The Wreath Process: A totally generative model of geometric shape based on nested symmetries. - Diana Borsa; Thore Graepel; Andrew D. Gordon 001 (CoRR, 2015)

Training Neural Nets to Aggregate Crowdsourced Responses. - Alex Gaunt; Diana Borsa; Yoram Bachrach (UAI, 2016)

Learning Shared Representations in Multi-task Reinforcement Learning. - Diana Borsa; Thore Graepel; John Shawe-Taylor (CoRR, 2016)

Observational Learning by Reinforcement Learning. - Diana Borsa; Bilal Piot; Rémi Munos; Olivier Pietquin (CoRR, 2017)

Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement. - André Barreto 001; Diana Borsa; John Quan; Tom Schaul; David Silver 001; Matteo Hessel; Daniel J. Mankowitz; Augustin Zídek; Rémi Munos (ICML, 2018)

The Termination Critic. - Anna Harutyunyan; Will Dabney; Diana Borsa; Nicolas Heess; Rémi Munos; Doina Precup (AISTATS, 2019)

Observational Learning by Reinforcement Learning. - Diana Borsa; Nicolas Heess; Bilal Piot; Siqi Liu 002; Leonard Hasenclever; Rémi Munos; Olivier Pietquin (AAMAS, 2019)

Universal Successor Features Approximators. - Diana Borsa; André Barreto 001; John Quan; Daniel J. Mankowitz; Hado van Hasselt; Rémi Munos; David Silver 001; Tom Schaul (ICLR (Poster), 2019)

The Option Keyboard: Combining Skills in Reinforcement Learning. - André Barreto 001; Diana Borsa; Shaobo Hou; Gheorghe Comanici; Eser Aygün; Philippe Hamel; Daniel Toyama; Jonathan J. Hunt; Shibl Mourad; David Silver 001; Doina Precup (NeurIPS, 2019)

Ray Interference: a Source of Plateaus in Deep Reinforcement Learning. - Tom Schaul; Diana Borsa; Joseph Modayil; Razvan Pascanu (CoRR, 2019)

General non-linear Bellman equations. - Hado van Hasselt; John Quan; Matteo Hessel; Zhongwen Xu; Diana Borsa; André Barreto 001 (CoRR, 2019)

Adapting Behaviour for Learning Progress. - Tom Schaul; Diana Borsa; David Ding; David Szepesvari; Georg Ostrovski; Will Dabney; Simon Osindero (CoRR, 2019)

Conditional Importance Sampling for Off-Policy Learning. - Mark Rowland 001; Anna Harutyunyan; Hado van Hasselt; Diana Borsa; Tom Schaul; Rémi Munos; Will Dabney (AISTATS, 2020)

Temporal Difference Uncertainties as a Signal for Exploration. - Sebastian Flennerhag; Jane X. Wang; Pablo Sprechmann; Francesco Visin; Alexandre Galashov; Steven Kapturowski; Diana L. Borsa; Nicolas Heess; André Barreto 001; Razvan Pascanu (CoRR, 2020)

Fast reinforcement learning with generalized policy updates. - André Barreto 001; Shaobo Hou; Diana Borsa; David Silver 001; Doina Precup (Proc. Natl. Acad. Sci. USA, 2020)

Reinforcement learning in persistent environments: representation learning and transfer. - Diana Borsa (2020)

Expected Eligibility Traces. - Hado van Hasselt; Sephora Madjiheurem; Matteo Hessel; David Silver 001; André Barreto 001; Diana Borsa (AAAI, 2021)

Return-based Scaling: Yet Another Normalisation Trick for Deep RL. - Tom Schaul; Georg Ostrovski; Iurii Kemaev; Diana Borsa (CoRR, 2021)

When should agents explore? - Miruna Pislar; David Szepesvari; Georg Ostrovski; Diana L. Borsa; Tom Schaul (ICLR, 2022)

Model-Value Inconsistency as a Signal for Epistemic Uncertainty. - Angelos Filos; Eszter Vértes; Zita Marinho; Gregory Farquhar; Diana Borsa; Abram L. Friesen; Feryal M. P. Behbahani; Tom Schaul; André Barreto 001; Simon Osindero (ICML, 2022)

Generalised Policy Improvement with Geometric Policy Composition. - Shantanu Thakoor; Mark Rowland 001; Diana Borsa; Will Dabney; Rémi Munos; André Barreto 001 (ICML, 2022)

Selective Credit Assignment. - Veronica Chelu; Diana Borsa; Doina Precup; Hado van Hasselt (CoRR, 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)

A State Representation for Diminishing Rewards. - Ted Moskovitz; Samo Hromadka; Ahmed Touati; Diana Borsa; Maneesh Sahani (NeurIPS, 2023)

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)

Wasserstein Policy Optimization. - David Pfau; Ian Davies; Diana Borsa; João G. M. Araújo; Brendan D. Tracey; Hado van Hasselt (CoRR, 2025)