Joint semantic utterance classification and slot filling with recursive neural networks. - Zhaohan Daniel Guo; Gökhan Tür; Wen-tau Yih; Geoffrey Zweig (SLT, 2014)

Concurrent PAC RL. - Zhaohan Guo; Emma Brunskill (AAAI, 2015)

A PAC RL Algorithm for Episodic POMDPs. - Zhaohan Daniel Guo; Shayan Doroudi; Emma Brunskill (AISTATS, 2016)

PAC Continuous State Online Multitask Reinforcement Learning with Identification. - Yao Liu 009; Zhaohan Guo; Emma Brunskill (AAMAS, 2016)

Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation. - Zhaohan Guo; Philip S. Thomas; Emma Brunskill (NIPS, 2017)

Sample Efficient Feature Selection for Factored MDPs. - Zhaohan Daniel Guo; Emma Brunskill (CoRR, 2017)

Using Options for Long-Horizon Off-Policy Evaluation. - Zhaohan Daniel Guo; Philip S. Thomas; Emma Brunskill (CoRR, 2017)

Neural Predictive Belief Representations. - Zhaohan Daniel Guo; Mohammad Gheshlaghi Azar; Bilal Piot; Bernardo A. Pires; Toby Pohlen; Rémi Munos (CoRR, 2018)

Directed Exploration for Reinforcement Learning. - Zhaohan Daniel Guo; Emma Brunskill (CoRR, 2019)

Never Give Up: Learning Directed Exploration Strategies. - Adrià Puigdomènech Badia; Pablo Sprechmann; Alex Vitvitskyi; Zhaohan Daniel Guo; Bilal Piot; Steven Kapturowski; Olivier Tieleman; Martín Arjovsky; Alexander Pritzel; Andrew Bolt; Charles Blundell (ICLR, 2020)

Agent57: Outperforming the Atari Human Benchmark. - Adrià Puigdomènech Badia; Bilal Piot; Steven Kapturowski; Pablo Sprechmann; Alex Vitvitskyi; Zhaohan Daniel Guo; Charles Blundell (ICML, 2020)

Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning. - Zhaohan Daniel Guo; Bernardo Ávila Pires; Bilal Piot; Jean-Bastien Grill; Florent Altché; Rémi Munos; Mohammad Gheshlaghi Azar (ICML, 2020)

Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning. - Jean-Bastien Grill; Florian Strub; Florent Altché; Corentin Tallec; Pierre H. Richemond; Elena Buchatskaya; Carl Doersch; Bernardo Ávila Pires; Zhaohan Guo; Mohammad Gheshlaghi Azar; Bilal Piot; Koray Kavukcuoglu; et al. (NeurIPS, 2020)

Geometric Entropic Exploration. - Zhaohan Daniel Guo; Mohammad Gheshlaghi Azar; Alaa Saade; Shantanu Thakoor; Bilal Piot; Bernardo Ávila Pires; Michal Valko; Thomas Mesnard; Tor Lattimore; Rémi Munos (CoRR, 2021)

BYOL-Explore: Exploration by Bootstrapped Prediction. - Zhaohan Guo; Shantanu Thakoor; Miruna Pislar; Bernardo Ávila Pires; Florent Altché; Corentin Tallec; Alaa Saade; Daniele Calandriello; Jean-Bastien Grill; Yunhao Tang; Michal Valko; Rémi Munos; et al. (NeurIPS, 2022)

Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition. - Yash Chandak; Shantanu Thakoor; Zhaohan Daniel Guo; Yunhao Tang; Rémi Munos; Will Dabney; Diana L. Borsa (ICML, 2023)

Understanding Self-Predictive Learning for Reinforcement Learning. - Yunhao Tang; Zhaohan Daniel Guo; Pierre Harvey Richemond; Bernardo Ávila Pires; Yash Chandak; Rémi Munos; Mark Rowland 001; Mohammad Gheshlaghi Azar; Charline Le Lan; Clare Lyle; András György 001; Shantanu Thakoor; et al. (ICML, 2023)

Nash Learning from Human Feedback. - Rémi Munos; Michal Valko; Daniele Calandriello; Mohammad Gheshlaghi Azar; Mark Rowland 001; Zhaohan Daniel Guo; Yunhao Tang; Matthieu Geist; Thomas Mesnard; Andrea Michi; Marco Selvi; Sertan Girgin; et al. (CoRR, 2023)

A General Theoretical Paradigm to Understand Learning from Human Preferences. - Mohammad Gheshlaghi Azar; Zhaohan Daniel Guo; Bilal Piot; Rémi Munos; Mark Rowland 001; Michal Valko; Daniele Calandriello (AISTATS, 2024)

Human Alignment of Large Language Models through Online Preference Optimisation. - Daniele Calandriello; Zhaohan Daniel Guo; Rémi Munos; Mark Rowland 001; Yunhao Tang; Bernardo Ávila Pires; Pierre Harvey Richemond; Charline Le Lan; Michal Valko; Tianqi Liu 002; Rishabh Joshi; Zeyu Zheng; et al. (ICML, 2024)

Nash Learning from Human Feedback. - Rémi Munos; Michal Valko; Daniele Calandriello; Mohammad Gheshlaghi Azar; Mark Rowland 001; Daniel Guo 001; Yunhao Tang; Matthieu Geist; Thomas Mesnard; Côme Fiegel; Andrea Michi; Marco Selvi; et al. (ICML, 2024)

Generalized Preference Optimization: A Unified Approach to Offline Alignment. - Yunhao Tang; Zhaohan Daniel Guo; Zeyu Zheng; Daniele Calandriello; Rémi Munos; Mark Rowland 001; Pierre Harvey Richemond; Michal Valko; Bernardo Ávila Pires; Bilal Piot (ICML, 2024)

Understanding the performance gap between online and offline alignment algorithms. - Yunhao Tang; Zhaohan Daniel Guo; Zeyu Zheng; Daniele Calandriello; Yuan Cao; Eugene Tarassov; Rémi Munos; Bernardo Ávila Pires; Michal Valko; Yong Cheng; Will Dabney (CoRR, 2024)

Offline Regularised Reinforcement Learning for Large Language Models Alignment. - Pierre Harvey Richemond; Yunhao Tang; Daniel Guo 001; Daniele Calandriello; Mohammad Gheshlaghi Azar; Rafael Rafailov; Bernardo Ávila Pires; Eugene Tarassov; Lucas Spangher; Will Ellsworth; Aliaksei Severyn; Jonathan Mallinson; et al. (CoRR, 2024)

A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning. - Khimya Khetarpal; Zhaohan Daniel Guo; Bernardo Ávila Pires; Yunhao Tang; Clare Lyle; Mark Rowland 001; Nicolas Heess; Diana L. Borsa; Arthur Guez; Will Dabney (AISTATS, 2025)

Optimizing Return Distributions with Distributional Dynamic Programming. - Bernardo Ávila Pires; Mark Rowland 001; Diana Borsa; Zhaohan Daniel Guo; Khimya Khetarpal; André Barreto 001; David Abel; Rémi Munos; Will Dabney (CoRR, 2025)

Representation Learning via Non-Contrastive Mutual Information. - Zhaohan Daniel Guo; Bernardo Ávila Pires; Khimya Khetarpal; Dale Schuurmans; Bo Dai 001 (CoRR, 2025)