Farahmand, Amir-massoud

39 publications

ICLR 2025 A Truncated Newton Method for Optimal Transport Mete Kemertas, Amir-massoud Farahmand, Allan Douglas Jepson

ICML 2025 Calibrated Value-Aware Model Learning with Probabilistic Environment Models Claas A Voelcker, Anastasiia Pedan, Arash Ahmadian, Romina Abachi, Igor Gilitschenski, Amir-Massoud Farahmand

ICML 2025 Categorical Distributional Reinforcement Learning with Kullback-Leibler Divergence: Convergence and Asymptotics Tyler Kastner, Mark Rowland, Yunhao Tang, Murat A Erdogdu, Amir-Massoud Farahmand

TMLR 2025 Deflated Dynamics Value Iteration Jongmin Lee, Amin Rakhsha, Ernest K. Ryu, Amir-massoud Farahmand

TMLR 2025 Efficient and Accurate Optimal Transport with Mirror Descent and Conjugate Gradients Mete Kemertas, Allan Douglas Jepson, Amir-massoud Farahmand

ICLR 2025 MAD-TD: Model-Augmented Data Stabilizes High Update Ratio RL Claas A Voelcker, Marcel Hussing, Eric Eaton, Amir-massoud Farahmand, Igor Gilitschenski

NeurIPS 2025 Majority of the Bests: Improving Best-of-N via Bootstrapping Amin Rakhsha, Kanika Madan, Tianyu Zhang, Amir-massoud Farahmand, Amir Khasahmadi

ICML 2025 PANDAS: Improving Many-Shot Jailbreaking via Positive Affirmation, Negative Demonstration, and Adaptive Sampling Avery Ma, Yangchen Pan, Amir-Massoud Farahmand

ECCV 2024 Improving Adversarial Transferability via Model Alignment Avery Ma, Amir-massoud Farahmand, Yangchen Pan, Philip Torr, Jindong Gu

ICLR 2024 Maximum Entropy Model Correction in Reinforcement Learning Amin Rakhsha, Mete Kemertas, Mohammad Ghavamzadeh, Amir-massoud Farahmand

NeurIPS 2023 Distributional Model Equivalence for Risk-Sensitive Reinforcement Learning Tyler Kastner, Murat A Erdogdu, Amir-massoud Farahmand

TMLR 2023 Understanding the Robustness Difference Between Stochastic Gradient Descent and Adaptive Gradient Methods Avery Ma, Yangchen Pan, Amir-massoud Farahmand

ICLR 2022 Learning Object-Oriented Dynamics for Planning from Text Guiliang Liu, Ashutosh Adhikari, Amir-massoud Farahmand, Pascal Poupart

NeurIPS 2022 Operator Splitting Value Iteration Amin Rakhsha, Andrew Wang, Mohammad Ghavamzadeh, Amir-massoud Farahmand

UAI 2022 Understanding and Mitigating the Limitations of Prioritized Experience Replay Yangchen Pan, Jincheng Mei, Amir-massoud Farahmand, Martha White, Hengshuai Yao, Mohsen Rohani, Jun Luo

ICMLW 2022 VIPer: Iterative Value-Aware Model Learning on the Value Improvement Path Romina Abachi, Claas A Voelcker, Animesh Garg, Amir-massoud Farahmand

ICLR 2022 Value Gradient Weighted Model-Based Reinforcement Learning Claas A Voelcker, Victor Liao, Animesh Garg, Amir-massoud Farahmand

NeurIPSW 2021 Deep Reinforcement Learning for Online Control of Stochastic Partial Differential Equations Erfan Pirmorad, Faraz Khoshbakhtian, Farnam Mansouri, Amir-massoud Farahmand

ICML 2021 PID Accelerated Value Iteration Algorithm Amir-Massoud Farahmand, Mohammad Ghavamzadeh

ICLR 2020 An Implicit Function Learning Approach for Parametric Modal Regression Yangchen Pan, Ehsan Imani, Martha White, Amir-massoud Farahmand

NeurIPS 2020 An Implicit Function Learning Approach for Parametric Modal Regression Yangchen Pan, Ehsan Imani, Amir-massoud Farahmand, Martha White

ICLR 2020 Frequency-Based Search-Control in Dyna Yangchen Pan, Jincheng Mei, Amir-massoud Farahmand

ICLR 2019 Dimensionality Reduction for Representing the Knowledge of Probabilistic Models Marc T Law, Jake Snell, Amir-massoud Farahmand, Raquel Urtasun, Richard S Zemel

IJCAI 2019 Hill Climbing on Value Estimates for Search-Control in Dyna Yangchen Pan, Hengshuai Yao, Amir-massoud Farahmand, Martha White

NeurIPS 2019 Value Function in Frequency Domain and the Characteristic Value Iteration Algorithm Amir-massoud Farahmand

NeurIPS 2018 Iterative Value-Aware Model Learning Amir-massoud Farahmand

ICML 2018 Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control Yangchen Pan, Amir-massoud Farahmand, Martha White, Saleh Nabi, Piyush Grover, Daniel Nikovski

NeurIPS 2017 Random Projection Filter Bank for Time Series Data Amir-massoud Farahmand, Sepideh Pourazarm, Daniel Nikovski

AISTATS 2017 Value-Aware Loss Function for Model-Based Reinforcement Learning Amir Massoud Farahmand, André Barreto, Daniel Nikovski

JMLR 2016 Regularized Policy Iteration with Nonparametric Function Spaces Amir-massoud Farahmand, Mohammad Ghavamzadeh, Csaba Szepesvári, Shie Mannor

AAAI 2016 Truncated Approximate Dynamic Programming with Task-Dependent Terminal Value Amir-massoud Farahmand, Daniel Nikolaev Nikovski, Yuji Igarashi, Hiroki Konaka

AAAI 2015 Approximate MaxEnt Inverse Optimal Control and Its Application for Mental Simulation of Human Interactions De-An Huang, Amir-massoud Farahmand, Kris M. Kitani, James Andrew Bagnell

ICML 2014 Sample-Based Approximate Regularization Philip Bachman, Amir-Massoud Farahmand, Doina Precup

NeurIPS 2013 Bellman Error Based Feature Generation Using Random Projections on Sparse Spaces Mahdi Milani Fard, Yuri Grinberg, Amir-massoud Farahmand, Joelle Pineau, Doina Precup

NeurIPS 2013 Learning from Limited Demonstrations Beomjoon Kim, Amir-massoud Farahmand, Joelle Pineau, Doina Precup

NeurIPS 2011 Action-Gap Phenomenon in Reinforcement Learning Amir-massoud Farahmand

MLJ 2011 Model Selection in Reinforcement Learning Amir Massoud Farahmand, Csaba Szepesvári

NeurIPS 2010 Error Propagation for Approximate Policy and Value Iteration Amir-massoud Farahmand, Csaba Szepesvári, Rémi Munos

ICML 2007 Manifold-Adaptive Dimension Estimation Amir Massoud Farahmand, Csaba Szepesvári, Jean-Yves Audibert