A New Take on Detecting Insider Threats: Exploring the Use of Hidden Markov Models. - Tabish Rashid; Ioannis Agrafiotis; Jason R. C. Nurse (MIST@CCS, 2016)

QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning. - Tabish Rashid; Mikayel Samvelyan; Christian Schröder de Witt; Gregory Farquhar; Jakob N. Foerster; Shimon Whiteson (ICML, 2018)

The StarCraft Multi-Agent Challenge. - Mikayel Samvelyan; Tabish Rashid; Christian Schröder de Witt; Gregory Farquhar; Nantas Nardelli; Tim G. J. Rudner; Chia-Man Hung; Philip H. S. Torr; Jakob N. Foerster; Shimon Whiteson (AAMAS, 2019)

MAVEN: Multi-Agent Variational Exploration. - Anuj Mahajan; Tabish Rashid; Mikayel Samvelyan; Shimon Whiteson (NeurIPS, 2019)

Exploration with Unreliable Intrinsic Reward in Multi-Agent Reinforcement Learning. - Wendelin Böhmer; Tabish Rashid; Shimon Whiteson (CoRR, 2019)

Optimistic Exploration even with a Pessimistic Initialisation. - Tabish Rashid; Bei Peng 001; Wendelin Boehmer; Shimon Whiteson (ICLR, 2020)

Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning. - Tabish Rashid; Gregory Farquhar; Bei Peng 001; Shimon Whiteson (NeurIPS, 2020)

Weighted QMIX: Expanding Monotonic Value Function Factorisation. - Tabish Rashid; Gregory Farquhar; Bei Peng 001; Shimon Whiteson (CoRR, 2020)

Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning. - Tabish Rashid; Mikayel Samvelyan; Christian Schröder de Witt; Gregory Farquhar; Jakob N. Foerster; Shimon Whiteson (J. Mach. Learn. Res., 2020)

Estimating α-Rank by Maximizing Information Gain. - Tabish Rashid; Cheng Zhang 005; Kamil Ciosek (AAAI, 2021)

Regularized Softmax Deep Multi-Agent Q-Learning. - Ling Pan; Tabish Rashid; Bei Peng 001; Longbo Huang; Shimon Whiteson (NeurIPS, 2021)

FACMAC: Factored Multi-Agent Centralised Policy Gradients. - Bei Peng 001; Tabish Rashid; Christian Schröder de Witt; Pierre-Alexandre Kamienny; Philip H. S. Torr; Wendelin Boehmer; Shimon Whiteson (NeurIPS, 2021)

Softmax with Regularization: Better Value Estimation in Multi-Agent Reinforcement Learning. - Ling Pan; Tabish Rashid; Bei Peng 001; Longbo Huang; Shimon Whiteson (CoRR, 2021)

Imitating Human Behaviour with Diffusion Models. - Tim Pearce; Tabish Rashid; Anssi Kanervisto; David Bignell; Mingfei Sun 001; Raluca Georgescu; Sergio Valcarcel Macua; Shan Zheng Tan; Ida Momennejad; Katja Hofmann; Sam Devlin (ICLR, 2023)

Visual Encoders for Data-Efficient Imitation Learning in Modern Video Games. - Lukas Schäfer 001; Logan Jones; Anssi Kanervisto; Yuhan Cao 001; Tabish Rashid; Raluca Georgescu; David Bignell; Siddhartha Sen 001; Andrea Treviño Gavito; Sam Devlin (CoRR, 2023)

Aligning Agents like Large Language Models. - Adam Jelley; Yuhan Cao 001; David Bignell; Sam Devlin; Tabish Rashid (CoRR, 2024)

Scaling Laws for Pre-training Agents and World Models. - Tim Pearce; Tabish Rashid; David Bignell; Raluca Georgescu; Sam Devlin; Katja Hofmann (CoRR, 2024)

World and Human Action Models towards gameplay ideation. - Anssi Kanervisto; David Bignell; Linda Yilin Wen; Martin Grayson; Raluca Georgescu; Sergio Valcarcel Macua; Shan Zheng Tan; Tabish Rashid; Tim Pearce; Yuhan Cao 001; Abdelhak Lemkhenter; Chentian Jiang; et al. (Nat., 2025)

Fast Autoregressive Video Generation with Diagonal Decoding. - Yang Ye; Junliang Guo; Haoyu Wu; Tianyu He; Tim Pearce; Tabish Rashid; Katja Hofmann; Jiang Bian 002 (CoRR, 2025)

Adapting Vision-Language Models for Evaluating World Models. - Mariya Hendriksen; Tabish Rashid; David Bignell; Raluca Georgescu; Abdelhak Lemkhenter; Katja Hofmann; Sam Devlin; Sarah Parisot (CoRR, 2025)