Maximally informative interaction learning for scene exploration. - Herke van Hoof; Oliver Kroemer; Heni Ben Amor; Jan Peters 001 (IROS, 2012)

Probabilistic interactive segmentation for anthropomorphic robots in cluttered environments. - Herke van Hoof; Oliver Kroemer; Jan Peters 001 (Humanoids, 2013)

Policy search for learning robot control using sparse data. - Bastian Bischoff; Duy Nguyen-Tuong; Herke van Hoof; Andrew McHutchon; Carl E. Rasmussen; Alois C. Knoll; Jan Peters 001; Marc Peter Deisenroth (ICRA, 2014)

Learning to predict phases of manipulation tasks as hidden states. - Oliver Kroemer; Herke van Hoof; Gerhard Neumann; Jan Peters 001 (ICRA, 2014)

Probabilistic Segmentation and Targeted Exploration of Objects in Cluttered Environments. - Herke van Hoof; Oliver Kroemer; Jan Peters 001 (IEEE Trans. Robotics, 2014)

Learning of Non-Parametric Control Policies with High-Dimensional State Features. - Herke van Hoof; Jan Peters 001; Gerhard Neumann (AISTATS, 2015)

Learning robot in-hand manipulation with tactile features. - Herke van Hoof; Tucker Hermans; Gerhard Neumann; Jan Peters 001 (Humanoids, 2015)

Towards learning hierarchical skills for multi-phase manipulation tasks. - Oliver Kroemer; Christian Daniel; Gerhard Neumann; Herke van Hoof; Jan Peters 001 (ICRA, 2015)

Stabilizing novel objects by learning to predict tactile slip. - Filipe Veiga; Herke van Hoof; Jan Peters 001; Tucker Hermans (IROS, 2015)

Stable reinforcement learning with autoencoders for tactile and visual data. - Herke van Hoof; Nutan Chen; Maximilian Karl; Patrick van der Smagt; Jan Peters 001 (IROS, 2016)

Active tactile object exploration with Gaussian processes. - Zhengkun Yi; Roberto Calandra; Filipe Veiga; Herke van Hoof; Tucker Hermans; Yilei Zhang; Jan Peters 001 (IROS, 2016)

Probabilistic inference for determining options in reinforcement learning. - Christian Daniel; Herke van Hoof; Jan Peters 001; Gerhard Neumann (Mach. Learn., 2016)

Machine learning through exploration for perception-driven robotics = Machinelles Lernen in der Perzeptions-basierte Robotik. - Herke van Hoof (2016)

Policy Search with High-Dimensional Context Variables. - Voot Tangkaratt; Herke van Hoof; Simone Parisi; Gerhard Neumann; Jan Peters 001; Masashi Sugiyama (AAAI, 2017)

Non-parametric Policy Search with Limited Information Loss. - Herke van Hoof; Gerhard Neumann; Jan Peters 001 (J. Mach. Learn. Res., 2017)

Generalized exploration in policy search. - Herke van Hoof; Daniel Tanneberg; Jan Peters 001 (Mach. Learn., 2017)

BanditSum: Extractive Summarization as a Contextual Bandit. - Yue Dong 002; Yikang Shen; Eric Crawford; Herke van Hoof; Jackie Chi Kit Cheung (EMNLP, 2018)

Addressing Function Approximation Error in Actor-Critic Methods. - Scott Fujimoto; Herke van Hoof; David Meger (ICML, 2018)

An Inference-Based Policy Gradient Method for Learning Options. - Matthew J. A. Smith; Herke van Hoof; Joelle Pineau (ICML, 2018)

Eager and Memory-Based Non-Parametric Stochastic Search Methods for Learning Control. - Victor Barbaros; Herke van Hoof; Abbas Abdolmaleki; David Meger (ICRA, 2018)

Policy Search on Aggregated State Space for Active Sampling. - Sandeep Manjanna; Herke van Hoof; Gregory Dudek (ISER, 2018)

Reinforcement Learning with Non-uniform State Representations for Adaptive Search. - Sandeep Manjanna; Herke van Hoof; Gregory Dudek (SSRR, 2018)

Attention, Learn to Solve Routing Problems! - Wouter Kool 001; Herke van Hoof; Max Welling (ICLR (Poster), 2019)

Buy 4 REINFORCE Samples, Get a Baseline for Free! - Wouter Kool 001; Herke van Hoof; Max Welling (DeepRLStructPred@ICLR, 2019)

Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement. - Wouter Kool 001; Herke van Hoof; Max Welling (ICML, 2019)

Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks. - Sanjay Thakur; Herke van Hoof; Juan Camilo Gamboa Higuera; Doina Precup; David Meger (ICRA, 2019)

Deep Generative Modeling of LiDAR Data. - Lucas Caccia; Herke van Hoof; Aaron C. Courville; Joelle Pineau (IROS, 2019)

Stochastic Activation Actor Critic Methods. - Wenling Shang; Douwe van der Wal; Herke van Hoof; Max Welling (ECML/PKDD (3), 2019)

Unifying Variational Inference and PAC-Bayes for Supervised Learning that Scales. - Sanjay Thakur; Herke van Hoof; Gunshi Gupta; David Meger (CoRR, 2019)

A Performance-Based Start State Curriculum Framework for Reinforcement Learning. - Jan Wöhlke; Felix Schmitt 001; Herke van Hoof (AAMAS, 2020)

Social Navigation with Human Empowerment Driven Deep Reinforcement Learning. - Tessa van der Heiden; Florian Mirus; Herke van Hoof (ICANN (2), 2020)

Estimating Gradients for Discrete Random Variables by Sampling without Replacement. - Wouter Kool 001; Herke van Hoof; Max Welling (ICLR, 2020)

Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables. - Qi Wang; Herke van Hoof (ICML, 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 E. Worrall; Herke van Hoof; Frans A. Oliehoek; Max Welling (NeurIPS, 2020)

Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems. - Jin Huang 010; Harrie Oosterhuis; Maarten de Rijke; Herke van Hoof (RecSys, 2020)

A Research Agenda for Hybrid Intelligence: Augmenting Human Intellect With Collaborative, Adaptive, Responsible, and Explainable Artificial Intelligence. - 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; et al. (Computer, 2020)

Social navigation with human empowerment driven reinforcement learning. - Tessa van der Heiden; Christian Weiss; Naveen Shankar Nagaraja; Herke van Hoof (CoRR, 2020)

An Autonomous Free Airspace En-route Controller using Deep Reinforcement Learning Techniques. - Joris Mollinga; Herke van Hoof (CoRR, 2020)

Robust Multi-Agent Reinforcement Learning with Social Empowerment for Coordination and Communication. - Tessa van der Heiden; Christoph Salge; Efstratios Gavves; Herke van Hoof (CoRR, 2020)

Ancestral Gumbel-Top-k Sampling for Sampling Without Replacement. - Wouter Kool 001; Herke van Hoof; Max Welling (J. Mach. Learn. Res., 2020)

Deep Coherent Exploration for Continuous Control. - Yijie Zhang; Herke van Hoof (ICML, 2021)

Hierarchies of Planning and Reinforcement Learning for Robot Navigation. - Jan Wöhlke; Felix Schmitt 001; Herke van Hoof (ICRA, 2021)

Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models. - Qi Wang; Herke van Hoof (CoRR, 2021)

Combining Reward Information from Multiple Sources. - Dmitrii Krasheninnikov; Rohin Shah; Herke van Hoof (CoRR, 2021)

A Survey of Exploration Methods in Reinforcement Learning. - Susan Amin; Maziar Gomrokchi; Harsh Satija; Herke van Hoof; Doina Precup (CoRR, 2021)

Optimizing Adaptive Notifications in Mobile Health Interventions Systems: Reinforcement Learning from a Data-driven Behavioral Simulator. - Shihan Wang 001; Chao Zhang 071; Ben J. A. Kröse; Herke van Hoof (J. Medical Syst., 2021)

Fast and Data Efficient Reinforcement Learning from Pixels via Non-parametric Value Approximation. - Alexander Long; Alan Blair 001; Herke van Hoof (AAAI, 2022)

Deep Policy Dynamic Programming for Vehicle Routing Problems. - Wouter Kool 001; Herke van Hoof; Joaquim A. S. Gromicho; Max Welling (CPAIOR, 2022)

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

Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models and Amortized Policy Search. - Qi Wang; Herke van Hoof (ICML, 2022)

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

Value Refinement Network (VRN). - Jan Wöhlke; Felix Schmitt 001; Herke van Hoof (IJCAI, 2022)

Logic-based AI for Interpretable Board Game Winner Prediction with Tsetlin Machine. - Charul Giri; Ole-Christoffer Granmo; Herke van Hoof; Christian D. Blakely (IJCNN, 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 Will Zeng; Piero Zappi (NeurIPS, 2022)

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

Reliably Re-Acting to Partner's Actions with the Social Intrinsic Motivation of Transfer Empowerment. - Tessa van der Heiden; Herke van Hoof; Efstratios Gavves; Christoph Salge (CoRR, 2022)

Calculus on MDPs: Potential Shaping as a Gradient. - Erik Jenner; Herke van Hoof; Adam Gleave (CoRR, 2022)

Exposure-Aware Recommendation using Contextual Bandits. - Masoud Mansoury; Bamshad Mobasher; Herke van Hoof (CoRR, 2022)

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

Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes. - Tim Bakker; Herke van Hoof; Max Welling (ECML/PKDD (1), 2023)

Learning Hierarchical Planning-Based Policies from Offline Data. - Jan Wöhlke; Felix Schmitt 001; Herke van Hoof (ECML/PKDD (4), 2023)

Hierarchical Reinforcement Learning for Power Network Topology Control. - Blazej Manczak; Jan Viebahn; Herke van Hoof (CoRR, 2023)

Editorial: Language, affordance and physics in robot cognition and intelligent systems. - Nutan Chen; Walterio W. Mayol-Cuevas; Maximilian Karl; Elie Aljalbout; Andy Zeng 001; Aurelio Cortese; Wolfram Burgard; Herke van Hoof (Frontiers Robotics AI, 2023)

Reusable Options through Gradient-based Meta Learning. - David Kuric; Herke van Hoof (Trans. Mach. Learn. Res., 2023)

Uncoupled Learning of Differential Stackelberg Equilibria with Commitments. - Robert T. Loftin; Mustafa Mert Çelikok; Herke van Hoof; Samuel Kaski; Frans A. Oliehoek (AAMAS, 2024)

Mitigating Exposure Bias in Online Learning to Rank Recommendation: A Novel Reward Model for Cascading Bandits. - Masoud Mansoury; Bamshad Mobasher; Herke van Hoof (CIKM, 2024)

Planning with a Learned Policy Basis to Optimally Solve Complex Tasks. - David Kuric; Guillermo Infante; Vicenç Gómez; Anders Jonsson 001; Herke van Hoof (ICAPS, 2024)

Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems. - Jin Huang 010; Harrie Oosterhuis; Masoud Mansoury; Herke van Hoof; Maarten de Rijke (SIGIR, 2024)

Data Augmentation for Instruction Following Policies via Trajectory Segmentation. - Niklas Höpner; Ilaria Tiddi; Herke van Hoof (AAAI, 2025)

Making Universal Policies Universal. - Niklas Höpner; David Kuric; Herke van Hoof (AAMAS, 2025)