talk: 7 May 2021 I gave a keynote talk on Equivariance in MDPs at the ICLR workshop on Self-supervision for Reinforcement Learning.

Talk: 15 December 2020 I gave a talk on MDP Homomorphic Networks for Deep Reinforcement Learning at the University of Tuebingen.
Talk: 23 November 2020 I gave a guest lecture Introduction to Reinforcement Learning in University of Amsterdam's MSc Data Science course Applied Machine Learning.
Talk: 13 November 2020 I gave a talk on Equivariance, deep reinforcement learning, and making it work at Leiden University.
Talk: I gave a talk on Data-efficient Reinforcement Learning as part of TU Delft's AI in Practice course.
Paper: MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning was accepted to NeurIPS 2020.
Paper: Plannable Approximations to MDP Homomorphisms: Equivariance under Actions was accepted to AAMAS 2020.
Paper: Contrastive Learning of Structured World Models was accepted to ICLR 2020 as a long talk.

The Impact of Negative Sampling on Contrastive Structured World Models - O. Biza, E. van der Pol, T. Kipf, ICML 2021 workshop on Self-Supervised Learning for Reasoning and Perception
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MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning - E. van der Pol, D.E. Worrall, H. van Hoof, F.A. Oliehoek, M. Welling, NeurIPS 2020
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Plannable Approximations to MDP Homomorphisms: Equivariance under Actions - E. van der Pol, T. Kipf, F.A. Oliehoek, M. Welling, AAMAS 2020
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Contrastive Learning of Structured World Models - T. Kipf, E. van der Pol, M. Welling, ICLR 2020 (Oral)
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Hyperspherical Prototype Networks - P. Mettes, E. van der Pol, C.G.M. Snoek, NeurIPS 2019
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Beyond Local Nash Equilibria for Adversarial Networks - F.A. Oliehoek, R. Savani, J. Gallego-Posada, E. van der Pol, R. Groß, Benelearn 2018
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Visual Rationalizations in Deep Reinforcement Learning for Atari Games - L. Weitkamp, E. van der Pol, Z. Akata, BNAIC 2018 (Oral)
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GANGs: Generative Adversarial Network Games - F.A. Oliehoek, R. Savani, J. Gallego-Posada, E. van der Pol, E.D. de Jong, R. Groß, Arxiv Preprint
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Deep Reinforcement Learning for Coordination in Traffic Light Control - E. van der Pol, Master's Thesis, University of Amsterdam
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Coordinated Deep Reinforcement Learners for Traffic Light Control - E. van der Pol, F.A. Oliehoek, NIPS 2016 Workshop on Learning, Inference and Control of Multi-Agent Systems
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Video Demo: Deep Reinforcement Learning for Coordination in Traffic Light Control - E. van der Pol, F.A. Oliehoek, BNAIC 2016
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Linguistic Style Accommodation in Disagreements - E. van der Pol, S. Gieske, R. Fernández, *SEM 2016
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Empirical Evaluation of Collective Rationality for Quota Rules in Judgment Aggregation - S. Gieske, E. van der Pol, U. Endriss, BNAIC 2015
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