H. V. Hoof

发表

Probabilistic inference for determining options in reinforcement learning

Jan Peters, Herke van Hoof, Christian Daniel, 2016, Machine Learning.

Learning robot in-hand manipulation with tactile features

Jan Peters, Herke van Hoof, Gerhard Neumann, 2015, 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids).

Learning of Non-Parametric Control Policies with High-Dimensional State Features

Jan Peters, Herke van Hoof, Gerhard Neumann, 2015, AISTATS.

Towards learning hierarchical skills for multi-phase manipulation tasks

Oliver Kroemer, Jan Peters, Herke van Hoof, 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

Maximally informative interaction learning for scene exploration

Oliver Kroemer, Jan Peters, Herke van Hoof, 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

Stabilizing novel objects by learning to predict tactile slip

Jan Peters, Herke van Hoof, Filipe Veiga, 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement

Max Welling, Wouter Kool, Herke van Hoof, 2019, ICML.

Active tactile object exploration with Gaussian processes

Jan Peters, Herke van Hoof, Filipe Veiga, 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Interaction between Face Detection and Learning Tracking Systems for Autonomous Robots Herke van Hoof

H. V. Hoof, 2011 .

Policy Search with Non-uniform State Representations for Environmental Sampling

H. V. Hoof, G. Dudek, Sandeep Manjanna, 2019 .

Reinforcement Learning for a Dexterous Manipulation Task

H. V. Hoof, 2016 .

Learning a Multi-Task Model for Control of a Compliant Robot

Jan Peters, H. V. Hoof, M. Smyk, 2016 .

Non-Parametric Policy Learning for High-Dimensional State Representations

Jan Peters, G. Neumann, H. V. Hoof, 2015 .

Multi-Agent MDP Homomorphic Networks

Elise van der Pol, M. Welling, F. Oliehoek, 2021, ICLR.

Buy 4 REINFORCE Samples, Get a Baseline for Free!

Max Welling, Wouter Kool, Herke van Hoof, 2019, DeepRLStructPred@ICLR.

Leveraging class abstraction for commonsense reinforcement learning via residual policy gradient methods

H. V. Hoof, Ilaria Tiddi, Niklas Höpner, 2022, IJCAI.

Learning Generalizable Models for Compliant Robots

J. Peters, H. V. Hoof, M. Smyk, 2014 .

Neural Topological Ordering for Computation Graphs

H. V. Hoof, Corrado Rainone, P. Zappi, 2022, 2207.05899.

Value Refinement Network (VRN)

H. V. Hoof, Felix Schmitt, J. Wöhlke, 2022, IJCAI.

Unifying Variational Inference and PAC-Bayes for Supervised Learning that Scales

H. V. Hoof, D. Meger, Sanjay Thakur, 2019, ArXiv.

Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks

Doina Precup, H. V. Hoof, D. Meger, 2019, 2019 International Conference on Robotics and Automation (ICRA).

Neural Topological Ordering for Computation Graphs

H. V. Hoof, Corrado Rainone, P. Zappi, 2022, NeurIPS.

Machine learning through exploration for perception-driven robotics = Machinelles Lernen in der Perzeptions-basierte Robotik

Herke van Hoof, H. V. Hoof, 2016 .

Addressing Function Approximation Error in Actor-Critic Methods pdf

Herke van Hoof, David Meger, Scott Fujimoto, 2018, ICML.

Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks

Sanjay Thakur, Doina Precup, Herke van Hoof, 2019, ICRA.

Experimental design for MRI by greedy policy search pdf

Max Welling, Herke van Hoof, Tim Bakker, 2020, NeurIPS.

Ancestral Gumbel-Top-k Sampling for Sampling Without Replacement

Wouter Kool, Herke van Hoof, Max Welling, 2020, J. Mach. Learn. Res..

Estimating Gradients for Discrete Random Variables by Sampling without Replacement pdf

Max Welling, Wouter Kool, Herke van Hoof, 2020, ICLR.

A Survey of Exploration Methods in Reinforcement Learning pdf

Doina Precup, Herke van Hoof, Maziar Gomrokchi, 2021, ArXiv.

Probabilistic Segmentation and Targeted Exploration of Objects in Cluttered Environments

Oliver Kroemer, Jan Peters, Herke van Hoof, 2014, IEEE Transactions on Robotics.

Optimizing Adaptive Notifications in Mobile Health Interventions Systems: Reinforcement Learning from a Data-driven Behavioral Simulator

Ben J. A. Kröse, Herke van Hoof, Chao Zhang, 2021, Journal of Medical Systems.

A Performance-Based Start State Curriculum Framework for Reinforcement Learning

Herke van Hoof, Felix Schmitt, Jan Wöhlke, 2020, AAMAS.

Non-parametric Policy Search with Limited Information Loss

Jan Peters, Herke van Hoof, Gerhard Neumann, 2017, J. Mach. Learn. Res..

Generalized exploration in policy search

Jan Peters, Herke van Hoof, Daniel Tanneberg, 2017, Machine Learning.

Learning to predict phases of manipulation tasks as hidden states

Oliver Kroemer, Jan Peters, Herke van Hoof, 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

Attention, Learn to Solve Routing Problems!

Max Welling, Wouter Kool, Herke van Hoof, 2018, ICLR.

Deep Policy Dynamic Programming for Vehicle Routing Problems pdf

Max Welling, Wouter Kool, Herke van Hoof, 2021, CPAIOR.

Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables

Qi Wang, Herke van Hoof, H. V. Hoof, 2020, ICML.

MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning pdf

Herke van Hoof, Frans A. Oliehoek, Elise van der Pol, 2020, NeurIPS.

Deep Generative Modeling of LiDAR Data

Joelle Pineau, Herke van Hoof, Aaron C. Courville, 2018, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Stable reinforcement learning with autoencoders for tactile and visual data

Jan Peters, Maximilian Karl, Patrick van der Smagt, 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems

M. de Rijke, H. V. Hoof, Harrie Oosterhuis, 2020, RecSys.

Policy Search on Aggregated State Space for Active Sampling

Gregory Dudek, Sandeep Manjanna, Herke Van Hoof, 2018, ISER.

Uncoupled Learning of Differential Stackelberg Equilibria with Commitments

F. Oliehoek, H. V. Hoof, R. Loftin, 2023, ArXiv.

BanditSum: Extractive Summarization as a Contextual Bandit

Jackie Chi Kit Cheung, Yue Dong, Herke van Hoof, 2018, EMNLP.

Robust Multi-Agent Reinforcement Learning with Social Empowerment for Coordination and Communication pdf

T. van der Heiden, C. Salge, E. Gavves, 2020, ArXiv.

Policy Search with High-Dimensional Context Variables

Masashi Sugiyama, Voot Tangkaratt, Jan Peters, 2016, AAAI.

Attention Solves Your TSP, Approximately

Wouter Kool, Herke van Hoof, Max Welling, 2018 .

Reliably Re-Acting to Partner's Actions with the Social Intrinsic Motivation of Transfer Empowerment

Christoph Salge, H. V. Hoof, E. Gavves, 2022, The 2022 Conference on Artificial Life.

An Inference-Based Policy Gradient Method for Learning Options

Joelle Pineau, Herke van Hoof, Matthew Smith, 2018, ICML.

Exposure-Aware Recommendation using Contextual Bandits

H. V. Hoof, B. Mobasher, M. Mansoury, 2022, ArXiv.

Calculus on MDPs: Potential Shaping as a Gradient

H. V. Hoof, A. Gleave, Erik Jenner, 2022, ArXiv.

M ULTI -A GENT MDP H OMOMORPHIC N ETWORKS

Elise van der Pol, H. V. Hoof, Homomorphic Networks, 2022 .

Hierarchies of Planning and Reinforcement Learning for Robot Navigation

H. V. Hoof, Felix Schmitt, J. Wöhlke, 2021, 2021 IEEE International Conference on Robotics and Automation (ICRA).

UvA-DARE (Digital Academic Attention, learn to solve routing problems! A TTENTION , L EARN TO S OLVE R OUTING P ROBLEMS !

H. V. Hoof, W. Kool, Solve Routing Problems, 2019 .

UvA-DARE ( Digital Academic Repository ) Attention , learn to solve routing problems !

H. V. Hoof, W. Kool, Solve Routing Problems, 2019 .

Stochastic Activation Actor Critic Methods

Max Welling, Herke van Hoof, Wenling Shang, 2019, ECML/PKDD.

Deep Coherent Exploration for Continuous Control

Herke Van Hoof, Yijie Zhang, H. V. Hoof, 2021, ICML.

Reusable Options through Gradient-based Meta Learning

H. V. Hoof, David Kuric, 2022, Trans. Mach. Learn. Res..

Eager and Memory-Based Non-Parametric Stochastic Search Methods for Learning Control

Herke van Hoof, Abbas Abdolmaleki, David Meger, 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

Fast and Data Efficient Reinforcement Learning from Pixels via Non-Parametric Value Approximation

A. Blair, H. V. Hoof, Alex Long, 2022, AAAI.

Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes

H. V. Hoof, Tim Bakker, Max Welling, 2023, ECML/PKDD.

Social navigation with human empowerment driven reinforcement learning

H. V. Hoof, N. Nagaraja, T. V. D. Heiden, 2020, arXiv.org.

Reinforcement Learning with Non-uniform State Representations for Adaptive Search

Gregory Dudek, Herke van Hoof, Sandeep Manjanna, 2018, 2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR).

Logic-based AI for Interpretable Board Game Winner Prediction with Tsetlin Machine

H. V. Hoof, Ole-Christoffer Granmo, Charul Giri, 2022, 2022 International Joint Conference on Neural Networks (IJCNN).

Hierarchies of Planning and Reinforcement Learning for Robot Navigation

Herke van Hoof, Jan Wöhlke, Felix Schmitt, 2021, 2021 IEEE International Conference on Robotics and Automation (ICRA).

Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models pdf

Herke van Hoof, Qi Wang, H. V. Hoof, 2021, ICML.

MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning

Elise van der Pol, Daniel E. Worrall, M. Welling, 2020, NeurIPS.

Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems

M. de Rijke, Maarten de Rijke, Herke van Hoof, 2020, RecSys.

H. V. Hoof, Harrie Oosterhuis, Masoud Mansoury, 2024, 2404.18640.

UvA-DARE (Digital Academic Repository) Social Navigation with Human Empowerment Driven Deep Reinforcement Learning

H. V. Hoof, Florian Mirus, T. V. D. Heiden, 2022 .

UvA-DARE (Digital Academic Repository) Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes

H. V. Hoof, Tim Bakker, Max Welling, 2022 .

Learning Expressive Meta-Representations with Mixture of Expert Neural Processes

H. V. Hoof, Q. Wang, 2022, NeurIPS.