Prediction with directed transitions: complex eigenstructure, grid cells and phase coding. - Changmin Yu; Timothy Edward John Behrens; Neil Burgess (CoRR, 2020)

Prediction and generalisation over directed actions by grid cells. - Changmin Yu; Timothy Behrens; Neil Burgess (ICLR, 2021)

DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention. - David Mguni; Joel Jennings; Taher Jafferjee; Aivar Sootla; Yaodong Yang 001; Changmin Yu; Usman Islam; Ziyan Wang; Jun Wang 012 (CoRR, 2021)

What about Inputting Policy in Value Function: Policy Representation and Policy-Extended Value Function Approximator. - Hongyao Tang; Zhaopeng Meng; Jianye Hao; Chen Chen 077; Daniel Graves; Dong Li 016; Changmin Yu; Hangyu Mao; Wulong Liu; Yaodong Yang 002; Wenyuan Tao; Li Wang (AAAI, 2022)

Learning State Representations via Retracing in Reinforcement Learning. - Changmin Yu; Dong Li 016; Jianye Hao; Jun Wang 012; Neil Burgess (ICLR, 2022)

Structured Recognition for Generative Models with Explaining Away. - Changmin Yu; Hugo Soulat; Neil Burgess; Maneesh Sahani (NeurIPS, 2022)

SEREN: Knowing When to Explore and When to Exploit. - Changmin Yu; David Mguni; Dong Li 016; Aivar Sootla; Jun Wang 012; Neil Burgess (CoRR, 2022)

Amortised Inference in Structured Generative Models with Explaining Away. - Changmin Yu; Hugo Soulat; Neil Burgess; Maneesh Sahani (CoRR, 2022)

Unsupervised representational learning with recognition-parametrised probabilistic models. - William I. Walker; Hugo Soulat; Changmin Yu; Maneesh Sahani (CoRR, 2022)

Unsupervised representation learning with recognition-parametrised probabilistic models. - William I. Walker; Hugo Soulat; Changmin Yu; Maneesh Sahani (AISTATS, 2023)

Successor-Predecessor Intrinsic Exploration. - Changmin Yu; Neil Burgess; Maneesh Sahani; Samuel J. Gershman (NeurIPS, 2023)

Discovering Temporally Compositional Neural Manifolds with Switching Infinite GPFA. - Changmin Yu; Maneesh Sahani; Máté Lengyel (ICLR, 2025)