van Hoof, Herke

29 publications

ICLR 2026 Gradient-Based Program Synthesis with Neurally Interpreted Languages Matthew Macfarlane, Clément Bonnet, Herke van Hoof, Levi Lelis

AAAI 2025 Data Augmentation for Instruction Following Policies via Trajectory Segmentation Niklas Höpner, Ilaria Tiddi, Herke van Hoof

ICLR 2023 Bridge the Inference Gaps of Neural Processes via Expectation Maximization Qi Wang, Marco Federici, Herke van Hoof

ECML-PKDD 2023 Learning Hierarchical Planning-Based Policies from Offline Data Jan Wöhlke, Felix Schmitt, Herke van Hoof

ECML-PKDD 2023 Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes Tim Bakker, Herke van Hoof, Max Welling

TMLR 2023 Reusable Options Through Gradient-Based Meta Learning David Kuric, Herke van Hoof

AAAI 2022 Fast and Data Efficient Reinforcement Learning from Pixels via Non-Parametric Value Approximation Alexander Long, Alan Blair, Herke van Hoof

NeurIPS 2022 Learning Expressive Meta-Representations with Mixture of Expert Neural Processes Qi Wang, Herke van Hoof

IJCAI 2022 Leveraging Class Abstraction for Commonsense Reinforcement Learning via Residual Policy Gradient Methods Niklas Höpner, Ilaria Tiddi, Herke van Hoof

ICML 2022 Model-Based Meta Reinforcement Learning Using Graph Structured Surrogate Models and Amortized Policy Search Qi Wang, Herke Van Hoof

ICLR 2022 Multi-Agent MDP Homomorphic Networks Elise van der Pol, Herke van Hoof, Frans A Oliehoek, Max Welling

NeurIPS 2022 Neural Topological Ordering for Computation Graphs Mukul Gagrani, Corrado Rainone, Yang Yang, Harris Teague, Wonseok Jeon, Roberto Bondesan, Herke van Hoof, Christopher Lott, Weiliang Zeng, Piero Zappi

NeurIPSW 2022 Training Graph Neural Networks with Policy Gradients to Perform Tree Search Matthew Macfarlane, Diederik M Roijers, Herke van Hoof

IJCAI 2022 Value Refinement Network (VRN) Jan Wöhlke, Felix Schmitt, Herke van Hoof

ICML 2021 Deep Coherent Exploration for Continuous Control Yijie Zhang, Herke Van Hoof

JMLR 2020 Ancestral Gumbel-Top-K Sampling for Sampling Without Replacement Wouter Kool, Herke van Hoof, Max Welling

ICML 2020 Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables Qi Wang, Herke Van Hoof

ICLR 2020 Estimating Gradients for Discrete Random Variables by Sampling Without Replacement Wouter Kool, Herke van Hoof, Max Welling

NeurIPS 2020 Experimental Design for MRI by Greedy Policy Search Tim Bakker, Herke van Hoof, Max Welling

NeurIPS 2020 MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning Elise van der Pol, Daniel Worrall, Herke van Hoof, Frans Oliehoek, Max Welling

ICLR 2019 Attention, Learn to Solve Routing Problems! Wouter Kool, Herke van Hoof, Max Welling

ICLRW 2019 Buy 4 REINFORCE Samples, Get a Baseline for Free! Wouter Kool, Herke van Hoof, Max Welling

ECML-PKDD 2019 Stochastic Activation Actor Critic Methods Wenling Shang, Douwe van der Wal, Herke van Hoof, Max Welling

ICML 2019 Stochastic Beams and Where to Find Them: The Gumbel-Top-K Trick for Sampling Sequences Without Replacement Wouter Kool, Herke Van Hoof, Max Welling

MLJ 2017 Generalized Exploration in Policy Search Herke van Hoof, Daniel Tanneberg, Jan Peters

JMLR 2017 Non-Parametric Policy Search with Limited Information Loss Herke van Hoof, Gerhard Neumann, Jan Peters

AAAI 2017 Policy Search with High-Dimensional Context Variables Voot Tangkaratt, Herke van Hoof, Simone Parisi, Gerhard Neumann, Jan Peters, Masashi Sugiyama

MLJ 2016 Probabilistic Inference for Determining Options in Reinforcement Learning Christian Daniel, Herke van Hoof, Jan Peters, Gerhard Neumann

AISTATS 2015 Learning of Non-Parametric Control Policies with High-Dimensional State Features Herke van Hoof, Jan Peters, Gerhard Neumann