Vivek Veeriah - RankMe

31 papers A* 9A 2Journal 18Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	A	`conf`	AugInsert: Learning Robust Visual-Force Policies via Data Augmentation for Object Assembly Tasks. IROS Ryan Diaz, Adam Imdieke, Vivek Veeriah, Karthik Desingh
2025	J	`jnl`	Evaluating In Silico Creativity: An Expert Review of AI Chess Compositions. CoRR Vivek Veeriah, Federico Barbero, Marcus Chiam, Xidong Feng, Michael Dennis, Ryan Pachauri, Thomas Tumiel, Johan Obando-Ceron, Jiaxin Shi, Shaobo Hou, Satinder Singh, Nenad Tomasev, Tom Zahavy
2025	J	`jnl`	Generating Creative Chess Puzzles. CoRR Xidong Feng, Vivek Veeriah, Marcus Chiam, Michael Dennis, Ryan Pachauri, Thomas Tumiel, Federico Barbero, Johan Obando-Ceron, Jiaxin Shi, Satinder Singh, Shaobo Hou, Nenad Tomasev, Tom Zahavy
2024	J	`jnl`	AugInsert: Learning Robust Visual-Force Policies via Data Augmentation for Object Assembly Tasks. CoRR Ryan Diaz, Adam Imdieke, Vivek Veeriah, Karthik Desingh
2023	J	`jnl`	Diversifying AI: Towards Creative Chess with AlphaZero. CoRR Tom Zahavy, Vivek Veeriah, Shaobo Hou, Kevin Waugh, Matthew Lai, Edouard Leurent, Nenad Tomasev, Lisa Schut, Demis Hassabis, Satinder Singh
2023	A*	`conf`	ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs. ICML Ted Moskovitz, Brendan O'Donoghue, Vivek Veeriah, Sebastian Flennerhag, Satinder Singh, Tom Zahavy
2023	J	`jnl`	ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs. CoRR Ted Moskovitz, Brendan O'Donoghue, Vivek Veeriah, Sebastian Flennerhag, Satinder Singh, Tom Zahavy
2022	—	`—`	Discovery in Reinforcement Learning Vivek Veeriah
2022	J	`jnl`	GrASP: Gradient-Based Affordance Selection for Planning. CoRR Vivek Veeriah, Zeyu Zheng, Richard L. Lewis, Satinder Singh
2021	A*	`conf`	Discovery of Options via Meta-Learned Subgoals. NeurIPS Vivek Veeriah, Tom Zahavy, Matteo Hessel, Zhongwen Xu, Junhyuk Oh, Iurii Kemaev, Hado van Hasselt, David Silver, Satinder Singh
2021	J	`jnl`	Discovery of Options via Meta-Learned Subgoals. CoRR Vivek Veeriah, Tom Zahavy, Matteo Hessel, Zhongwen Xu, Junhyuk Oh, Iurii Kemaev, Hado van Hasselt, David Silver, Satinder Singh
2021	A*	`conf`	Learning State Representations from Random Deep Action-conditional Predictions. NeurIPS Zeyu Zheng, Vivek Veeriah, Risto Vuorio, Richard L. Lewis, Satinder Singh
2021	J	`jnl`	Learning State Representations from Random Deep Action-conditional Predictions. CoRR Zeyu Zheng, Vivek Veeriah, Risto Vuorio, Richard L. Lewis, Satinder Singh
2020	A*	`conf`	A Self-Tuning Actor-Critic Algorithm. NeurIPS Tom Zahavy, Zhongwen Xu, Vivek Veeriah, Matteo Hessel, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh
2020	A*	`conf`	How Should an Agent Practice? AAAI Janarthanan Rajendran, Richard L. Lewis, Vivek Veeriah, Honglak Lee, Satinder Singh
2020	A*	`conf`	Learning Retrospective Knowledge with Reverse Reinforcement Learning. NeurIPS Shangtong Zhang, Vivek Veeriah, Shimon Whiteson
2020	J	`jnl`	Learning Retrospective Knowledge with Reverse Reinforcement Learning. CoRR Shangtong Zhang, Vivek Veeriah, Shimon Whiteson
2020	J	`jnl`	Self-Tuning Deep Reinforcement Learning. CoRR Tom Zahavy, Zhongwen Xu, Vivek Veeriah, Matteo Hessel, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh
2019	A*	`conf`	Discovery of Useful Questions as Auxiliary Tasks. NeurIPS Vivek Veeriah, Matteo Hessel, Zhongwen Xu, Janarthanan Rajendran, Richard L. Lewis, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh
2019	J	`jnl`	Discovery of Useful Questions as Auxiliary Tasks. CoRR Vivek Veeriah, Matteo Hessel, Zhongwen Xu, Richard L. Lewis, Janarthanan Rajendran, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh
2019	J	`jnl`	How Should an Agent Practice? CoRR Janarthanan Rajendran, Richard L. Lewis, Vivek Veeriah, Honglak Lee, Satinder Singh
2019	J	`jnl`	Learning Feature Relevance Through Step Size Adaptation in Temporal-Difference Learning. CoRR Alexandra Kearney, Vivek Veeriah, Jaden B. Travnik, Patrick M. Pilarski, Richard S. Sutton
2018	J	`jnl`	Many-Goals Reinforcement Learning. CoRR Vivek Veeriah, Junhyuk Oh, Satinder Singh
2018	J	`jnl`	TIDBD: Adapting Temporal-difference Step-sizes Through Stochastic Meta-descent. CoRR Alexandra Kearney, Vivek Veeriah, Jaden B. Travnik, Richard S. Sutton, Patrick M. Pilarski
2017	—	`conf`	Crossprop: Learning Representations by Stochastic Meta-Gradient Descent in Neural Networks. ECML/PKDD (1) Vivek Veeriah, Shangtong Zhang, Richard S. Sutton
2017	A	`conf`	Forward Actor-Critic for Nonlinear Function Approximation in Reinforcement Learning. AAMAS Vivek Veeriah, Harm van Seijen, Richard S. Sutton
2016	J	`jnl`	Face valuing: Training user interfaces with facial expressions and reinforcement learning. CoRR Vivek Veeriah, Patrick M. Pilarski, Richard S. Sutton
2016	J	`jnl`	Learning representations through stochastic gradient descent in cross-validation error. CoRR Richard S. Sutton, Vivek Veeriah
2015	A*	`conf`	Deep Learning Architecture with Dynamically Programmed Layers for Brain Connectome Prediction. KDD Vivek Veeriah, Rohit Durvasula, Guo-Jun Qi
2015	A*	`conf`	Differential Recurrent Neural Networks for Action Recognition. ICCV Vivek Veeriah, Naifan Zhuang, Guo-Jun Qi
2015	J	`jnl`	Differential Recurrent Neural Networks for Action Recognition. CoRR Vivek Veeriah, Naifan Zhuang, Guo-Jun Qi