Flexible and accurate inference and learning for deep generative models. - Eszter Vértes; Maneesh Sahani (NeurIPS, 2018)

A neurally plausible model learns successor representations in partially observable environments. - Eszter Vértes; Maneesh Sahani (NeurIPS, 2019)

Procedural generalization by planning with self-supervised world models. - Ankesh Anand; Jacob C. Walker; Yazhe Li; Eszter Vértes; Julian Schrittwieser; Sherjil Ozair; Theophane Weber; Jessica B. Hamrick (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)

Investigating the Role of Model-Based Learning in Exploration and Transfer. - Jacob C. Walker; Eszter Vértes; Yazhe Li; Gabriel Dulac-Arnold; Ankesh Anand; Theophane Weber; Jessica B. Hamrick (ICML, 2023)

Active Acquisition for Multimodal Temporal Data: A Challenging Decision-Making Task. - Jannik Kossen; Catalina Cangea; Eszter Vértes; Andrew Jaegle; Viorica Patraucean; Ira Ktena; Nenad Tomasev; Danielle Belgrave (Trans. Mach. Learn. Res., 2023)