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ORCIDHerke van Hoof
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- affiliation: University of Amsterdam, The Netherlands
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Books and Theses
- 2016
[b1]Herke van Hoof:
Machine learning through exploration for perception-driven robotics = Machinelles Lernen in der Perzeptions-basierte Robotik. Darmstadt University of Technology, Germany, 2016, pp. 1-116
Journal Articles
- 2023
- [j9]Nutan Chen, Walterio W. Mayol-Cuevas, Maximilian Karl, Elie Aljalbout, Andy Zeng, Aurelio Cortese, Wolfram Burgard, Herke van Hoof:
Editorial: Language, affordance and physics in robot cognition and intelligent systems. Frontiers Robotics AI 10 (2023) - [j8]David Kuric, Herke van Hoof:
Reusable Options through Gradient-based Meta Learning. Trans. Mach. Learn. Res. 2023 (2023) - 2021
- [j7]Shihan Wang, Chao Zhang
, Ben J. A. Kröse, Herke van Hoof:
Optimizing Adaptive Notifications in Mobile Health Interventions Systems: Reinforcement Learning from a Data-driven Behavioral Simulator. J. Medical Syst. 45(12): 102 (2021) - 2020
- [j6]Zeynep Akata, Dan Balliet, Maarten de Rijke, Frank Dignum, Virginia Dignum, Guszti Eiben, Antske Fokkens, Davide Grossi, Koen V. Hindriks, Holger H. Hoos, Hayley Hung, Catholijn M. Jonker, Christof Monz, Mark A. Neerincx, Frans A. Oliehoek, Henry Prakken, Stefan Schlobach, Linda C. van der Gaag, Frank van Harmelen, Herke van Hoof, Birna van Riemsdijk, Aimee van Wynsberghe, Rineke Verbrugge, Bart Verheij, Piek Vossen, Max Welling:
A Research Agenda for Hybrid Intelligence: Augmenting Human Intellect With Collaborative, Adaptive, Responsible, and Explainable Artificial Intelligence. Computer 53(8): 18-28 (2020) - [j5]Wouter Kool, Herke van Hoof, Max Welling:
Ancestral Gumbel-Top-k Sampling for Sampling Without Replacement. J. Mach. Learn. Res. 21: 47:1-47:36 (2020) - 2017
- [j4]Herke van Hoof, Gerhard Neumann, Jan Peters:
Non-parametric Policy Search with Limited Information Loss. J. Mach. Learn. Res. 18: 73:1-73:46 (2017) - [j3]Herke van Hoof, Daniel Tanneberg, Jan Peters:
Generalized exploration in policy search. Mach. Learn. 106(9-10): 1705-1724 (2017) - 2016
- [j2]Christian Daniel, Herke van Hoof, Jan Peters, Gerhard Neumann:
Probabilistic inference for determining options in reinforcement learning. Mach. Learn. 104(2-3): 337-357 (2016) - 2014
- [j1]Herke van Hoof, Oliver Kroemer, Jan Peters:
Probabilistic Segmentation and Targeted Exploration of Objects in Cluttered Environments. IEEE Trans. Robotics 30(5): 1198-1209 (2014)
Conference and Workshop Papers
- 2024
- [c47]Robert T. Loftin, Mustafa Mert Çelikok, Herke van Hoof, Samuel Kaski, Frans A. Oliehoek:
Uncoupled Learning of Differential Stackelberg Equilibria with Commitments. AAMAS 2024: 1265-1273 - [c46]David Kuric, Guillermo Infante, Vicenç Gómez, Anders Jonsson, Herke van Hoof:
Planning with a Learned Policy Basis to Optimally Solve Complex Tasks. ICAPS 2024: 333-341 - [c45]Jin Huang, Harrie Oosterhuis, Masoud Mansoury, Herke van Hoof, Maarten de Rijke:
Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems. SIGIR 2024: 416-426 - 2023
- [c44]Qi Wang, Marco Federici, Herke van Hoof:
Bridge the Inference Gaps of Neural Processes via Expectation Maximization. ICLR 2023 - [c43]Tim Bakker, Herke van Hoof, Max Welling:
Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes. ECML/PKDD (1) 2023: 3-19 - [c42]Jan Wöhlke, Felix Schmitt, Herke van Hoof:
Learning Hierarchical Planning-Based Policies from Offline Data. ECML/PKDD (4) 2023: 489-505 - 2022
- [c41]Alexander Long, Alan Blair, Herke van Hoof:
Fast and Data Efficient Reinforcement Learning from Pixels via Non-parametric Value Approximation. AAAI 2022: 7620-7627 - [c40]Wouter Kool, Herke van Hoof, Joaquim A. S. Gromicho, Max Welling:
Deep Policy Dynamic Programming for Vehicle Routing Problems. CPAIOR 2022: 190-213 - [c39]Elise van der Pol, Herke van Hoof, Frans A. Oliehoek, Max Welling:
Multi-Agent MDP Homomorphic Networks. ICLR 2022 - [c38]Qi Wang, Herke van Hoof:
Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models and Amortized Policy Search. ICML 2022: 23055-23077 - [c37]Niklas Höpner, Ilaria Tiddi, Herke van Hoof:
Leveraging Class Abstraction for Commonsense Reinforcement Learning via Residual Policy Gradient Methods. IJCAI 2022: 3050-3056 - [c36]Jan Wöhlke, Felix Schmitt, Herke van Hoof:
Value Refinement Network (VRN). IJCAI 2022: 3558-3565 - [c35]Charul Giri, Ole-Christoffer Granmo, Herke van Hoof, Christian D. Blakely:
Logic-based AI for Interpretable Board Game Winner Prediction with Tsetlin Machine. IJCNN 2022: 1-9 - [c34]Mukul Gagrani, Corrado Rainone, Yang Yang, Harris Teague, Wonseok Jeon, Roberto Bondesan, Herke van Hoof, Christopher Lott, Weiliang Will Zeng, Piero Zappi:
Neural Topological Ordering for Computation Graphs. NeurIPS 2022 - [c33]Qi Wang, Herke van Hoof:
Learning Expressive Meta-Representations with Mixture of Expert Neural Processes. NeurIPS 2022 - 2021
- [c32]Yijie Zhang, Herke van Hoof:
Deep Coherent Exploration for Continuous Control. ICML 2021: 12567-12577 - [c31]Jan Wöhlke, Felix Schmitt, Herke van Hoof:
Hierarchies of Planning and Reinforcement Learning for Robot Navigation. ICRA 2021: 10682-10688 - 2020
- [c30]Jan Wöhlke, Felix Schmitt, Herke van Hoof:
A Performance-Based Start State Curriculum Framework for Reinforcement Learning. AAMAS 2020: 1503-1511 - [c29]Tessa van der Heiden, Florian Mirus, Herke van Hoof:
Social Navigation with Human Empowerment Driven Deep Reinforcement Learning. ICANN (2) 2020: 395-407 - [c28]Wouter Kool, Herke van Hoof, Max Welling:
Estimating Gradients for Discrete Random Variables by Sampling without Replacement. ICLR 2020 - [c27]Qi Wang, Herke van Hoof:
Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables. ICML 2020: 10018-10028 - [c26]Tim Bakker, Herke van Hoof, Max Welling:
Experimental design for MRI by greedy policy search. NeurIPS 2020 - [c25]Elise van der Pol, Daniel E. Worrall, Herke van Hoof, Frans A. Oliehoek, Max Welling:
MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning. NeurIPS 2020 - [c24]Jin Huang, Harrie Oosterhuis, Maarten de Rijke, Herke van Hoof:
Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems. RecSys 2020: 190-199 - 2019
- [c23]Wouter Kool, Herke van Hoof, Max Welling:
Attention, Learn to Solve Routing Problems! ICLR (Poster) 2019 - [c22]Wouter Kool, Herke van Hoof, Max Welling:
Buy 4 REINFORCE Samples, Get a Baseline for Free! DeepRLStructPred@ICLR 2019 - [c21]Wouter Kool, Herke van Hoof, Max Welling:
Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement. ICML 2019: 3499-3508 - [c20]Sanjay Thakur, Herke van Hoof, Juan Camilo Gamboa Higuera, Doina Precup, David Meger:
Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks. ICRA 2019: 768-774 - [c19]Lucas Caccia, Herke van Hoof, Aaron C. Courville, Joelle Pineau:
Deep Generative Modeling of LiDAR Data. IROS 2019: 5034-5040 - [c18]Wenling Shang, Douwe van der Wal, Herke van Hoof, Max Welling:
Stochastic Activation Actor Critic Methods. ECML/PKDD (3) 2019: 103-117 - 2018
- [c17]Yue Dong, Yikang Shen, Eric Crawford, Herke van Hoof, Jackie Chi Kit Cheung:
BanditSum: Extractive Summarization as a Contextual Bandit. EMNLP 2018: 3739-3748 - [c16]Scott Fujimoto, Herke van Hoof, David Meger:
Addressing Function Approximation Error in Actor-Critic Methods. ICML 2018: 1582-1591 - [c15]Matthew J. A. Smith, Herke van Hoof, Joelle Pineau:
An Inference-Based Policy Gradient Method for Learning Options. ICML 2018: 4710-4719 - [c14]Victor Barbaros, Herke van Hoof, Abbas Abdolmaleki, David Meger:
Eager and Memory-Based Non-Parametric Stochastic Search Methods for Learning Control. ICRA 2018: 1-9 - [c13]Sandeep Manjanna, Herke van Hoof, Gregory Dudek:
Policy Search on Aggregated State Space for Active Sampling. ISER 2018: 211-221 - [c12]Sandeep Manjanna, Herke van Hoof, Gregory Dudek:
Reinforcement Learning with Non-uniform State Representations for Adaptive Search. SSRR 2018: 1-7 - 2017
- [c11]Voot Tangkaratt, Herke van Hoof, Simone Parisi, Gerhard Neumann, Jan Peters, Masashi Sugiyama:
Policy Search with High-Dimensional Context Variables. AAAI 2017: 2632-2638 - 2016
- [c10]Herke van Hoof, Nutan Chen, Maximilian Karl, Patrick van der Smagt, Jan Peters:
Stable reinforcement learning with autoencoders for tactile and visual data. IROS 2016: 3928-3934 - [c9]Zhengkun Yi, Roberto Calandra, Filipe Veiga, Herke van Hoof, Tucker Hermans, Yilei Zhang, Jan Peters:
Active tactile object exploration with Gaussian processes. IROS 2016: 4925-4930 - 2015
- [c8]Herke van Hoof, Jan Peters, Gerhard Neumann:
Learning of Non-Parametric Control Policies with High-Dimensional State Features. AISTATS 2015 - [c7]Herke van Hoof, Tucker Hermans, Gerhard Neumann, Jan Peters:
Learning robot in-hand manipulation with tactile features. Humanoids 2015: 121-127 - [c6]Oliver Kroemer, Christian Daniel, Gerhard Neumann, Herke van Hoof, Jan Peters:
Towards learning hierarchical skills for multi-phase manipulation tasks. ICRA 2015: 1503-1510 - [c5]Filipe Veiga, Herke van Hoof, Jan Peters, Tucker Hermans:
Stabilizing novel objects by learning to predict tactile slip. IROS 2015: 5065-5072 - 2014
- [c4]Bastian Bischoff, Duy Nguyen-Tuong, Herke van Hoof, Andrew McHutchon, Carl E. Rasmussen, Alois C. Knoll, Jan Peters, Marc Peter Deisenroth:
Policy search for learning robot control using sparse data. ICRA 2014: 3882-3887 - [c3]Oliver Kroemer, Herke van Hoof, Gerhard Neumann, Jan Peters:
Learning to predict phases of manipulation tasks as hidden states. ICRA 2014: 4009-4014 - 2013
- [c2]Herke van Hoof, Oliver Kroemer, Jan Peters:
Probabilistic interactive segmentation for anthropomorphic robots in cluttered environments. Humanoids 2013: 169-176 - 2012
- [c1]Herke van Hoof, Oliver Kroemer, Heni Ben Amor, Jan Peters:
Maximally informative interaction learning for scene exploration. IROS 2012: 5152-5158
Informal and Other Publications
- 2024
- [i35]Guillermo Infante, David Kuric, Anders Jonsson, Vicenç Gómez, Herke van Hoof:
Planning with a Learned Policy Basis to Optimally Solve Complex Tasks. CoRR abs/2403.15301 (2024) - [i34]Jin Huang, Harrie Oosterhuis, Masoud Mansoury, Herke van Hoof, Maarten de Rijke:
Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems. CoRR abs/2404.18640 (2024) - [i33]Masoud Mansoury, Bamshad Mobasher, Herke van Hoof:
Mitigating Exposure Bias in Online Learning to Rank Recommendation: A Novel Reward Model for Cascading Bandits. CoRR abs/2408.04332 (2024) - 2023
- [i32]Robert Tyler Loftin, Mustafa Mert Çelikok, Herke van Hoof, Samuel Kaski, Frans A. Oliehoek:
Uncoupled Learning of Differential Stackelberg Equilibria with Commitments. CoRR abs/2302.03438 (2023) - [i31]Tim Bakker, Herke van Hoof, Max Welling:
Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes. CoRR abs/2309.05477 (2023) - [i30]Blazej Manczak, Jan Viebahn, Herke van Hoof:
Hierarchical Reinforcement Learning for Power Network Topology Control. CoRR abs/2311.02129 (2023) - 2022
- [i29]Niklas Höpner, Ilaria Tiddi, Herke van Hoof:
Leveraging class abstraction for commonsense reinforcement learning via residual policy gradient methods. CoRR abs/2201.12126 (2022) - [i28]Alexander Long, Alan Blair, Herke van Hoof:
Fast and Data Efficient Reinforcement Learning from Pixels via Non-Parametric Value Approximation. CoRR abs/2203.03078 (2022) - [i27]Tessa van der Heiden, Herke van Hoof, Efstratios Gavves, Christoph Salge:
Reliably Re-Acting to Partner's Actions with the Social Intrinsic Motivation of Transfer Empowerment. CoRR abs/2203.03355 (2022) - [i26]Charul Giri, Ole-Christoffer Granmo, Herke van Hoof, Christian D. Blakely:
Logic-based AI for Interpretable Board Game Winner Prediction with Tsetlin Machine. CoRR abs/2203.04378 (2022) - [i25]Mukul Gagrani, Corrado Rainone, Yang Yang, Harris Teague, Wonseok Jeon, Herke van Hoof, Weiliang Will Zeng, Piero Zappi, Christopher Lott, Roberto Bondesan:
Neural Topological Ordering for Computation Graphs. CoRR abs/2207.05899 (2022) - [i24]Erik Jenner, Herke van Hoof, Adam Gleave:
Calculus on MDPs: Potential Shaping as a Gradient. CoRR abs/2208.09570 (2022) - [i23]Masoud Mansoury, Bamshad Mobasher, Herke van Hoof:
Exposure-Aware Recommendation using Contextual Bandits. CoRR abs/2209.01665 (2022) - [i22]David Kuric, Herke van Hoof:
Reusable Options through Gradient-based Meta Learning. CoRR abs/2212.11726 (2022) - 2021
- [i21]Qi Wang, Herke van Hoof:
Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models. CoRR abs/2102.08291 (2021) - [i20]Wouter Kool, Herke van Hoof, Joaquim A. S. Gromicho, Max Welling:
Deep Policy Dynamic Programming for Vehicle Routing Problems. CoRR abs/2102.11756 (2021) - [i19]Dmitrii Krasheninnikov, Rohin Shah, Herke van Hoof:
Combining Reward Information from Multiple Sources. CoRR abs/2103.12142 (2021) - [i18]Susan Amin, Maziar Gomrokchi, Harsh Satija, Herke van Hoof, Doina Precup:
A Survey of Exploration Methods in Reinforcement Learning. CoRR abs/2109.00157 (2021) - [i17]Jan Wöhlke, Felix Schmitt, Herke van Hoof:
Hierarchies of Planning and Reinforcement Learning for Robot Navigation. CoRR abs/2109.11178 (2021) - [i16]Elise van der Pol, Herke van Hoof, Frans A. Oliehoek, Max Welling:
Multi-Agent MDP Homomorphic Networks. CoRR abs/2110.04495 (2021) - 2020
- [i15]Wouter Kool, Herke van Hoof, Max Welling:
Estimating Gradients for Discrete Random Variables by Sampling without Replacement. CoRR abs/2002.06043 (2020) - [i14]Tessa van der Heiden, Christian Weiss, Naveen Shankar Nagaraja, Herke van Hoof:
Social navigation with human empowerment driven reinforcement learning. CoRR abs/2003.08158 (2020) - [i13]Elise van der Pol, Daniel E. Worrall, Herke van Hoof, Frans A. Oliehoek, Max Welling:
MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning. CoRR abs/2006.16908 (2020) - [i12]Joris Mollinga, Herke van Hoof:
An Autonomous Free Airspace En-route Controller using Deep Reinforcement Learning Techniques. CoRR abs/2007.01599 (2020) - [i11]Qi Wang, Herke van Hoof:
Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables. CoRR abs/2008.09469 (2020) - [i10]Tim Bakker, Herke van Hoof, Max Welling:
Experimental design for MRI by greedy policy search. CoRR abs/2010.16262 (2020) - [i9]Tessa van der Heiden, Christoph Salge, Efstratios Gavves, Herke van Hoof:
Robust Multi-Agent Reinforcement Learning with Social Empowerment for Coordination and Communication. CoRR abs/2012.08255 (2020) - 2019
- [i8]Sanjay Thakur, Herke van Hoof, Juan Camilo Gamboa Higuera, Doina Precup, David Meger:
Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks. CoRR abs/1903.05697 (2019) - [i7]Wouter Kool, Herke van Hoof, Max Welling:
Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement. CoRR abs/1903.06059 (2019) - [i6]Sandeep Manjanna, Herke van Hoof, Gregory Dudek:
Reinforcement Learning with Non-uniform State Representations for Adaptive Search. CoRR abs/1906.06588 (2019) - [i5]Sanjay Thakur, Herke van Hoof, Gunshi Gupta, David Meger:
Unifying Variational Inference and PAC-Bayes for Supervised Learning that Scales. CoRR abs/1910.10367 (2019) - 2018
- [i4]Scott Fujimoto, Herke van Hoof, David Meger:
Addressing Function Approximation Error in Actor-Critic Methods. CoRR abs/1802.09477 (2018) - [i3]Yue Dong, Yikang Shen, Eric Crawford, Herke van Hoof, Jackie Chi Kit Cheung:
BanditSum: Extractive Summarization as a Contextual Bandit. CoRR abs/1809.09672 (2018) - [i2]Lucas Caccia, Herke van Hoof, Aaron C. Courville, Joelle Pineau:
Deep Generative Modeling of LiDAR Data. CoRR abs/1812.01180 (2018) - 2016
- [i1]Voot Tangkaratt, Herke van Hoof, Simone Parisi, Gerhard Neumann, Jan Peters, Masashi Sugiyama:
Policy Search with High-Dimensional Context Variables. CoRR abs/1611.03231 (2016)
Coauthor Index
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