Nicolas Heess
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
72
Venues
11
Active years
2009–2025
Best venue rank
A*
Where they publish
Papers
72 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | AISTATS | A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning. | Khimya Khetarpal, Zhaohan Daniel Guo, Bernardo vila Pires, Yunhao Tang, Clare Lyle, Mark Rowland, Nicolas Heess, Diana L. Borsa, Arthur Guez, Will Dabney |
| 2025 | ICLR | Re-evaluating Open-ended Evaluation of Large Language Models. | Siqi Liu, Ian Gemp, Luke Marris, Georgios Piliouras, Nicolas Heess, Marc Lanctot |
| 2025 | ICLR | Learning from negative feedback, or positive feedback or both. | Abbas Abdolmaleki, Bilal Piot, Bobak Shahriari, Jost Tobias Springenberg, Tim Hertweck, Michael Bloesch, Rishabh Joshi, Thomas Lampe, Junhyuk Oh, Nicolas Heess, Jonas Buchli, Martin A. Riedmiller |
| 2025 | ICML | Learning-Order Autoregressive Models with Application to Molecular Graph Generation. | Zhe Wang, Jiaxin Shi, Nicolas Heess, Arthur Gretton, Michalis K. Titsias |
| 2025 | ICML | EvoControl: Multi-Frequency Bi-Level Control for High-Frequency Continuous Control. | Samuel Holt, Todor Davchev, Dhruva Tirumala, Ben Moran, Atil Iscen, Antoine Laurens, Yixin Lin, Erik Frey, Markus Wulfmeier, Francesco Romano, Nicolas Heess |
| 2025 | ICRA | DemoStart: Demonstration-Led Auto-Curriculum Applied to Sim-to-Real with Multi-Fingered Robots. | Maria Bauz, Jose Enriaue Chen, Valentin Dalibard, Nimrod Gileadi, Roland Hafner, Murilo F. Martins, Joss Moore, Rugile Pevceviciute, Antoine Laurens, Dushyant Rao, Martina Zambelli, Martin A. Riedmiller, Jon Scholz, Konstantinos Bousmalis, Francesco Nori, Nicolas Heess |
| 2025 | IROS | Exploiting Policy Idling for Dexterous Manipulation. | Annie S. Chen, Philemon Brakel, Antonia Bronars, Annie Xie, Sandy Han Huang, Oliver Groth, Maria Bauz, Markus Wulfmeier, Nicolas Heess, Dushyant Rao |
| 2024 | ACL | The Probabilities Also Matter: A More Faithful Metric for Faithfulness of Free-Text Explanations in Large Language Models. | Noah Y. Siegel, Oana-Maria Camburu, Nicolas Heess, Mara Prez-Ortiz |
| 2024 | CoRL | Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning. | Dhruva Tirumala, Markus Wulfmeier, Ben Moran, Sandy H. Huang, Jan Humplik, Guy Lever, Tuomas Haarnoja, Leonard Hasenclever, Arunkumar Byravan, Nathan Batchelor, Neil Sreendra, Kushal Patel, Marlon Gwira, Francesco Nori, Martin A. Riedmiller, Nicolas Heess |
| 2024 | ICLR | NfgTransformer: Equivariant Representation Learning for Normal-form Games. | Siqi Liu, Luke Marris, Georgios Piliouras, Ian Gemp, Nicolas Heess |
| 2024 | ICLR | Replay across Experiments: A Natural Extension of Off-Policy RL. | Dhruva Tirumala, Thomas Lampe, Jos Enrique Chen, Tuomas Haarnoja, Sandy H. Huang, Guy Lever, Ben Moran, Tim Hertweck, Leonard Hasenclever, Martin A. Riedmiller, Nicolas Heess, Markus Wulfmeier |
| 2024 | ICML | Genie: Generative Interactive Environments. | Jake Bruce, Michael D. Dennis, Ashley Edwards, Jack Parker-Holder, Yuge Shi, Edward Hughes, Matthew Lai, Aditi Mavalankar, Richie Steigerwald, Chris Apps, Yusuf Aytar, Sarah Bechtle, Feryal M. P. Behbahani, Stephanie C. Y. Chan, Nicolas Heess, Lucy Gonzalez, Simon Osindero, Sherjil Ozair, Scott E. Reed, Jingwei Zhang, Konrad Zolna, Jeff Clune, Nando de Freitas, Satinder Singh, Tim Rocktschel |
| 2024 | ICML | PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs. | Soroush Nasiriany, Fei Xia, Wenhao Yu, Ted Xiao, Jacky Liang, Ishita Dasgupta, Annie Xie, Danny Driess, Ayzaan Wahid, Zhuo Xu, Quan Vuong, Tingnan Zhang, Tsang-Wei Edward Lee, Kuang-Huei Lee, Peng Xu, Sean Kirmani, Yuke Zhu, Andy Zeng, Karol Hausman, Nicolas Heess, Chelsea Finn, Sergey Levine, Brian Ichter |
| 2024 | ICML | Offline Actor-Critic Reinforcement Learning Scales to Large Models. | Jost Tobias Springenberg, Abbas Abdolmaleki, Jingwei Zhang, Oliver Groth, Michael Bloesch, Thomas Lampe, Philemon Brakel, Sarah Bechtle, Steven Kapturowski, Roland Hafner, Nicolas Heess, Martin A. Riedmiller |
| 2024 | ICRA | Mastering Stacking of Diverse Shapes with Large-Scale Iterative Reinforcement Learning on Real Robots. | Thomas Lampe, Abbas Abdolmaleki, Sarah Bechtle, Sandy H. Huang, Jost Tobias Springenberg, Michael Bloesch, Oliver Groth, Roland Hafner, Tim Hertweck, Michael Neunert, Markus Wulfmeier, Jingwei Zhang, Francesco Nori, Nicolas Heess, Martin A. Riedmiller |
| 2024 | ICRA | Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration. | Abby O'Neill, Abdul Rehman, Abhiram Maddukuri, Abhishek Gupta, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, Albert Tung, Alex Bewley, Alexander Herzog, Alex Irpan, Alexander Khazatsky, Anant Rai, Anchit Gupta, Andrew E. Wang, Anikait Singh, Animesh Garg, Aniruddha Kembhavi, Annie Xie, Anthony Brohan, Antonin Raffin, Archit Sharma, Arefeh Yavary, Arhan Jain, Ashwin Balakrishna, Ayzaan Wahid, Ben Burgess-Limerick, Beomjoon Kim, Bernhard Schlkopf, Blake Wulfe, Brian Ichter, Cewu Lu, Charles Xu, Charlotte Le, Chelsea Finn, Chen Wang, Chenfeng Xu, Cheng Chi, Chenguang Huang, Christine Chan, Christopher Agia, Chuer Pan, Chuyuan Fu, Coline Devin, Danfei Xu, Daniel Morton, Danny Driess, Daphne Chen, Deepak Pathak, Dhruv Shah, Dieter Bchler, Dinesh Jayaraman, Dmitry Kalashnikov, Dorsa Sadigh, Edward Johns, Ethan Paul Foster, Fangchen Liu, Federico Ceola, Fei Xia, Feiyu Zhao, Freek Stulp, Gaoyue Zhou, Gaurav S. Sukhatme, Gautam Salhotra, Ge Yan, Gilbert Feng, Giulio Schiavi, Glen Berseth, Gregory Kahn, Guanzhi Wang, Hao Su, Haoshu Fang, Haochen Shi, Henghui Bao, Heni Ben Amor, Henrik I. Christensen, Hiroki Furuta, Homer Walke, Hongjie Fang, Huy Ha, Igor Mordatch, Ilija Radosavovic, Isabel Leal, Jacky Liang, Jad Abou-Chakra, Jaehyung Kim, Jaimyn Drake, Jan Peters, Jan Schneider, Jasmine Hsu, Jeannette Bohg, Jeffrey T. Bingham, Jeffrey Wu, Jensen Gao, Jiaheng Hu, Jiajun Wu, Jialin Wu, Jiankai Sun, Jianlan Luo, Jiayuan Gu, Jie Tan, Jihoon Oh, Jimmy Wu, Jingpei Lu, Jingyun Yang, Jitendra Malik, Joo Silvrio, Joey Hejna, Jonathan Booher, Jonathan Tompson, Jonathan Yang, Jordi Salvador, Joseph J. Lim, Junhyek Han, Kaiyuan Wang, Kanishka Rao, Karl Pertsch, Karol Hausman, Keegan Go, Keerthana Gopalakrishnan, Ken Goldberg, Kendra Byrne, Kenneth Oslund, Kento Kawaharazuka, Kevin Black, Kevin Lin, Kevin Zhang, Kiana Ehsani, Kiran Lekkala, Kirsty Ellis, Krishan Rana, Krishnan Srinivasan, Kuan Fang, Kunal Pratap Singh, Kuo-Hao Zeng, Kyle Hatch, Kyle Hsu, Laurent Itti, Lawrence Yunliang Chen, Lerrel Pinto, Li Fei-Fei, Liam Tan, Linxi Jim Fan, Lionel Ott, Lisa Lee, Luca Weihs, Magnum Chen, Marion Lepert, Marius Memmel, Masayoshi Tomizuka, Masha Itkina, Mateo Guaman Castro, Max Spero, Maximilian Du, Michael Ahn, Michael C. Yip, Mingtong Zhang, Mingyu Ding, Minho Heo, Mohan Kumar Srirama, Mohit Sharma, Moo Jin Kim, Naoaki Kanazawa, Nicklas Hansen, Nicolas Heess, Nikhil J. Joshi, Niko Snderhauf, Ning Liu, Norman Di Palo, Nur Muhammad (Mahi) Shafiullah, Oier Mees, Oliver Kroemer, Osbert Bastani, Pannag R. Sanketi, Patrick Tree Miller, Patrick Yin, Paul Wohlhart, Peng Xu, Peter David Fagan, Peter Mitrano, Pierre Sermanet, Pieter Abbeel, Priya Sundaresan, Qiuyu Chen, Quan Vuong, Rafael Rafailov, Ran Tian, Ria Doshi, Roberto Martn-Martn, Rohan Baijal, Rosario Scalise, Rose Hendrix, Roy Lin, Runjia Qian, Ruohan Zhang, Russell Mendonca, Rutav Shah, Ryan Hoque, Ryan Julian, Samuel Bustamante-Gomez, Sean Kirmani, Sergey Levine, Shan Lin, Sherry Moore, Shikhar Bahl, Shivin Dass, Shubham D. Sonawani, Shuran Song, Sichun Xu, Siddhant Haldar, Siddharth Karamcheti, Simeon Adebola, Simon Guist, Soroush Nasiriany, Stefan Schaal, Stefan Welker, Stephen Tian, Subramanian Ramamoorthy, Sudeep Dasari, Suneel Belkhale, Sungjae Park, Suraj Nair, Suvir Mirchandani, Takayuki Osa, Tanmay Gupta, Tatsuya Harada, Tatsuya Matsushima, Ted Xiao, Thomas Kollar, Tianhe Yu, Tianli Ding, Todor Davchev, Tony Z. Zhao, Travis Armstrong, Trevor Darrell, Trinity Chung, Vidhi Jain, Vincent Vanhoucke, Wei Zhan, Wenxuan Zhou, Wolfram Burgard, Xi Chen, Xiaolong Wang, Xinghao Zhu, Xinyang Geng, Xiyuan Liu, Liangwei Xu, Xuanlin Li, Yao Lu, Yecheng Jason Ma, Yejin Kim, Yevgen Chebotar, Yifan Zhou, Yifeng Zhu, Yilin Wu, Ying Xu, Yixuan Wang, Yonatan Bisk, Yoonyoung Cho, Youngwoon Lee, Yuchen Cui, Yue Cao, Yueh-Hua Wu, Yujin Tang, Yuke Zhu, Yunchu Zhang, Yunfan Jiang, Yunshuang Li, Yunzhu Li, Yusuke Iwasawa, Yutaka Matsuo, Zehan Ma, Zhuo Xu, Zichen Jeff Cui, Zichen Zhang, Zipeng Lin |
| 2024 | IROS | The Design of the Barkour Benchmark for Robot Agility. | Wenhao Yu, Ken Caluwaerts, Atil Iscen, J. Chase Kew, Tingnan Zhang, Daniel Freeman, Lisa Lee, Stefano Saliceti, Vincent Zhuang, Nathan Batchelor, Steven Bohez, Federico Casarini, Jos Enrique Chen, Erwin Coumans, Adil Dostmohamed, Gabriel Dulac-Arnold, Alejandro Escontrela, Erik Frey, Roland Hafner, Deepali Jain, Bauyrjan Jyenis, Yuheng Kuang, Tsang-Wei Edward Lee, Ofir Nachum, Ken Oslund, Francesco Romano, Fereshteh Sadeghi, Baruch Tabanpour, Daniel Zheng, Michael Neunert, Raia Hadsell, Nicolas Heess, Francesco Nori, Jeff Seto, Carolina Parada, Vikas Sindhwani, Vincent Vanhoucke, Jie Tan, Kuang-Huei Lee |
| 2023 | AISTATS | Representation Learning in Deep RL via Discrete Information Bottleneck. | Riashat Islam, Hongyu Zang, Manan Tomar, Aniket Didolkar, Md Mofijul Islam, Samin Yeasar Arnob, Tariq Iqbal, Xin Li, Anirudh Goyal, Nicolas Heess, Alex Lamb |
| 2023 | CoRL | Language to Rewards for Robotic Skill Synthesis. | Wenhao Yu, Nimrod Gileadi, Chuyuan Fu, Sean Kirmani, Kuang-Huei Lee, Montserrat Gonzalez Arenas, Hao-Tien Lewis Chiang, Tom Erez, Leonard Hasenclever, Jan Humplik, Brian Ichter, Ted Xiao, Peng Xu, Andy Zeng, Tingnan Zhang, Nicolas Heess, Dorsa Sadigh, Jie Tan, Yuval Tassa, Fei Xia |
| 2023 | ICLR | Stateful Active Facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning. | Dianbo Liu, Vedant Shah, Oussama Boussif, Cristian Meo, Anirudh Goyal, Tianmin Shu, Michael Curtis Mozer, Nicolas Heess, Yoshua Bengio |
| 2023 | ICLR | Lossless Adaptation of Pretrained Vision Models For Robotic Manipulation. | Mohit Sharma, Claudio Fantacci, Yuxiang Zhou, Skanda Koppula, Nicolas Heess, Jon Scholz, Yusuf Aytar |
| 2023 | ICRA | NeRF2Real: Sim2real Transfer of Vision-guided Bipedal Motion Skills using Neural Radiance Fields. | Arunkumar Byravan, Jan Humplik, Leonard Hasenclever, Arthur Brussee, Francesco Nori, Tuomas Haarnoja, Ben Moran, Steven Bohez, Fereshteh Sadeghi, Bojan Vujatovic, Nicolas Heess |
| 2022 | ICLR | COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation. | Jongmin Lee, Cosmin Paduraru, Daniel J. Mankowitz, Nicolas Heess, Doina Precup, Kee-Eung Kim, Arthur Guez |
| 2022 | ICLR | Evaluating Model-Based Planning and Planner Amortization for Continuous Control. | Arunkumar Byravan, Leonard Hasenclever, Piotr Trochim, Mehdi Mirza, Alessandro Davide Ialongo, Yuval Tassa, Jost Tobias Springenberg, Abbas Abdolmaleki, Nicolas Heess, Josh Merel, Martin A. Riedmiller |
| 2022 | ICLR | NeuPL: Neural Population Learning. | Siqi Liu, Luke Marris, Daniel Hennes, Josh Merel, Nicolas Heess, Thore Graepel |
| 2022 | ICLR | Learning transferable motor skills with hierarchical latent mixture policies. | Dushyant Rao, Fereshteh Sadeghi, Leonard Hasenclever, Markus Wulfmeier, Martina Zambelli, Giulia Vezzani, Dhruva Tirumala, Yusuf Aytar, Josh Merel, Nicolas Heess, Raia Hadsell |
| 2022 | ICML | Retrieval-Augmented Reinforcement Learning. | Anirudh Goyal, Abram L. Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke, Adri Puigdomnech Badia, Arthur Guez, Mehdi Mirza, Peter Conway Humphreys, Ksenia Konyushkova, Michal Valko, Simon Osindero, Timothy P. Lillicrap, Nicolas Heess, Charles Blundell |
| 2022 | ICML | Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games. | Siqi Liu, Marc Lanctot, Luke Marris, Nicolas Heess |
| 2022 | IROS | Learning Coordinated Terrain-Adaptive Locomotion by Imitating a Centroidal Dynamics Planner. | Philmon Brakel, Steven Bohez, Leonard Hasenclever, Nicolas Heess, Konstantinos Bousmalis |
| 2022 | ICRA | Offline Meta-Reinforcement Learning for Industrial Insertion. | Tony Z. Zhao, Jianlan Luo, Oleg Sushkov, Rugile Pevceviciute, Nicolas Heess, Jon Scholz, Stefan Schaal, Sergey Levine |
| 2021 | CoRL | Towards Real Robot Learning in the Wild: A Case Study in Bipedal Locomotion. | Michael Bloesch, Jan Humplik, Viorica Patraucean, Roland Hafner, Tuomas Haarnoja, Arunkumar Byravan, Noah Yamamoto Siegel, Saran Tunyasuvunakool, Federico Casarini, Nathan Batchelor, Francesco Romano, Stefano Saliceti, Martin A. Riedmiller, S. M. Ali Eslami, Nicolas Heess |
| 2021 | CoRL | A Constrained Multi-Objective Reinforcement Learning Framework. | Sandy H. Huang, Abbas Abdolmaleki, Giulia Vezzani, Philemon Brakel, Daniel J. Mankowitz, Michael Neunert, Steven Bohez, Yuval Tassa, Nicolas Heess, Martin A. Riedmiller, Raia Hadsell |
| 2021 | CoRL | Collect & Infer - a fresh look at data-efficient Reinforcement Learning. | Martin A. Riedmiller, Jost Tobias Springenberg, Roland Hafner, Nicolas Heess |
| 2021 | ICML | Counterfactual Credit Assignment in Model-Free Reinforcement Learning. | Thomas Mesnard, Theophane Weber, Fabio Viola, Shantanu Thakoor, Alaa Saade, Anna Harutyunyan, Will Dabney, Thomas S. Stepleton, Nicolas Heess, Arthur Guez, Eric Moulines, Marcus Hutter, Lars Buesing, Rmi Munos |
| 2021 | ICML | Data-efficient Hindsight Off-policy Option Learning. | Markus Wulfmeier, Dushyant Rao, Roland Hafner, Thomas Lampe, Abbas Abdolmaleki, Tim Hertweck, Michael Neunert, Dhruva Tirumala, Noah Y. Siegel, Nicolas Heess, Martin A. Riedmiller |
| 2020 | AISTATS | Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions. | Lars Buesing, Nicolas Heess, Theophane Weber |
| 2020 | CoRL | Towards General and Autonomous Learning of Core Skills: A Case Study in Locomotion. | Roland Hafner, Tim Hertweck, Philipp Klppner, Michael Bloesch, Michael Neunert, Markus Wulfmeier, Saran Tunyasuvunakool, Nicolas Heess, Martin A. Riedmiller |
| 2020 | CoRL | Learning Dexterous Manipulation from Suboptimal Experts. | Rae Jeong, Jost Tobias Springenberg, Jackie Kay, Daniel Zheng, Alexandre Galashov, Nicolas Heess, Francesco Nori |
| 2020 | ICLR | A Generalized Training Approach for Multiagent Learning. | Paul Muller, Shayegan Omidshafiei, Mark Rowland, Karl Tuyls, Julien Prolat, Siqi Liu, Daniel Hennes, Luke Marris, Marc Lanctot, Edward Hughes, Zhe Wang, Guy Lever, Nicolas Heess, Thore Graepel, Rmi Munos |
| 2020 | ICLR | Keep Doing What Worked: Behavior Modelling Priors for Offline Reinforcement Learning. | Noah Y. Siegel, Jost Tobias Springenberg, Felix Berkenkamp, Abbas Abdolmaleki, Michael Neunert, Thomas Lampe, Roland Hafner, Nicolas Heess, Martin A. Riedmiller |
| 2020 | ICLR | V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control. | H. Francis Song, Abbas Abdolmaleki, Jost Tobias Springenberg, Aidan Clark, Hubert Soyer, Jack W. Rae, Seb Noury, Arun Ahuja, Siqi Liu, Dhruva Tirumala, Nicolas Heess, Dan Belov, Martin A. Riedmiller, Matthew M. Botvinick |
| 2020 | ICML | A distributional view on multi-objective policy optimization. | Abbas Abdolmaleki, Sandy H. Huang, Leonard Hasenclever, Michael Neunert, H. Francis Song, Martina Zambelli, Murilo F. Martins, Nicolas Heess, Raia Hadsell, Martin A. Riedmiller |
| 2020 | ICML | CoMic: Complementary Task Learning & Mimicry for Reusable Skills. | Leonard Hasenclever, Fabio Pardo, Raia Hadsell, Nicolas Heess, Josh Merel |
| 2020 | ICML | Stabilizing Transformers for Reinforcement Learning. | Emilio Parisotto, H. Francis Song, Jack W. Rae, Razvan Pascanu, aglar Glehre, Siddhant M. Jayakumar, Max Jaderberg, Raphal Lopez Kaufman, Aidan Clark, Seb Noury, Matthew M. Botvinick, Nicolas Heess, Raia Hadsell |
| 2019 | AISTATS | The Termination Critic. | Anna Harutyunyan, Will Dabney, Diana Borsa, Nicolas Heess, Rmi Munos, Doina Precup |
| 2019 | AISTATS | Credit Assignment Techniques in Stochastic Computation Graphs. | Thophane Weber, Nicolas Heess, Lars Buesing, David Silver |
| 2019 | CoRL | Imagined Value Gradients: Model-Based Policy Optimization with Tranferable Latent Dynamics Models. | Arunkumar Byravan, Jost Tobias Springenberg, Abbas Abdolmaleki, Roland Hafner, Michael Neunert, Thomas Lampe, Noah Y. Siegel, Nicolas Heess, Martin A. Riedmiller |
| 2019 | CoRL | Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics. | Michael Neunert, Abbas Abdolmaleki, Markus Wulfmeier, Thomas Lampe, Jost Tobias Springenberg, Roland Hafner, Francesco Romano, Jonas Buchli, Nicolas Heess, Martin A. Riedmiller |
| 2019 | ICLR | Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search. | Lars Buesing, Theophane Weber, Yori Zwols, Nicolas Heess, Sbastien Racanire, Arthur Guez, Jean-Baptiste Lespiau |
| 2019 | ICLR | Information asymmetry in KL-regularized RL. | Alexandre Galashov, Siddhant M. Jayakumar, Leonard Hasenclever, Dhruva Tirumala, Jonathan Schwarz, Guillaume Desjardins, Wojciech M. Czarnecki, Yee Whye Teh, Razvan Pascanu, Nicolas Heess |
| 2019 | ICLR | Emergent Coordination Through Competition. | Siqi Liu, Guy Lever, Josh Merel, Saran Tunyasuvunakool, Nicolas Heess, Thore Graepel |
| 2019 | ICLR | Hierarchical Visuomotor Control of Humanoids. | Josh Merel, Arun Ahuja, Vu Pham, Saran Tunyasuvunakool, Siqi Liu, Dhruva Tirumala, Nicolas Heess, Greg Wayne |
| 2019 | ICLR | Neural Probabilistic Motor Primitives for Humanoid Control. | Josh Merel, Leonard Hasenclever, Alexandre Galashov, Arun Ahuja, Vu Pham, Greg Wayne, Yee Whye Teh, Nicolas Heess |
| 2019 | ICLR | Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures. | Jonathan Uesato, Ananya Kumar, Csaba Szepesvri, Tom Erez, Avraham Ruderman, Keith Anderson, Krishnamurthy (Dj) Dvijotham, Nicolas Heess, Pushmeet Kohli |
| 2019 | ICML | Composing Entropic Policies using Divergence Correction. | Jonathan J. Hunt, Andr Barreto, Timothy P. Lillicrap, Nicolas Heess |
| 2018 | ICLR | Maximum a Posteriori Policy Optimisation. | Abbas Abdolmaleki, Jost Tobias Springenberg, Yuval Tassa, Rmi Munos, Nicolas Heess, Martin A. Riedmiller |
| 2018 | ICLR | Distributed Distributional Deterministic Policy Gradients. | Gabriel Barth-Maron, Matthew W. Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva TB, Alistair Muldal, Nicolas Heess, Timothy P. Lillicrap |
| 2018 | ICLR | Learning an Embedding Space for Transferable Robot Skills. | Karol Hausman, Jost Tobias Springenberg, Ziyu Wang, Nicolas Heess, Martin A. Riedmiller |
| 2018 | ICML | Mix & Match Agent Curricula for Reinforcement Learning. | Wojciech Marian Czarnecki, Siddhant M. Jayakumar, Max Jaderberg, Leonard Hasenclever, Yee Whye Teh, Nicolas Heess, Simon Osindero, Razvan Pascanu |
| 2018 | ICML | Learning by Playing Solving Sparse Reward Tasks from Scratch. | Martin A. Riedmiller, Roland Hafner, Thomas Lampe, Michael Neunert, Jonas Degrave, Tom Van de Wiele, Vlad Mnih, Nicolas Heess, Jost Tobias Springenberg |
| 2018 | ICML | Graph Networks as Learnable Physics Engines for Inference and Control. | Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin A. Riedmiller, Raia Hadsell, Peter W. Battaglia |
| 2017 | CoRL | Sim-to-Real Robot Learning from Pixels with Progressive Nets. | Andrei A. Rusu, Matej Vecerk, Thomas Rothrl, Nicolas Heess, Razvan Pascanu, Raia Hadsell |
| 2017 | ICLR | Sample Efficient Actor-Critic with Experience Replay. | Ziyu Wang, Victor Bapst, Nicolas Heess, Volodymyr Mnih, Rmi Munos, Koray Kavukcuoglu, Nando de Freitas |
| 2017 | ICLR | Metacontrol for Adaptive Imagination-Based Optimization. | Jessica B. Hamrick, Andrew J. Ballard, Razvan Pascanu, Oriol Vinyals, Nicolas Heess, Peter W. Battaglia |
| 2017 | ICLR | Particle Value Functions. | Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh |
| 2017 | ICML | FeUdal Networks for Hierarchical Reinforcement Learning. | Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, Koray Kavukcuoglu |
| 2015 | UAI | Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages. | Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess, S. M. Ali Eslami, Balaji Lakshminarayanan, Dino Sejdinovic, Zoltn Szab |
| 2014 | AISTATS | Visual Boundary Prediction: A Deep Neural Prediction Network and Quality Dissection. | Jyri J. Kivinen, Christopher K. I. Williams, Nicolas Heess |
| 2014 | ICML | Deterministic Policy Gradient Algorithms. | David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, Martin A. Riedmiller |
| 2012 | CVPR | The Shape Boltzmann Machine: A strong model of object shape. | S. M. Ali Eslami, Nicolas Heess, John M. Winn |
| 2011 | ICANN | Weakly Supervised Learning of Foreground-Background Segmentation Using Masked RBMs. | Nicolas Heess, Nicolas Le Roux, John M. Winn |
| 2009 | BMVC | Learning Generative Texture Models with extended Fields-of-Experts. | Nicolas Heess, Christopher K. I. Williams, Geoffrey E. Hinton |