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Amir-massoud Farahmand
Amir Massoud Farahmand
Person information
- affiliation: Vector Institute, Toronto, ON, Canada
- affiliation: University of Toronto, ON, Canada
- affiliation (former): Mitsubishi Electric Research Laboratories (MERL)
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2020 – today
- 2024
- [j9]Marcel Hussing, Claas Voelcker, Igor Gilitschenski, Amir-massoud Farahmand, Eric Eaton:
Dissecting Deep RL with High Update Ratios: Combatting Value Divergence. RLJ 2: 995-1018 (2024) - [j8]Claas Voelcker, Tyler Kastner, Igor Gilitschenski, Amir-massoud Farahmand:
When does Self-Prediction help? Understanding Auxiliary Tasks in Reinforcement Learning. RLJ 4: 1567-1597 (2024) - [j7]Mark Bedaywi, Amin Rakhsha, Amir-massoud Farahmand:
PID Accelerated Temporal Difference Algorithms. RLJ 5: 2071-2095 (2024) - [c43]Avery Ma, Amir-massoud Farahmand, Yangchen Pan, Philip Torr, Jindong Gu:
Improving Adversarial Transferability via Model Alignment. ECCV (62) 2024: 74-92 - [c42]Amin Rakhsha, Mete Kemertas, Mohammad Ghavamzadeh, Amir-massoud Farahmand:
Maximum Entropy Model Correction in Reinforcement Learning. ICLR 2024 - [i28]Marcel Hussing, Claas Voelcker, Igor Gilitschenski, Amir-massoud Farahmand, Eric Eaton:
Dissecting Deep RL with High Update Ratios: Combatting Value Overestimation and Divergence. CoRR abs/2403.05996 (2024) - [i27]Claas Voelcker, Tyler Kastner, Igor Gilitschenski, Amir-massoud Farahmand:
When does Self-Prediction help? Understanding Auxiliary Tasks in Reinforcement Learning. CoRR abs/2406.17718 (2024) - [i26]Mark Bedaywi, Amin Rakhsha, Amir-massoud Farahmand:
PID Accelerated Temporal Difference Algorithms. CoRR abs/2407.08803 (2024) - [i25]Jongmin Lee, Amin Rakhsha, Ernest K. Ryu, Amir-massoud Farahmand:
Deflated Dynamics Value Iteration. CoRR abs/2407.10454 (2024) - [i24]Claas Völcker, Marcel Hussing, Eric Eaton, Amir-massoud Farahmand, Igor Gilitschenski:
MAD-TD: Model-Augmented Data stabilizes High Update Ratio RL. CoRR abs/2410.08896 (2024) - 2023
- [j6]Avery Ma, Yangchen Pan, Amir-massoud Farahmand:
Understanding the robustness difference between stochastic gradient descent and adaptive gradient methods. Trans. Mach. Learn. Res. 2023 (2023) - [c41]Tyler Kastner, Murat A. Erdogdu, Amir-massoud Farahmand:
Distributional Model Equivalence for Risk-Sensitive Reinforcement Learning. NeurIPS 2023 - [i23]Claas Voelcker, Arash Ahmadian, Romina Abachi, Igor Gilitschenski, Amir-massoud Farahmand:
λ-AC: Learning latent decision-aware models for reinforcement learning in continuous state-spaces. CoRR abs/2306.17366 (2023) - [i22]Tyler Kastner, Murat A. Erdogdu, Amir-massoud Farahmand:
Distributional Model Equivalence for Risk-Sensitive Reinforcement Learning. CoRR abs/2307.01708 (2023) - [i21]Mete Kemertas, Allan D. Jepson, Amir-massoud Farahmand:
Efficient and Accurate Optimal Transport with Mirror Descent and Conjugate Gradients. CoRR abs/2307.08507 (2023) - [i20]Avery Ma, Yangchen Pan
, Amir-massoud Farahmand:
Understanding the robustness difference between stochastic gradient descent and adaptive gradient methods. CoRR abs/2308.06703 (2023) - [i19]Amin Rakhsha, Mete Kemertas, Mohammad Ghavamzadeh, Amir-massoud Farahmand:
Maximum Entropy Model Correction in Reinforcement Learning. CoRR abs/2311.17855 (2023) - [i18]Avery Ma, Amir-massoud Farahmand, Yangchen Pan
Improving Adversarial Transferability via Model Alignment. CoRR abs/2311.18495 (2023) - 2022
- [c40]Guiliang Liu, Ashutosh Adhikari, Amir-massoud Farahmand, Pascal Poupart:
Learning Object-Oriented Dynamics for Planning from Text. ICLR 2022 - [c39]Claas Voelcker, Victor Liao, Animesh Garg, Amir-massoud Farahmand:
Value Gradient weighted Model-Based Reinforcement Learning. ICLR 2022 - [c38]Amin Rakhsha, Andrew Wang, Mohammad Ghavamzadeh, Amir-massoud Farahmand:
Operator Splitting Value Iteration. NeurIPS 2022 - [c37]Yangchen Pan
Understanding and mitigating the limitations of prioritized experience replay. UAI 2022: 1561-1571 - [i17]Claas Voelcker, Victor Liao, Animesh Garg, Amir-massoud Farahmand:
Value Gradient weighted Model-Based Reinforcement Learning. CoRR abs/2204.01464 (2022) - [i16]Amin Rakhsha, Andrew Wang, Mohammad Ghavamzadeh, Amir-massoud Farahmand:
Operator Splitting Value Iteration. CoRR abs/2211.13937 (2022) - 2021
- [c36]Amir Massoud Farahmand, Mohammad Ghavamzadeh:
PID Accelerated Value Iteration Algorithm. ICML 2021: 3143-3153 - [i15]Erfan Pirmorad, Faraz Khoshbakhtian, Farnam Mansouri, Amir-massoud Farahmand:
Deep Reinforcement Learning for Online Control of Stochastic Partial Differential Equations. CoRR abs/2110.11265 (2021) - 2020
- [c35]Yangchen Pan
Frequency-based Search-control in Dyna. ICLR 2020 - [c34]Yangchen Pan
An implicit function learning approach for parametric modal regression. NeurIPS 2020 - [i14]Yangchen Pan, Jincheng Mei, Amir-massoud Farahmand:
Frequency-based Search-control in Dyna. CoRR abs/2002.05822 (2020) - [i13]Yangchen Pan, Ehsan Imani, Martha White, Amir-massoud Farahmand:
An implicit function learning approach for parametric modal regression. CoRR abs/2002.06195 (2020) - [i12]Romina Abachi, Mohammad Ghavamzadeh, Amir-massoud Farahmand:
Policy-Aware Model Learning for Policy Gradient Methods. CoRR abs/2003.00030 (2020) - [i11]Avery Ma, Fartash Faghri, Amir-massoud Farahmand:
Adversarial Robustness through Regularization: A Second-Order Approach. CoRR abs/2004.01832 (2020) - [i10]Jincheng Mei, Yangchen Pan, Martha White, Amir-massoud Farahmand, Hengshuai Yao:
Beyond Prioritized Replay: Sampling States in Model-Based RL via Simulated Priorities. CoRR abs/2007.09569 (2020) - [i9]Rodrigo Toro Icarte, Richard Anthony Valenzano, Toryn Q. Klassen, Phillip J. K. Christoffersen, Amir-massoud Farahmand, Sheila A. McIlraith:
The act of remembering: a study in partially observable reinforcement learning. CoRR abs/2010.01753 (2020)
2010 – 2019
- 2019
- [c33]Marc T. Law, Jake Snell, Amir-massoud Farahmand, Raquel Urtasun, Richard S. Zemel:
Dimensionality Reduction for Representing the Knowledge of Probabilistic Models. ICLR (Poster) 2019 - [c32]Yangchen Pan
Hill Climbing on Value Estimates for Search-control in Dyna. IJCAI 2019: 3209-3215 - [c31]Mohamed Akrout, Amir-massoud Farahmand, Tory Jarmain, Latif Abid:
Improving Skin Condition Classification with a Visual Symptom Checker Trained Using Reinforcement Learning. MICCAI (4) 2019: 549-557 - [c30]Amir-massoud Farahmand:
Value Function in Frequency Domain and the Characteristic Value Iteration Algorithm. NeurIPS 2019: 14780-14790 - [i8]Mohamed Akrout, Amir-massoud Farahmand, Tory Jarmain, Latif Abid:
Improving Skin Condition Classification with a Visual Symptom Checker Trained using Reinforcement Learning. CoRR abs/1903.03495 (2019) - [i7]Yangchen Pan, Hengshuai Yao, Amir-massoud Farahmand, Martha White:
Hill Climbing on Value Estimates for Search-control in Dyna. CoRR abs/1906.07791 (2019) - 2018
- [c29]Yangchen Pan
Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control. ICML 2018: 3983-3992 - [c28]Amir-massoud Farahmand:
Iterative Value-Aware Model Learning. NeurIPS 2018: 9090-9101 - [i6]Yangchen Pan, Amir-massoud Farahmand, Martha White, Saleh Nabi, Piyush Grover, Daniel Nikovski:
Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control. CoRR abs/1806.06931 (2018) - [i5]Mohamed Akrout, Amir-massoud Farahmand, Tory Jarmain:
Improving Skin Condition Classification with a Question Answering Model. CoRR abs/1811.06165 (2018) - 2017
- [c27]Amir Massoud Farahmand, André Barreto, Daniel Nikovski:
Value-Aware Loss Function for Model-based Reinforcement Learning. AISTATS 2017: 1486-1494 - [c26]Amir-massoud Farahmand, Saleh Nabi, Daniel Nikolaev Nikovski:
Deep reinforcement learning for partial differential equation control. ACC 2017: 3120-3127 - [c25]Amir-massoud Farahmand, Sepideh Pourazarm, Daniel Nikovski:
Random Projection Filter Bank for Time Series Data. NIPS 2017: 6562-6572 - [c24]Devesh K. Jha, Daniel Nikovski, William Yerazunis, Amir-massoud Farahmand:
Learning to regulate rolling ball motion. SSCI 2017: 1-6 - [i4]Kota Hara, Ming-Yu Liu, Oncel Tuzel, Amir-massoud Farahmand:
Attentional Network for Visual Object Detection. CoRR abs/1702.01478 (2017) - 2016
- [j5]Amir-massoud Farahmand, Mohammad Ghavamzadeh, Csaba Szepesvári, Shie Mannor:
Regularized Policy Iteration with Nonparametric Function Spaces. J. Mach. Learn. Res. 17: 139:1-139:66 (2016) - [c23]Amir-massoud Farahmand, Daniel Nikolaev Nikovski, Yuji Igarashi, Hiroki Konaka:
Truncated Approximate Dynamic Programming with Task-Dependent Terminal Value. AAAI 2016: 3123-3129 - [c22]Mouhacine Benosman, Amir-massoud Farahmand, Meng Xia:
Learning-based modular indirect adaptive control for a class of nonlinear systems. ACC 2016: 733-738 - [c21]Amir-massoud Farahmand, Saleh Nabi, Piyush Grover
Learning to control partial differential equations: Regularized Fitted Q-Iteration approach. CDC 2016: 4578-4585 - 2015
- [j4]Stefano V. Albrecht, André da Motta Salles Barreto, Darius Braziunas, David L. Buckeridge, Heriberto Cuayáhuitl, Nina Dethlefs, Markus Endres
Reports of the AAAI 2014 Conference Workshops. AI Mag. 36(1): 87-98 (2015) - [j3]Amir-massoud Farahmand, Doina Precup, André da Motta Salles Barreto, Mohammad Ghavamzadeh:
Classification-Based Approximate Policy Iteration. IEEE Trans. Autom. Control. 60(11): 2989-2993 (2015) - [c20]De-An Huang, Amir-massoud Farahmand, Kris M. Kitani, James Andrew Bagnell:
Approximate MaxEnt Inverse Optimal Control and Its Application for Mental Simulation of Human Interactions. AAAI 2015: 2673-2679 - [i3]Mouhacine Benosman, Amir-massoud Farahmand, Meng Xia:
Learning-Based Modular Indirect Adaptive Control for a Class of Nonlinear Systems. CoRR abs/1509.07860 (2015) - 2014
- [c19]Philip Bachman, Amir-massoud Farahmand, Doina Precup:
Sample-based approximate regularization. ICML 2014: 1926-1934 - [i2]Amir-massoud Farahmand, Doina Precup, André da Motta Salles Barreto, Mohammad Ghavamzadeh:
Classification-based Approximate Policy Iteration: Experiments and Extended Discussions. CoRR abs/1407.0449 (2014) - 2013
- [c18]Beomjoon Kim, Amir-massoud Farahmand, Joelle Pineau, Doina Precup:
Learning from Limited Demonstrations. NIPS 2013: 2859-2867 - [c17]Mahdi Milani Fard, Yuri Grinberg, Amir-massoud Farahmand, Joelle Pineau, Doina Precup:
Bellman Error Based Feature Generation using Random Projections on Sparse Spaces. NIPS 2013: 3030-3038 - 2012
- [c16]Amir Massoud Farahmand, Doina Precup:
Value Pursuit Iteration. NIPS 2012: 1349-1357 - [i1]Mahdi Milani Fard, Yuri Grinberg, Amir Massoud Farahmand, Joelle Pineau, Doina Precup:
Bellman Error Based Feature Generation using Random Projections on Sparse Spaces. CoRR abs/1207.5554 (2012) - 2011
- [j2]Amir Massoud Farahmand, Csaba Szepesvári:
Model selection in reinforcement learning. Mach. Learn. 85(3): 299-332 (2011) - [c15]Amir Massoud Farahmand:
Action-Gap Phenomenon in Reinforcement Learning. NIPS 2011: 172-180 - 2010
- [j1]Amir Massoud Farahmand, Majid Nili Ahmadabadi, Caro Lucas, Babak Nadjar Araabi
Interaction of Culture-Based Learning and Cooperative Co-Evolution and its Application to Automatic Behavior-Based System Design. IEEE Trans. Evol. Comput. 14(1): 23-57 (2010) - [c14]Azad Shademan, Amir Massoud Farahmand, Martin Jägersand:
Robust Jacobian estimation for uncalibrated visual servoing. ICRA 2010: 5564-5569 - [c13]Amir Massoud Farahmand, Rémi Munos, Csaba Szepesvári:
Error Propagation for Approximate Policy and Value Iteration. NIPS 2010: 568-576
2000 – 2009
- 2009
- [c12]Amir Massoud Farahmand, Mohammad Ghavamzadeh, Csaba Szepesvári, Shie Mannor
Regularized Fitted Q-Iteration for planning in continuous-space Markovian decision problems. ACC 2009: 725-730 - [c11]Azad Shademan, Amir Massoud Farahmand, Martin Jägersand:
Towards Learning Robotic Reaching and Pointing: An Uncalibrated Visual Servoing Approach. CRV 2009: 229-236 - [c10]Amir Massoud Farahmand, Azad Shademan, Martin Jägersand, Csaba Szepesvári:
Model-based and model-free reinforcement learning for visual servoing. ICRA 2009: 2917-2924 - 2008
- [c9]Amir Massoud Farahmand, Mohammad Ghavamzadeh, Csaba Szepesvári, Shie Mannor
Regularized Fitted Q-Iteration: Application to Planning. EWRL 2008: 55-68 - [c8]Amir Massoud Farahmand, Mohammad Ghavamzadeh, Csaba Szepesvári, Shie Mannor:
Regularized Policy Iteration. NIPS 2008: 441-448 - 2007
- [c7]Amir Massoud Farahmand, Csaba Szepesvári, Jean-Yves Audibert:
Manifold-adaptive dimension estimation. ICML 2007: 265-272 - [c6]Amir Massoud Farahmand, Azad Shademan, Martin Jägersand:
Global visual-motor estimation for uncalibrated visual servoing. IROS 2007: 1969-1974 - 2006
- [c5]Amir Massoud Farahmand, Majid Nili Ahmadabadi, Caro Lucas, Babak Nadjar Araabi
Hybrid Behavior Co-evolution and Structure Learning in Behavior-based Systems. IEEE Congress on Evolutionary Computation 2006: 275-282 - [c4]Mohammad G. Azar, Majid Nili Ahmadabadi, Amir Massoud Farahmand, Babak Nadjar Araabi
Learning to Coordinate Behaviors in Soft Behavior-Based Systems Using Reinforcement Learning. IJCNN 2006: 241-248 - [c3]Amir Massoud Farahmand, Mohammad Javad Yazdanpanah
Channel Assignment using Chaotic Simulated Annealing Enhanced Hopfield Neural Network. IJCNN 2006: 4491-4497 - 2005
- [c2]Amir Massoud Farahmand, Mohammad Javad Yazdanpanah
Locally Optimal Takagi-Sugeno Fuzzy Controllers. CDC/ECC 2005: 4095-4099 - 2004
- [c1]Amir Massoud Farahmand, Majid Nili Ahmadabadi, Babak Nadjar Araabi:
Behavior hierarchy learning in a behavior-based system using reinforcement learning. IROS 2004: 2050-2055
Coauthor Index
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