Online Prediction on Large Diameter Graphs. - Mark Herbster; Guy Lever; Massimiliano Pontil (NIPS, 2008)

Predicting the Labelling of a Graph via Minimum $p$-Seminorm Interpolation. - Mark Herbster; Guy Lever (COLT, 2009)

Distribution-Dependent PAC-Bayes Priors. - Guy Lever; François Laviolette; John Shawe-Taylor (ALT, 2010)

Relating Function Class Complexity and Cluster Structure in the Function Domain with Applications to Transduction. - Guy Lever (AISTATS, 2010)

Exploiting structure defined by data in machine learning : some new analyses. - Guy Lever (2011)

Modelling transition dynamics in MDPs with RKHS embeddings. - Steffen Grünewälder; Guy Lever; Luca Baldassarre; Massimiliano Pontil; Arthur Gretton (ICML, 2012)

Conditional mean embeddings as regressors. - Steffen Grünewälder; Guy Lever; Arthur Gretton; Luca Baldassarre; Sam Patterson; Massimiliano Pontil (ICML, 2012)

Conditional mean embeddings as regressors - supplementary - Steffen Grünewälder; Guy Lever; Luca Baldassarre; Sam Patterson; Arthur Gretton; Massimiliano Pontil (CoRR, 2012)

Data dependent kernels in nearly-linear time. - Guy Lever; Tom Diethe; John Shawe-Taylor (AISTATS, 2012)

Tighter PAC-Bayes bounds through distribution-dependent priors. - Guy Lever; François Laviolette; John Shawe-Taylor (Theor. Comput. Sci., 2013)

Deterministic Policy Gradient Algorithms. - David Silver; Guy Lever; Nicolas Heess; Thomas Degris; Daan Wierstra; Martin A. Riedmiller (ICML, 2014)

Modelling Policies in MDPs in Reproducing Kernel Hilbert Space. - Guy Lever; Ronnie Stafford (AISTATS, 2015)

A Gauss-Newton Method for Markov Decision Processes. - Thomas Furmston; Guy Lever (CoRR, 2015)

Compressed Conditional Mean Embeddings for Model-Based Reinforcement Learning. - Guy Lever; John Shawe-Taylor; Ronnie Stafford; Csaba Szepesvári (AAAI, 2016)

Approximate Newton Methods for Policy Search in Markov Decision Processes. - Thomas Furmston; Guy Lever; David Barber (J. Mach. Learn. Res., 2016)

Nesterov's accelerated gradient and momentum as approximations to regularised update descent. - Aleksandar Botev; Guy Lever; David Barber (IJCNN, 2017)

Value-Decomposition Networks For Cooperative Multi-Agent Learning. - Peter Sunehag; Guy Lever; Audrunas Gruslys; Wojciech Marian Czarnecki; Vinícius Flores Zambaldi; Max Jaderberg; Marc Lanctot; Nicolas Sonnerat; Joel Z. Leibo; Karl Tuyls; Thore Graepel (CoRR, 2017)

Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward. - Peter Sunehag; Guy Lever; Audrunas Gruslys; Wojciech Marian Czarnecki; Vinícius Flores Zambaldi; Max Jaderberg; Marc Lanctot; Nicolas Sonnerat; Joel Z. Leibo; Karl Tuyls; Thore Graepel (AAMAS, 2018)

Human-level performance in first-person multiplayer games with population-based deep reinforcement learning. - Max Jaderberg; Wojciech M. Czarnecki; Iain Dunning; Luke Marris; Guy Lever; Antonio García Castañeda; Charles Beattie; Neil C. Rabinowitz; Ari S. Morcos; Avraham Ruderman; Nicolas Sonnerat; Tim Green; et al. (CoRR, 2018)

The Body is Not a Given: Joint Agent Policy Learning and Morphology Evolution. - Dylan Banarse; Yoram Bachrach; Siqi Liu 002; Guy Lever; Nicolas Heess; Chrisantha Fernando; Pushmeet Kohli; Thore Graepel (AAMAS, 2019)

Emergent Coordination Through Competition. - Siqi Liu 002; Guy Lever; Josh Merel; Saran Tunyasuvunakool; Nicolas Heess; Thore Graepel (ICLR (Poster), 2019)

Reinforcement Learning Agents acquire Flocking and Symbiotic Behaviour in Simulated Ecosystems. - Peter Sunehag; Guy Lever; Siqi Liu 002; Josh Merel; Nicolas Heess; Joel Z. Leibo; Edward Hughes 001; Tom Eccles; Thore Graepel (ALIFE, 2019)

Biases for Emergent Communication in Multi-agent Reinforcement Learning. - Tom Eccles; Yoram Bachrach; Guy Lever; Angeliki Lazaridou; Thore Graepel (NeurIPS, 2019)

A Generalized Training Approach for Multiagent Learning. - Paul Muller; Shayegan Omidshafiei; Mark Rowland 001; Karl Tuyls; Julien Pérolat; Siqi Liu 002; Daniel Hennes; Luke Marris; Marc Lanctot; Edward Hughes 001; Zhe Wang 055; Guy Lever; et al. (ICLR, 2020)

Developing, evaluating and scaling learning agents in multi-agent environments. - Ian Gemp; Thomas W. Anthony 001; Yoram Bachrach; Avishkar Bhoopchand; Kalesha Bullard; Jerome T. Connor; Vibhavari Dasagi; Bart De Vylder; Edgar A. Duéñez-Guzmán; Romuald Elie; Richard Everett 001; Daniel Hennes; et al. (AI Commun., 2022)

From motor control to team play in simulated humanoid football. - Siqi Liu 002; Guy Lever; Zhe Wang 055; Josh Merel; S. M. Ali Eslami; Daniel Hennes; Wojciech M. Czarnecki; Yuval Tassa; Shayegan Omidshafiei; Abbas Abdolmaleki; Noah Y. Siegel; Leonard Hasenclever; et al. (Sci. Robotics, 2022)

Figure Data for the paper "From Motor Control to Team Play in Simulated Humanoid Football". - Siqi Liu 002; Guy Lever; Zhe Wang 055; Josh Merel; S. M. Ali Eslami; Daniel Hennes; Wojciech Czarnecki 001; Yuval Tassa; Shayegan Omidshafiei; Abbas Abdolmaleki; Noah Y. Siegel; Leonard Hasenclever; et al. (Zenodo, 2022)

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; et al. (CoRL, 2024)

Generative Adversarial Equilibrium Solvers. - Denizalp Goktas; David C. Parkes; Ian Gemp; Luke Marris; Georgios Piliouras; Romuald Elie; Guy Lever; Andrea Tacchetti (ICLR, 2024)

Replay across Experiments: A Natural Extension of Off-Policy RL. - Dhruva Tirumala; Thomas Lampe; José Enrique Chen; Tuomas Haarnoja; Sandy H. Huang; Guy Lever; Ben Moran; Tim Hertweck; Leonard Hasenclever; Martin A. Riedmiller; Nicolas Heess; Markus Wulfmeier (ICLR, 2024)

Learning agile soccer skills for a bipedal robot with deep reinforcement learning. - Tuomas Haarnoja; Ben Moran; Guy Lever; Sandy H. Huang; Dhruva Tirumala; Jan Humplik; Markus Wulfmeier; Saran Tunyasuvunakool; Noah Y. Siegel; Roland Hafner; Michael Bloesch; Kristian Hartikainen; et al. (Sci. Robotics, 2024)

Data Release for: Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning. - Tuomas Haarnoja; Ben Moran; Guy Lever; Sandy H. Huang; Dhruva Tirumala; Jan Humplik; Markus Wulfmeier; Saran Tunyasuvunakool; Noah Y. Siegel; Roland Hafner; Michael Bloesch; Kristian Hartikainen; et al. (Zenodo, 2024)