Robert Dadashi

35 papers A* 12A 1Journal 20Unranked 2

Year	Rank	Type	Title / Venue / Authors
2024	J	`jnl`	BOND: Aligning LLMs with Best-of-N Distillation. CoRR Pier Giuseppe Sessa, Robert Dadashi, Léonard Hussenot, Johan Ferret, Nino Vieillard, Alexandre Ramé, Bobak Shahriari, Sarah Perrin, Abe Friesen, Geoffrey Cideron, Sertan Girgin, Piotr Stanczyk, Andrea Michi, Danila Sinopalnikov, Sabela Ramos, Amélie Héliou, Aliaksei Severyn, Matt Hoffman, Nikola Momchev, Olivier Bachem
2024	J	`jnl`	RecurrentGemma: Moving Past Transformers for Efficient Open Language Models. CoRR Aleksandar Botev, Soham De, Samuel L. Smith, Anushan Fernando, George-Cristian Muraru, Ruba Haroun, Leonard Berrada, Razvan Pascanu, Pier Giuseppe Sessa, Robert Dadashi, Léonard Hussenot, Johan Ferret, Sertan Girgin, Olivier Bachem, Alek Andreev, Kathleen Kenealy, Thomas Mesnard, Cassidy Hardin, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, Pouya Tafti, Armand Joulin, Noah Fiedel, Evan Senter, Yutian Chen, Srivatsan Srinivasan, Guillaume Desjardins, David Budden, Arnaud Doucet, Sharad Vikram, Adam Paszke, Trevor Gale, Sebastian Borgeaud, Charlie Chen, Andy Brock, Antonia Paterson, Jenny Brennan, Meg Risdal, Raj Gundluru, Nesh Devanathan, Paul Mooney, Nilay Chauhan, Phil Culliton, Luiz Gustavo Martins, Elisa Bandy, David Huntsperger, Glenn Cameron, Arthur Zucker, Tris Warkentin, Ludovic Peran, Minh Giang, Zoubin Ghahramani, Clément Farabet, Koray Kavukcuoglu, Demis Hassabis, Raia Hadsell, Yee Whye Teh, Nando de Frietas
2024	A*	`conf`	WARM: On the Benefits of Weight Averaged Reward Models. ICML Alexandre Ramé, Nino Vieillard, Léonard Hussenot, Robert Dadashi, Geoffrey Cideron, Olivier Bachem, Johan Ferret
2024	J	`jnl`	WARM: On the Benefits of Weight Averaged Reward Models. CoRR Alexandre Ramé, Nino Vieillard, Léonard Hussenot, Robert Dadashi, Geoffrey Cideron, Olivier Bachem, Johan Ferret
2024	J	`jnl`	WARP: On the Benefits of Weight Averaged Rewarded Policies. CoRR Alexandre Ramé, Johan Ferret, Nino Vieillard, Robert Dadashi, Léonard Hussenot, Pierre-Louis Cedoz, Pier Giuseppe Sessa, Sertan Girgin, Arthur Douillard, Olivier Bachem
2023	—	`conf`	Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback. ACL (1) Paul Roit, Johan Ferret, Lior Shani, Roee Aharoni, Geoffrey Cideron, Robert Dadashi, Matthieu Geist, Sertan Girgin, Léonard Hussenot, Orgad Keller, Nikola Momchev, Sabela Ramos Garea, Piotr Stanczyk, Nino Vieillard, Olivier Bachem, Gal Elidan, Avinatan Hassidim, Olivier Pietquin, Idan Szpektor
2023	J	`jnl`	Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback. CoRR Paul Roit, Johan Ferret, Lior Shani, Roee Aharoni, Geoffrey Cideron, Robert Dadashi, Matthieu Geist, Sertan Girgin, Léonard Hussenot, Orgad Keller, Nikola Momchev, Sabela Ramos, Piotr Stanczyk, Nino Vieillard, Olivier Bachem, Gal Elidan, Avinatan Hassidim, Olivier Pietquin, Idan Szpektor
2023	J	`jnl`	Get Back Here: Robust Imitation by Return-to-Distribution Planning. CoRR Geoffrey Cideron, Baruch Tabanpour, Sebastian Curi, Sertan Girgin, Léonard Hussenot, Gabriel Dulac-Arnold, Matthieu Geist, Olivier Pietquin, Robert Dadashi
2023	—	`conf`	Offline Reinforcement Learning with On-Policy Q-Function Regularization. ECML/PKDD (4) Laixi Shi, Robert Dadashi, Yuejie Chi, Pablo Samuel Castro, Matthieu Geist
2023	J	`jnl`	Offline Reinforcement Learning with On-Policy Q-Function Regularization. CoRR Laixi Shi, Robert Dadashi, Yuejie Chi, Pablo Samuel Castro, Matthieu Geist
2022	A*	`conf`	Continuous Control with Action Quantization from Demonstrations. ICML Robert Dadashi, Léonard Hussenot, Damien Vincent, Sertan Girgin, Anton Raichuk, Matthieu Geist, Olivier Pietquin
2022	A*	`conf`	Learning Energy Networks with Generalized Fenchel-Young Losses. NeurIPS Mathieu Blondel, Felipe Llinares-López, Robert Dadashi, Léonard Hussenot, Matthieu Geist
2022	J	`jnl`	Learning Energy Networks with Generalized Fenchel-Young Losses. CoRR Mathieu Blondel, Felipe Llinares-López, Robert Dadashi, Léonard Hussenot, Matthieu Geist
2022	A*	`conf`	Offline Reinforcement Learning as Anti-exploration. AAAI Shideh Rezaeifar, Robert Dadashi, Nino Vieillard, Léonard Hussenot, Olivier Bachem, Olivier Pietquin, Matthieu Geist
2021	J	`jnl`	Continuous Control with Action Quantization from Demonstrations. CoRR Robert Dadashi, Léonard Hussenot, Damien Vincent, Sertan Girgin, Anton Raichuk, Matthieu Geist, Olivier Pietquin
2021	A*	`conf`	Hyperparameter Selection for Imitation Learning. ICML Léonard Hussenot, Marcin Andrychowicz, Damien Vincent, Robert Dadashi, Anton Raichuk, Sabela Ramos, Nikola Momchev, Sertan Girgin, Raphaël Marinier, Lukasz Stafiniak, Manu Orsini, Olivier Bachem, Matthieu Geist, Olivier Pietquin
2021	J	`jnl`	Hyperparameter Selection for Imitation Learning. CoRR Léonard Hussenot, Marcin Andrychowicz, Damien Vincent, Robert Dadashi, Anton Raichuk, Lukasz Stafiniak, Sertan Girgin, Raphaël Marinier, Nikola Momchev, Sabela Ramos, Manu Orsini, Olivier Bachem, Matthieu Geist, Olivier Pietquin
2021	J	`jnl`	Offline Reinforcement Learning as Anti-Exploration. CoRR Shideh Rezaeifar, Robert Dadashi, Nino Vieillard, Léonard Hussenot, Olivier Bachem, Olivier Pietquin, Matthieu Geist
2021	A*	`conf`	Offline Reinforcement Learning with Pseudometric Learning. ICML Robert Dadashi, Shideh Rezaeifar, Nino Vieillard, Léonard Hussenot, Olivier Pietquin, Matthieu Geist
2021	J	`jnl`	Offline Reinforcement Learning with Pseudometric Learning. CoRR Robert Dadashi, Shideh Rezaeifar, Nino Vieillard, Léonard Hussenot, Olivier Pietquin, Matthieu Geist
2021	A*	`conf`	Primal Wasserstein Imitation Learning. ICLR Robert Dadashi, Léonard Hussenot, Matthieu Geist, Olivier Pietquin
2021	A	`conf`	Show Me the Way: Intrinsic Motivation from Demonstrations. AAMAS Léonard Hussenot, Robert Dadashi, Matthieu Geist, Olivier Pietquin
2021	A*	`conf`	The Value-Improvement Path: Towards Better Representations for Reinforcement Learning. AAAI Will Dabney, André Barreto, Mark Rowland, Robert Dadashi, John Quan, Marc G. Bellemare, David Silver
2021	A*	`conf`	What Matters for Adversarial Imitation Learning? NeurIPS Manu Orsini, Anton Raichuk, Léonard Hussenot, Damien Vincent, Robert Dadashi, Sertan Girgin, Matthieu Geist, Olivier Bachem, Olivier Pietquin, Marcin Andrychowicz
2021	J	`jnl`	What Matters for Adversarial Imitation Learning? CoRR Manu Orsini, Anton Raichuk, Léonard Hussenot, Damien Vincent, Robert Dadashi, Sertan Girgin, Matthieu Geist, Olivier Bachem, Olivier Pietquin, Marcin Andrychowicz
2020	J	`jnl`	Primal Wasserstein Imitation Learning. CoRR Robert Dadashi, Léonard Hussenot, Matthieu Geist, Olivier Pietquin
2020	J	`jnl`	Show me the Way: Intrinsic Motivation from Demonstrations. CoRR Léonard Hussenot, Robert Dadashi, Matthieu Geist, Olivier Pietquin
2020	J	`jnl`	The Value-Improvement Path: Towards Better Representations for Reinforcement Learning. CoRR Will Dabney, André Barreto, Mark Rowland, Robert Dadashi, John Quan, Marc G. Bellemare, David Silver
2019	A*	`conf`	A Geometric Perspective on Optimal Representations for Reinforcement Learning. NeurIPS Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle
2019	J	`jnl`	A Geometric Perspective on Optimal Representations for Reinforcement Learning. CoRR Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle
2019	A*	`conf`	Statistics and Samples in Distributional Reinforcement Learning. ICML Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare, Will Dabney
2019	J	`jnl`	Statistics and Samples in Distributional Reinforcement Learning. CoRR Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare, Will Dabney
2019	A*	`conf`	The Value Function Polytope in Reinforcement Learning. ICML Robert Dadashi, Marc G. Bellemare, Adrien Ali Taïga, Nicolas Le Roux, Dale Schuurmans
2019	J	`jnl`	The Value Function Polytope in Reinforcement Learning. CoRR Robert Dadashi, Adrien Ali Taïga, Nicolas Le Roux, Dale Schuurmans, Marc G. Bellemare
2018	J	`jnl`	Boosting Model Performance through Differentially Private Model Aggregation. CoRR Sophia Collet, Robert Dadashi, Zahi N. Karam, Chang Liu, Parinaz Sobhani, Yevgeniy Vahlis, Jichao Zhang