John Quan - RankMe

26 papers A* 5Journal 16Unranked 5

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Teaching Models to Teach Themselves: Reasoning at the Edge of Learnability. CoRR Shobhita Sundaram, John Quan, Ariel Kwiatkowski, Kartik Ahuja, Yann Ollivier, Julia Kempe
2025	J	`jnl`	Rethinking Thinking Tokens: LLMs as Improvement Operators. CoRR Lovish Madaan, Aniket Didolkar, Suchin Gururangan, John Quan, Ruan Silva, Ruslan Salakhutdinov, Manzil Zaheer, Sanjeev Arora, Anirudh Goyal
2023	J	`jnl`	Vision-Language Models as a Source of Rewards. CoRR Kate Baumli, Satinder Baveja, Feryal M. P. Behbahani, Harris Chan, Gheorghe Comanici, Sebastian Flennerhag, Maxime Gazeau, Kristian Holsheimer, Dan Horgan, Michael Laskin, Clare Lyle, Hussain Masoom, Kay McKinney, Volodymyr Mnih, Alexander Neitz, Fabio Pardo, Jack Parker-Holder, John Quan, Tim Rocktäschel, Himanshu Sahni, Tom Schaul, Yannick Schroecker, Stephen Spencer, Richie Steigerwald, Luyu Wang, Lei Zhang
2022	A*	`conf`	The Phenomenon of Policy Churn. NeurIPS Tom Schaul, André Barreto, John Quan, Georg Ostrovski
2022	J	`jnl`	The Phenomenon of Policy Churn. CoRR Tom Schaul, André Barreto, John Quan, Georg Ostrovski
2021	J	`jnl`	Podracer architectures for scalable Reinforcement Learning. CoRR Matteo Hessel, Manuel Kroiss, Aidan Clark, Iurii Kemaev, John Quan, Thomas Keck, Fabio Viola, Hado van Hasselt
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
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	J	`jnl`	General non-linear Bellman equations. CoRR Hado van Hasselt, John Quan, Matteo Hessel, Zhongwen Xu, Diana Borsa, André Barreto
2019	—	`conf`	Recurrent Experience Replay in Distributed Reinforcement Learning. ICLR (Poster) Steven Kapturowski, Georg Ostrovski, John Quan, Rémi Munos, Will Dabney
2019	J	`jnl`	Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement. CoRR André Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel J. Mankowitz, Augustin Zídek, Rémi Munos
2019	—	`conf`	Universal Successor Features Approximators. ICLR (Poster) Diana Borsa, André Barreto, John Quan, Daniel J. Mankowitz, Hado van Hasselt, Rémi Munos, David Silver, Tom Schaul
2018	A*	`conf`	Deep Q-learning From Demonstrations. AAAI Todd Hester, Matej Vecerík, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, Andrew Sendonaris, Ian Osband, Gabriel Dulac-Arnold, John P. Agapiou, Joel Z. Leibo, Audrunas Gruslys
2018	—	`conf`	Distributed Prioritized Experience Replay. ICLR (Poster) Dan Horgan, John Quan, David Budden, Gabriel Barth-Maron, Matteo Hessel, Hado van Hasselt, David Silver
2018	J	`jnl`	Distributed Prioritized Experience Replay. CoRR Dan Horgan, John Quan, David Budden, Gabriel Barth-Maron, Matteo Hessel, Hado van Hasselt, David Silver
2018	J	`jnl`	Observe and Look Further: Achieving Consistent Performance on Atari. CoRR Tobias Pohlen, Bilal Piot, Todd Hester, Mohammad Gheshlaghi Azar, Dan Horgan, David Budden, Gabriel Barth-Maron, Hado van Hasselt, John Quan, Mel Vecerík, Matteo Hessel, Rémi Munos, Olivier Pietquin
2018	A*	`conf`	Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement. ICML André Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel J. Mankowitz, Augustin Zídek, Rémi Munos
2018	J	`jnl`	Unicorn: Continual Learning with a Universal, Off-policy Agent. CoRR Daniel J. Mankowitz, Augustin Zídek, André Barreto, Dan Horgan, Matteo Hessel, John Quan, Junhyuk Oh, Hado van Hasselt, David Silver, Tom Schaul
2018	J	`jnl`	Universal Successor Features Approximators. CoRR Diana Borsa, André Barreto, John Quan, Daniel J. Mankowitz, Rémi Munos, Hado van Hasselt, David Silver, Tom Schaul
2017	J	`jnl`	Distral: Robust Multitask Reinforcement Learning. CoRR Yee Whye Teh, Victor Bapst, Wojciech Marian Czarnecki, John Quan, James Kirkpatrick, Raia Hadsell, Nicolas Heess, Razvan Pascanu
2017	—	`conf`	Distral: Robust multitask reinforcement learning. NIPS Yee Whye Teh, Victor Bapst, Wojciech M. Czarnecki, John Quan, James Kirkpatrick, Raia Hadsell, Nicolas Heess, Razvan Pascanu
2017	J	`jnl`	StarCraft II: A New Challenge for Reinforcement Learning. CoRR Oriol Vinyals, Timo Ewalds, Sergey Bartunov, Petko Georgiev, Alexander Sasha Vezhnevets, Michelle Yeo, Alireza Makhzani, Heinrich Küttler, John P. Agapiou, Julian Schrittwieser, John Quan, Stephen Gaffney, Stig Petersen, Karen Simonyan, Tom Schaul, Hado van Hasselt, David Silver, Timothy P. Lillicrap, Kevin Calderone, Paul Keet, Anthony Brunasso, David Lawrence, Anders Ekermo, Jacob Repp, Rodney Tsing
2016	J	`jnl`	Overcoming catastrophic forgetting in neural networks. CoRR James Kirkpatrick, Razvan Pascanu, Neil C. Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, Raia Hadsell
2016	A*	`conf`	Prioritized Experience Replay. ICLR (Poster) Tom Schaul, John Quan, Ioannis Antonoglou, David Silver
2011	J	`jnl`	A Mutualistic Security Service Model: Supporting Large-Scale Virtualized Environments. IT Prof. John Quan, Kara L. Nance, Brian Hay
2011	—	`conf`	Attribute Description Service for Large-Scale Networks. HCI (16) Donald Kline, John Quan