Differential Recurrent Neural Networks for Action Recognition. - Vivek Veeriah; Naifan Zhuang; Guo-Jun Qi (ICCV, 2015)

Deep Learning Architecture with Dynamically Programmed Layers for Brain Connectome Prediction. - Vivek Veeriah; Rohit Durvasula; Guo-Jun Qi (KDD, 2015)

Learning representations through stochastic gradient descent in cross-validation error. - Richard S. Sutton; Vivek Veeriah (CoRR, 2016)

Face valuing: Training user interfaces with facial expressions and reinforcement learning. - Vivek Veeriah; Patrick M. Pilarski; Richard S. Sutton (CoRR, 2016)

Forward Actor-Critic for Nonlinear Function Approximation in Reinforcement Learning. - Vivek Veeriah; Harm van Seijen; Richard S. Sutton (AAMAS, 2017)

Crossprop: Learning Representations by Stochastic Meta-Gradient Descent in Neural Networks. - Vivek Veeriah; Shangtong Zhang; Richard S. Sutton (ECML/PKDD (1), 2017)

TIDBD: Adapting Temporal-difference Step-sizes Through Stochastic Meta-descent. - Alexandra Kearney; Vivek Veeriah; Jaden B. Travnik; Richard S. Sutton; Patrick M. Pilarski (CoRR, 2018)

Many-Goals Reinforcement Learning. - Vivek Veeriah; Junhyuk Oh; Satinder Singh 001 (CoRR, 2018)

Discovery of Useful Questions as Auxiliary Tasks. - Vivek Veeriah; Matteo Hessel; Zhongwen Xu; Janarthanan Rajendran; Richard L. Lewis; Junhyuk Oh; Hado van Hasselt; David Silver; Satinder Singh 001 (NeurIPS, 2019)

Learning Feature Relevance Through Step Size Adaptation in Temporal-Difference Learning. - Alexandra Kearney; Vivek Veeriah; Jaden B. Travnik; Patrick M. Pilarski; Richard S. Sutton (CoRR, 2019)

How Should an Agent Practice? - Janarthanan Rajendran; Richard L. Lewis; Vivek Veeriah; Honglak Lee; Satinder Singh 001 (AAAI, 2020)

A Self-Tuning Actor-Critic Algorithm. - Tom Zahavy; Zhongwen Xu; Vivek Veeriah; Matteo Hessel; Junhyuk Oh; Hado van Hasselt; David Silver; Satinder Singh 001 (NeurIPS, 2020)

Learning Retrospective Knowledge with Reverse Reinforcement Learning. - Shangtong Zhang; Vivek Veeriah; Shimon Whiteson (NeurIPS, 2020)

Self-Tuning Deep Reinforcement Learning. - Tom Zahavy; Zhongwen Xu; Vivek Veeriah; Matteo Hessel; Junhyuk Oh; Hado van Hasselt; David Silver; Satinder Singh 001 (CoRR, 2020)

Discovery of Options via Meta-Learned Subgoals. - Vivek Veeriah; Tom Zahavy; Matteo Hessel; Zhongwen Xu; Junhyuk Oh; Iurii Kemaev; Hado van Hasselt; David Silver; Satinder Singh 001 (NeurIPS, 2021)

Learning State Representations from Random Deep Action-conditional Predictions. - Zeyu Zheng; Vivek Veeriah; Risto Vuorio; Richard L. Lewis; Satinder Singh 001 (NeurIPS, 2021)

GrASP: Gradient-Based Affordance Selection for Planning. - Vivek Veeriah; Zeyu Zheng; Richard L. Lewis; Satinder Singh 001 (CoRR, 2022)

Discovery in Reinforcement Learning - Vivek Veeriah (2022)

ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs. - Ted Moskovitz; Brendan O'Donoghue; Vivek Veeriah; Sebastian Flennerhag; Satinder Singh 001; Tom Zahavy (ICML, 2023)

Diversifying AI: Towards Creative Chess with AlphaZero. - Tom Zahavy; Vivek Veeriah; Shaobo Hou; Kevin Waugh; Matthew Lai; Edouard Leurent; Nenad Tomasev; Lisa Schut; Demis Hassabis; Satinder Singh 001 (CoRR, 2023)

AugInsert: Learning Robust Visual-Force Policies via Data Augmentation for Object Assembly Tasks. - Ryan Diaz; Adam Imdieke; Vivek Veeriah; Karthik Desingh (CoRR, 2024)