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ORCIDA. Rupam Mahmood
Rupam Mahmood – Ashique Rupam Mahmood
Person information
- affiliation: University of Alberta, Reinforcement Learning & Artificial Intelligence Lab, Edmonton, AB, Canada
- affiliation: Alberta Machine Intelligence Institute (Amii), Edmonton, AB, Canada
- affiliation: Kindred AI, Toronto, ON, Canada
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2020 – today
- 2025
[i39]Fengdi Che, Bryan Chan, Chen Ma, A. Rupam Mahmood:
AVG-DICE: Stationary Distribution Correction by Regression. CoRR abs/2503.02125 (2025)- [i38]Jiamin He, A. Rupam Mahmood, Martha White:
Distribution Parameter Actor-Critic: Shifting the Agent-Environment Boundary for Diverse Action Spaces. CoRR abs/2506.16608 (2025) - 2024
- [j11]Qingfeng Lan, A. Rupam Mahmood, Shuicheng Yan, Zhongwen Xu:
Learning to Optimize for Reinforcement Learning. RLJ 2: 481-497 (2024) - [j10]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. RLJ 3: 1211-1235 (2024) - [j9]Gautham Vasan, Yan Wang, Fahim Shahriar, James Bergstra, Martin Jägersand, A. Rupam Mahmood:
Revisiting Sparse Rewards for Goal-Reaching Reinforcement Learning. RLJ 4: 1841-1854 (2024) - [j8]Mohamed Elsayed, Qingfeng Lan, Clare Lyle, A. Rupam Mahmood:
Weight Clipping for Deep Continual and Reinforcement Learning. RLJ 5: 2198-2217 (2024) - [j7]Shibhansh Dohare
, J. Fernando Hernandez-Garcia, Qingfeng Lan, Parash Rahman, A. Rupam Mahmood, Richard S. Sutton:
Loss of plasticity in deep continual learning. Nat. 632(8026): 768-774 (2024) - [c27]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: 733-742 - [c26]Mohamed Elsayed, A. Rupam Mahmood:
Addressing Loss of Plasticity and Catastrophic Forgetting in Continual Learning. ICLR 2024 - [c25]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 - [c24]Mohamed Elsayed, Homayoon Farrahi, Felix Dangel, A. Rupam Mahmood:
Revisiting Scalable Hessian Diagonal Approximations for Applications in Reinforcement Learning. ICML 2024 - [c23]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. ICML 2024 - [c22]Gautham Vasan, Mohamed Elsayed, Seyed Alireza Azimi, Jiamin He, Fahim Shahriar, Colin Bellinger, Martha White, Rupam Mahmood:
Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers. NeurIPS 2024 - [i37]Mohamed Elsayed, A. Rupam Mahmood:
Addressing Loss of Plasticity and Catastrophic Forgetting in Continual Learning. CoRR abs/2404.00781 (2024) - [i36]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 abs/2405.21043 (2024) - [i35]Mohamed Elsayed, Homayoon Farrahi, Felix Dangel, A. Rupam Mahmood:
Revisiting Scalable Hessian Diagonal Approximations for Applications in Reinforcement Learning. CoRR abs/2406.03276 (2024) - [i34]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 abs/2406.12241 (2024) - [i33]Gautham Vasan, Yan Wang, Fahim Shahriar, James Bergstra, Martin Jägersand, A. Rupam Mahmood:
Revisiting Sparse Rewards for Goal-Reaching Reinforcement Learning. CoRR abs/2407.00324 (2024) - [i32]Mohamed Elsayed, Qingfeng Lan, Clare Lyle, A. Rupam Mahmood:
Weight Clipping for Deep Continual and Reinforcement Learning. CoRR abs/2407.01704 (2024) - [i31]Mohamed Elsayed, Gautham Vasan, A. Rupam Mahmood:
Streaming Deep Reinforcement Learning Finally Works. CoRR abs/2410.14606 (2024) - [i30]Gautham Vasan, Mohamed Elsayed, Alireza Azimi, Jiamin He, Fahim Shahriar, Colin Bellinger, Martha White, A. Rupam Mahmood:
Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers. CoRR abs/2411.15370 (2024) - 2023
- [j6]Qingfeng Lan, Yangchen Pan, Jun Luo, A. Rupam Mahmood:
Memory-efficient Reinforcement Learning with Value-based Knowledge Consolidation. Trans. Mach. Learn. Res. 2023 (2023) - [c21]Fengdi Che, Gautham Vasan, A. Rupam Mahmood:
Correcting discount-factor mismatch in on-policy policy gradient methods. ICML 2023: 4218-4240 - [c20]Yan Wang, Gautham Vasan, A. Rupam Mahmood:
Real-Time Reinforcement Learning for Vision-Based Robotics Utilizing Local and Remote Computers. ICRA 2023: 9435-9441 - [c19]Homayoon Farrahi, A. Rupam Mahmood:
Reducing the Cost of Cycle-Time Tuning for Real-World Policy Optimization. IJCNN 2023: 1-8 - [c18]Amirmohammad Karimi, Jun Jin, Jun Luo, A. Rupam Mahmood, Martin Jägersand, Samuele Tosatto:
Dynamic Decision Frequency with Continuous Options. IROS 2023: 7545-7552 - [c17]Jiamin He, Fengdi Che, Yi Wan, A. Rupam Mahmood:
Loosely consistent emphatic temporal-difference learning. UAI 2023: 849-859 - [i29]Qingfeng Lan, A. Rupam Mahmood, Shuicheng Yan, Zhongwen Xu:
Learning to Optimize for Reinforcement Learning. CoRR abs/2302.01470 (2023) - [i28]Mohamed Elsayed, A. Rupam Mahmood:
Utility-based Perturbed Gradient Descent: An Optimizer for Continual Learning. CoRR abs/2302.03281 (2023) - [i27]Homayoon Farrahi, A. Rupam Mahmood:
Reducing the Cost of Cycle-Time Tuning for Real-World Policy Optimization. CoRR abs/2305.05760 (2023) - [i26]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 abs/2305.18246 (2023) - [i25]Fengdi Che, Gautham Vasan, A. Rupam Mahmood:
Correcting discount-factor mismatch in on-policy policy gradient methods. CoRR abs/2306.13284 (2023) - [i24]Shibhansh Dohare, J. Fernando Hernandez-Garcia, Parash Rahman, Richard S. Sutton, A. Rupam Mahmood:
Maintaining Plasticity in Deep Continual Learning. CoRR abs/2306.13812 (2023) - [i23]Qingfeng Lan, A. Rupam Mahmood:
Elephant Neural Networks: Born to Be a Continual Learner. CoRR abs/2310.01365 (2023) - [i22]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 abs/2312.15339 (2023) - 2022
- [j5]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: 253:1-253:79 (2022) - [c16]Qingfeng Lan, Samuele Tosatto, Homayoon Farrahi, Rupam Mahmood:
Model-free Policy Learning with Reward Gradients. AISTATS 2022: 4217-4234 - [c15]Shivam Garg, Samuele Tosatto, Yangchen Pan, Martha White, Rupam Mahmood:
An Alternate Policy Gradient Estimator for Softmax Policies. AISTATS 2022: 6630-6689 - [c14]Samuele Tosatto, Andrew Patterson, Martha White, Rupam Mahmood:
A Temporal-Difference Approach to Policy Gradient Estimation. ICML 2022: 21609-21632 - [c13]Yufeng Yuan, A. Rupam Mahmood:
Asynchronous Reinforcement Learning for Real-Time Control of Physical Robots. ICRA 2022: 5546-5552 - [i21]Samuele Tosatto, Andrew Patterson, Martha White, A. Rupam Mahmood:
A Temporal-Difference Approach to Policy Gradient Estimation. CoRR abs/2202.02396 (2022) - [i20]Yufeng Yuan, Rupam Mahmood:
Asynchronous Reinforcement Learning for Real-Time Control of Physical Robots. CoRR abs/2203.12759 (2022) - [i19]Qingfeng Lan, Yangchen Pan, Jun Luo, A. Rupam Mahmood:
Memory-efficient Reinforcement Learning with Knowledge Consolidation. CoRR abs/2205.10868 (2022) - [i18]Yan Wang, Gautham Vasan, A. Rupam Mahmood:
Real-Time Reinforcement Learning for Vision-Based Robotics Utilizing Local and Remote Computers. CoRR abs/2210.02317 (2022) - [i17]Mohamed Elsayed, A. Rupam Mahmood:
HesScale: Scalable Computation of Hessian Diagonals. CoRR abs/2210.11639 (2022) - [i16]Amirmohammad Karimi, Jun Jin, Jun Luo, A. Rupam Mahmood, Martin Jägersand, Samuele Tosatto:
Variable-Decision Frequency Option Critic. CoRR abs/2212.04407 (2022) - 2021
- [c12]Michael Przystupa, Masood Dehghan, Martin Jägersand, A. Rupam Mahmood:
Analyzing Neural Jacobian Methods in Applications of Visual Servoing and Kinematic Control. ICRA 2021: 14276-14283 - [i15]Qingfeng Lan, A. Rupam Mahmood:
Model-free Policy Learning with Reward Gradients. CoRR abs/2103.05147 (2021) - [i14]Michael Przystupa, Masood Dehghan, Martin Jägersand, A. Rupam Mahmood:
Analyzing Neural Jacobian Methods in Applications of Visual Servoing and Kinematic Control. CoRR abs/2106.06083 (2021) - [i13]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 abs/2107.08285 (2021) - [i12]Shibhansh Dohare, A. Rupam Mahmood, Richard S. Sutton:
Continual Backprop: Stochastic Gradient Descent with Persistent Randomness. CoRR abs/2108.06325 (2021) - [i11]Shivam Garg, Samuele Tosatto, Yangchen Pan, Martha White, A. Rupam Mahmood:
An Alternate Policy Gradient Estimator for Softmax Policies. CoRR abs/2112.11622 (2021) - 2020
- [j4]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): 6654-6661 (2020) - [i10]Oliver Limoyo, Bryan Chan, Filip Maric, Brandon Wagstaff, A. Rupam Mahmood, Jonathan Kelly:
Heteroscedastic Uncertainty for Robust Generative Latent Dynamics. CoRR abs/2008.08157 (2020)
2010 – 2019
- 2019
- [c11]Dmytro Korenkevych, A. Rupam Mahmood, Gautham Vasan, James Bergstra:
Autoregressive Policies for Continuous Control Deep Reinforcement Learning. IJCAI 2019: 2754-2762 - [i9]Dmytro Korenkevych, A. Rupam Mahmood, Gautham Vasan, James Bergstra:
Autoregressive Policies for Continuous Control Deep Reinforcement Learning. CoRR abs/1903.11524 (2019) - 2018
- [j3]Huizhen Yu, Ashique Rupam Mahmood, Richard S. Sutton:
On Generalized Bellman Equations and Temporal-Difference Learning. J. Mach. Learn. Res. 19: 48:1-48:49 (2018) - [c10]A. Rupam Mahmood, Dmytro Korenkevych, Gautham Vasan, William Ma, James Bergstra:
Benchmarking Reinforcement Learning Algorithms on Real-World Robots. CoRL 2018: 561-591 - [c9]A. Rupam Mahmood, Dmytro Korenkevych, Brent J. Komer, James Bergstra:
Setting up a Reinforcement Learning Task with a Real-World Robot. IROS 2018: 4635-4640 - [i8]A. Rupam Mahmood, Dmytro Korenkevych, Brent J. Komer, James Bergstra:
Setting up a Reinforcement Learning Task with a Real-World Robot. CoRR abs/1803.07067 (2018) - [i7]A. Rupam Mahmood, Dmytro Korenkevych, Gautham Vasan, William Ma, James Bergstra:
Benchmarking Reinforcement Learning Algorithms on Real-World Robots. CoRR abs/1809.07731 (2018) - 2017
- [c8]Huizhen Yu, Ashique Rupam Mahmood, Richard S. Sutton:
On Generalized Bellman Equations and Temporal-Difference Learning. Canadian AI 2017: 3-14 - [i6]Ashique Rupam Mahmood, Huizhen Yu, Richard S. Sutton:
Multi-step Off-policy Learning Without Importance Sampling Ratios. CoRR abs/1702.03006 (2017) - [i5]Huizhen Yu, Ashique Rupam Mahmood, Richard S. Sutton:
On Generalized Bellman Equations and Temporal-Difference Learning. CoRR abs/1704.04463 (2017) - 2016
- [j2]Richard S. Sutton, Ashique Rupam Mahmood, Martha White:
An Emphatic Approach to the Problem of Off-policy Temporal-Difference Learning. J. Mach. Learn. Res. 17: 73:1-73:29 (2016) - [j1]Harm van Seijen, Ashique Rupam Mahmood, Patrick M. Pilarski, Marlos C. Machado, Richard S. Sutton:
True Online Temporal-Difference Learning. J. Mach. Learn. Res. 17: 145:1-145:40 (2016) - 2015
- [c7]Ashique Rupam Mahmood, Richard S. Sutton:
Off-policy learning based on weighted importance sampling with linear computational complexity. UAI 2015: 552-561 - [i4]Richard S. Sutton, Ashique Rupam Mahmood, Martha White:
An Emphatic Approach to the Problem of Off-policy Temporal-Difference Learning. CoRR abs/1503.04269 (2015) - [i3]Harm van Seijen, Ashique Rupam Mahmood, Patrick M. Pilarski, Richard S. Sutton:
An Empirical Evaluation of True Online TD(λ). CoRR abs/1507.00353 (2015) - [i2]Ashique Rupam Mahmood, Huizhen Yu, Martha White, Richard S. Sutton:
Emphatic Temporal-Difference Learning. CoRR abs/1507.01569 (2015) - [i1]Harm van Seijen, Ashique Rupam Mahmood, Patrick M. Pilarski, Marlos C. Machado, Richard S. Sutton:
True Online Temporal-Difference Learning. CoRR abs/1512.04087 (2015) - 2014
- [c6]Richard S. Sutton, Ashique Rupam Mahmood, Doina Precup, Hado van Hasselt:
A new Q(lambda) with interim forward view and Monte Carlo equivalence. ICML 2014: 568-576 - [c5]Ashique Rupam Mahmood, Hado van Hasselt, Richard S. Sutton:
Weighted importance sampling for off-policy learning with linear function approximation. NIPS 2014: 3014-3022 - [c4]Hado van Hasselt, Ashique Rupam Mahmood, Richard S. Sutton:
Off-policy TD( l) with a true online equivalence. UAI 2014: 330-339 - 2013
- [c3]Ashique Rupam Mahmood, Richard S. Sutton:
Representation Search through Generate and Test. AAAI Workshop: Learning Rich Representations from Low-Level Sensors 2013 - [c2]Ashique Rupam Mahmood, Richard S. Sutton:
Position Paper: Representation Search through Generate and Test. SARA 2013 - 2012
- [c1]Ashique Rupam Mahmood, Richard S. Sutton, Thomas Degris, Patrick M. Pilarski:
Tuning-free step-size adaptation. ICASSP 2012: 2121-2124
Coauthor Index
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