A. Rupam Mahmood

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 44

Top Topics

Reinforcement Learning

Visual Servoing

Kullback Leibler

Top Venues

Publications

Mohamed Elsayed, A. Rupam Mahmood
Addressing Loss of Plasticity and Catastrophic Forgetting in Continual Learning. CoRR (2024)
Fengdi Che, Chenjun Xiao, Jincheng Mei, Bo Dai, Ramki Gummadi, Oscar A Ramirez, Christopher K. Harris, A. Rupam Mahmood, Dale Schuurmans
Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function Approximation. CoRR (2024)
Mohamed Elsayed, Homayoon Farrahi, Felix Dangel, A. Rupam Mahmood
Revisiting Scalable Hessian Diagonal Approximations for Applications in Reinforcement Learning. CoRR (2024)
Gautham Vasan, Yan Wang, Fahim Shahriar, James Bergstra, Martin Jägersand, A. Rupam Mahmood
Revisiting Sparse Rewards for Goal-Reaching Reinforcement Learning. CoRR (2024)
Mohamed Elsayed, A. Rupam Mahmood
Addressing Loss of Plasticity and Catastrophic Forgetting in Continual Learning. ICLR (2024)
Bram Grooten, Tristan Tomilin, Gautham Vasan, Matthew E. Taylor, A. Rupam Mahmood, Meng Fang, Mykola Pechenizkiy, Decebal Constantin Mocanu
MaDi: Learning to Mask Distractions for Generalization in Visual Deep Reinforcement Learning. AAMAS (2024)
Haque Ishfaq, Yixin Tan, Yu Yang, Qingfeng Lan, Jianfeng Lu, A. Rupam Mahmood, Doina Precup, Pan Xu
More Efficient Randomized Exploration for Reinforcement Learning via Approximate Sampling. CoRR (2024)
Haque Ishfaq, Qingfeng Lan, Pan Xu, A. Rupam Mahmood, Doina Precup, Anima Anandkumar, Kamyar Azizzadenesheli
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo. ICLR (2024)
Mohamed Elsayed, Qingfeng Lan, Clare Lyle, A. Rupam Mahmood
Weight Clipping for Deep Continual and Reinforcement Learning. CoRR (2024)
Qingfeng Lan, A. Rupam Mahmood, Shuicheng Yan, Zhongwen Xu
Learning to Optimize for Reinforcement Learning. CoRR (2023)
Amirmohammad Karimi, Jun Jin, Jun Luo, A. Rupam Mahmood, Martin Jägersand, Samuele Tosatto
Dynamic Decision Frequency with Continuous Options. IROS (2023)
Mohamed Elsayed, A. Rupam Mahmood
Utility-based Perturbed Gradient Descent: An Optimizer for Continual Learning. CoRR (2023)
Homayoon Farrahi, A. Rupam Mahmood
Reducing the Cost of Cycle-Time Tuning for Real-World Policy Optimization. IJCNN (2023)
Haque Ishfaq, Qingfeng Lan, Pan Xu, A. Rupam Mahmood, Doina Precup, Anima Anandkumar, Kamyar Azizzadenesheli
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo. CoRR (2023)
Jiamin He, Fengdi Che, Yi Wan, A. Rupam Mahmood
Loosely consistent emphatic temporal-difference learning. UAI (2023)
Yan Wang, Gautham Vasan, A. Rupam Mahmood
Real-Time Reinforcement Learning for Vision-Based Robotics Utilizing Local and Remote Computers. ICRA (2023)
Homayoon Farrahi, A. Rupam Mahmood
Reducing the Cost of Cycle-Time Tuning for Real-World Policy Optimization. CoRR (2023)
Fengdi Che, Gautham Vasan, A. Rupam Mahmood
Correcting discount-factor mismatch in on-policy policy gradient methods. CoRR (2023)
Fengdi Che, Gautham Vasan, A. Rupam Mahmood
Correcting discount-factor mismatch in on-policy policy gradient methods. ICML (2023)
Bram Grooten, Tristan Tomilin, Gautham Vasan, Matthew E. Taylor, A. Rupam Mahmood, Meng Fang, Mykola Pechenizkiy, Decebal Constantin Mocanu
MaDi: Learning to Mask Distractions for Generalization in Visual Deep Reinforcement Learning. CoRR (2023)
Shibhansh Dohare, J. Fernando Hernandez-Garcia, Parash Rahman, Richard S. Sutton, A. Rupam Mahmood
Maintaining Plasticity in Deep Continual Learning. CoRR (2023)
Qingfeng Lan, A. Rupam Mahmood
Elephant Neural Networks: Born to Be a Continual Learner. CoRR (2023)
Qingfeng Lan, Yangchen Pan, Jun Luo, A. Rupam Mahmood
Memory-efficient Reinforcement Learning with Value-based Knowledge Consolidation. Trans. Mach. Learn. Res. 2023 (2023)
Yan Wang, Gautham Vasan, A. Rupam Mahmood
Real-Time Reinforcement Learning for Vision-Based Robotics Utilizing Local and Remote Computers. CoRR (2022)
Qingfeng Lan, Yangchen Pan, Jun Luo, A. Rupam Mahmood
Memory-efficient Reinforcement Learning with Knowledge Consolidation. CoRR (2022)
Mohamed Elsayed, A. Rupam Mahmood
HesScale: Scalable Computation of Hessian Diagonals. CoRR (2022)
Amirmohammad Karimi, Jun Jin, Jun Luo, A. Rupam Mahmood, Martin Jägersand, Samuele Tosatto
Variable-Decision Frequency Option Critic. CoRR (2022)
Yufeng Yuan, A. Rupam Mahmood
Asynchronous Reinforcement Learning for Real-Time Control of Physical Robots. ICRA (2022)
Alan Chan, Hugo Silva, Sungsu Lim, Tadashi Kozuno, A. Rupam Mahmood, Martha White
Greedification Operators for Policy Optimization: Investigating Forward and Reverse KL Divergences. J. Mach. Learn. Res. 23 (2022)
Samuele Tosatto, Andrew Patterson, Martha White, A. Rupam Mahmood
A Temporal-Difference Approach to Policy Gradient Estimation. CoRR (2022)
Michael Przystupa, Masood Dehghan, Martin Jägersand, A. Rupam Mahmood
Analyzing Neural Jacobian Methods in Applications of Visual Servoing and Kinematic Control. ICRA (2021)
Shibhansh Dohare, A. Rupam Mahmood, Richard S. Sutton
Continual Backprop: Stochastic Gradient Descent with Persistent Randomness. CoRR (2021)
Alan Chan, Hugo Silva, Sungsu Lim, Tadashi Kozuno, A. Rupam Mahmood, Martha White
Greedification Operators for Policy Optimization: Investigating Forward and Reverse KL Divergences. CoRR (2021)
Michael Przystupa, Masood Dehghan, Martin Jägersand, A. Rupam Mahmood
Analyzing Neural Jacobian Methods in Applications of Visual Servoing and Kinematic Control. CoRR (2021)
Qingfeng Lan, A. Rupam Mahmood
Model-free Policy Learning with Reward Gradients. CoRR (2021)
Shivam Garg, Samuele Tosatto, Yangchen Pan, Martha White, A. Rupam Mahmood
An Alternate Policy Gradient Estimator for Softmax Policies. CoRR (2021)
Oliver Limoyo, Bryan Chan, Filip Maric, Brandon Wagstaff, A. Rupam Mahmood, Jonathan Kelly
Heteroscedastic Uncertainty for Robust Generative Latent Dynamics. IEEE Robotics Autom. Lett. 5 (4) (2020)
Oliver Limoyo, Bryan Chan, Filip Maric, Brandon Wagstaff, A. Rupam Mahmood, Jonathan Kelly
Heteroscedastic Uncertainty for Robust Generative Latent Dynamics. CoRR (2020)
Dmytro Korenkevych, A. Rupam Mahmood, Gautham Vasan, James Bergstra
Autoregressive Policies for Continuous Control Deep Reinforcement Learning. CoRR (2019)
Dmytro Korenkevych, A. Rupam Mahmood, Gautham Vasan, James Bergstra
Autoregressive Policies for Continuous Control Deep Reinforcement Learning. IJCAI (2019)
A. Rupam Mahmood, Dmytro Korenkevych, Brent J. Komer, James Bergstra
Setting up a Reinforcement Learning Task with a Real-World Robot. IROS (2018)
A. Rupam Mahmood, Dmytro Korenkevych, Brent J. Komer, James Bergstra
Setting up a Reinforcement Learning Task with a Real-World Robot. CoRR (2018)
A. Rupam Mahmood, Dmytro Korenkevych, Gautham Vasan, William Ma, James Bergstra
Benchmarking Reinforcement Learning Algorithms on Real-World Robots. CoRL (2018)
A. Rupam Mahmood, Dmytro Korenkevych, Gautham Vasan, William Ma, James Bergstra
Benchmarking Reinforcement Learning Algorithms on Real-World Robots. CoRR (2018)