White, Martha

82 publications

ICLR 2025 $q$-Exponential Family for Policy Optimization Lingwei Zhu, Haseeb Shah, Han Wang, Yukie Nagai, Martha White

ICML 2025 Position: Lifetime Tuning Is Incompatible with Continual Reinforcement Learning Golnaz Mesbahi, Parham Mohammad Panahi, Olya Mastikhina, Steven Tang, Martha White, Adam White

ICML 2024 Averaging $n$-Step Returns Reduces Variance in Reinforcement Learning Brett Daley, Martha White, Marlos C. Machado

JMLR 2024 Data-Efficient Policy Evaluation Through Behavior Policy Search Josiah P. Hanna, Yash Chandak, Philip S. Thomas, Martha White, Peter Stone, Scott Niekum

NeurIPS 2024 Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers Gautham Vasan, Mohamed Elsayed, Alireza Azimi, Jiamin He, Fahim Shariar, Colin Bellinger, Martha White, A. Rupam Mahmood

JMLR 2024 Empirical Design in Reinforcement Learning Andrew Patterson, Samuel Neumann, Martha White, Adam White

AAAI 2024 Exploiting Action Impact Regularity and Exogenous State Variables for Offline Reinforcement Learning (Abstract Reprint) Vincent Liu, James R. Wright, Martha White

MLJ 2024 GVFs in the Real World: Making Predictions Online for Water Treatment Muhammad Kamran Janjua, Haseeb Shah, Martha White, Erfan Miahi, Marlos C. Machado, Adam White

JMLR 2024 Goal-Space Planning with Subgoal Models Chunlok Lo, Kevin Roice, Parham Mohammad Panahi, Scott M. Jordan, Adam White, Gabor Mihucz, Farzane Aminmansour, Martha White

JAIR 2024 Mitigating Value Hallucination in Dyna-Style Planning via Multistep Predecessor Models Farzane Aminmansour, Taher Jafferjee, Ehsan Imani, Erin J. Talvitie, Michael Bowling, Martha White

TMLR 2024 Offline Reinforcement Learning via Tsallis Regularization Lingwei Zhu, Matthew Kyle Schlegel, Han Wang, Martha White

ICML 2024 Position: Benchmarking Is Limited in Reinforcement Learning Research Scott M. Jordan, Adam White, Bruno Castro Da Silva, Martha White, Philip S. Thomas

NeurIPS 2024 Real-Time Recurrent Learning Using Trace Units in Reinforcement Learning Esraa Elelimy, Adam White, Michael Bowling, Martha White

AISTATS 2023 Asymptotically Unbiased Off-Policy Policy Evaluation When Reusing Old Data in Nonstationary Environments Vincent Liu, Yash Chandak, Philip Thomas, Martha White

JAIR 2023 Exploiting Action Impact Regularity and Exogenous State Variables for Offline Reinforcement Learning Vincent Liu, James R. Wright, Martha White

NeurIPS 2023 General Munchausen Reinforcement Learning with Tsallis Kullback-Leibler Divergence Lingwei Zhu, Zheng Chen, Matthew Schlegel, Martha White

ICLR 2023 Greedy Actor-Critic: A New Conditional Cross-Entropy Method for Policy Improvement Samuel Neumann, Sungsu Lim, Ajin George Joseph, Yangchen Pan, Adam White, Martha White

TMLR 2023 Investigating Action Encodings in Recurrent Neural Networks in Reinforcement Learning Matthew Kyle Schlegel, Volodymyr Tkachuk, Adam M White, Martha White

CoLLAs 2023 Measuring and Mitigating Interference in Reinforcement Learning Vincent Liu, Han Wang, Ruo Yu Tao, Khurram Javed, Adam White, Martha White

JMLR 2023 Off-Policy Actor-Critic with Emphatic Weightings Eric Graves, Ehsan Imani, Raksha Kumaraswamy, Martha White

TMLR 2023 Resmax: An Alternative Soft-Greedy Operator for Reinforcement Learning Erfan Miahi, Revan MacQueen, Alex Ayoub, Abbas Masoumzadeh, Martha White

JMLR 2023 Scalable Real-Time Recurrent Learning Using Columnar-Constructive Networks Khurram Javed, Haseeb Shah, Richard S. Sutton, Martha White

ICLR 2023 The In-Sample SoftMax for Offline Reinforcement Learning Chenjun Xiao, Han Wang, Yangchen Pan, Adam White, Martha White

ICML 2023 Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning Brett Daley, Martha White, Christopher Amato, Marlos C. Machado

AISTATS 2022 An Alternate Policy Gradient Estimator for SoftMax Policies Shivam Garg, Samuele Tosatto, Yangchen Pan, Martha White, Rupam Mahmood

JMLR 2022 A Generalized Projected Bellman Error for Off-Policy Value Estimation in Reinforcement Learning Andrew Patterson, Adam White, Martha White

ICML 2022 A Temporal-Difference Approach to Policy Gradient Estimation Samuele Tosatto, Andrew Patterson, Martha White, Rupam Mahmood

JMLR 2022 Greedification Operators for Policy Optimization: Investigating Forward and Reverse KL Divergences Alan Chan, Hugo Silva, Sungsu Lim, Tadashi Kozuno, A. Rupam Mahmood, Martha White

TMLR 2022 No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL Han Wang, Archit Sakhadeo, Adam M White, James M Bell, Vincent Liu, Xutong Zhao, Puer Liu, Tadashi Kozuno, Alona Fyshe, Martha White

TMLR 2022 Representation Alignment in Neural Networks Ehsan Imani, Wei Hu, Martha White

ICLR 2022 Resonance in Weight Space: Covariate Shift Can Drive Divergence of SGD with Momentum Kirby Banman, Garnet Liam Peet-Pare, Nidhi Hegde, Alona Fyshe, Martha White

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

NeurIPS 2021 Continual Auxiliary Task Learning Matthew McLeod, Chunlok Lo, Matthew Schlegel, Andrew Jacobsen, Raksha Kumaraswamy, Martha White, Adam White

ICLR 2021 Fuzzy Tiling Activations: A Simple Approach to Learning Sparse Representations Online Yangchen Pan, Kirby Banman, Martha White

JAIR 2021 General Value Function Networks Matthew Schlegel, Andrew Jacobsen, Zaheer Abbas, Andrew Patterson, Adam White, Martha White

NeurIPS 2021 Structural Credit Assignment in Neural Networks Using Reinforcement Learning Dhawal Gupta, Gabor Mihucz, Matthew Schlegel, James Kostas, Philip S. Thomas, Martha White

JAIR 2020 Adapting Behavior via Intrinsic Reward: A Survey and Empirical Study Cam Linke, Nadia M. Ady, Martha White, Thomas Degris, Adam White

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

ICML 2020 Gradient Temporal-Difference Learning with Regularized Corrections Sina Ghiassian, Andrew Patterson, Shivam Garg, Dhawal Gupta, Adam White, Martha White

ICLR 2020 Maxmin Q-Learning: Controlling the Estimation Bias of Q-Learning Qingfeng Lan, Yangchen Pan, Alona Fyshe, Martha White

ICML 2020 Optimizing for the Future in Non-Stationary MDPs Yash Chandak, Georgios Theocharous, Shiv Shankar, Martha White, Sridhar Mahadevan, Philip Thomas

ICMLW 2020 Optimizing for the Future in Non-Stationary MDPs Yash Chandak, Georgios Theocharous, Shiv Shankar, Martha White, Sridhar Mahadevan, Philip S. Thomas

ICML 2020 Selective Dyna-Style Planning Under Limited Model Capacity Zaheer Abbas, Samuel Sokota, Erin Talvitie, Martha White

NeurIPS 2020 Towards Safe Policy Improvement for Non-Stationary MDPs Yash Chandak, Scott Jordan, Georgios Theocharous, Martha White, Philip S. Thomas

ICLR 2020 Training Recurrent Neural Networks Online by Learning Explicit State Variables Somjit Nath, Vincent Liu, Alan Chan, Xin Li, Adam White, Martha White

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

NeurIPS 2019 Importance Resampling for Off-Policy Prediction Matthew Schlegel, Wesley Chung, Daniel Graves, Jian Qian, Martha White

NeurIPS 2019 Learning Macroscopic Brain Connectomes via Group-Sparse Factorization Farzane Aminmansour, Andrew Patterson, Lei Le, Yisu Peng, Daniel Mitchell, Franco Pestilli, Cesar F. Caiafa, Russell Greiner, Martha White

AAAI 2019 Meta-Descent for Online, Continual Prediction Andrew Jacobsen, Matthew Schlegel, Cameron Linke, Thomas Degris, Adam White, Martha White

NeurIPS 2019 Meta-Learning Representations for Continual Learning Khurram Javed, Martha White

IJCAI 2019 Planning with Expectation Models Yi Wan, Muhammad Zaheer, Adam White, Martha White, Richard S. Sutton

AAAI 2019 The Utility of Sparse Representations for Control in Reinforcement Learning Vincent Liu, Raksha Kumaraswamy, Lei Le, Martha White

ICLR 2019 Two-Timescale Networks for Nonlinear Value Function Approximation Wesley Chung, Somjit Nath, Ajin Joseph, Martha White

NeurIPS 2018 An Off-Policy Policy Gradient Theorem Using Emphatic Weightings Ehsan Imani, Eric Graves, Martha White

UAI 2018 Comparing Direct and Indirect Temporal-Difference Methods for Estimating the Variance of the Return Craig Sherstan, Dylan R. Ashley, Brendan Bennett, Kenny Young, Adam White, Martha White, Richard S. Sutton

NeurIPS 2018 Context-Dependent Upper-Confidence Bounds for Directed Exploration Raksha Kumaraswamy, Matthew Schlegel, Adam White, Martha White

UAI 2018 High-Confidence Error Estimates for Learned Value Functions Touqir Sajed, Wesley Chung, Martha White

ICML 2018 Improving Regression Performance with Distributional Losses Ehsan Imani, Martha White

IJCAI 2018 Organizing Experience: A Deeper Look at Replay Mechanisms for Sample-Based Planning in Continuous State Domains Yangchen Pan, Muhammad Zaheer, Adam White, Andrew Patterson, Martha White

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 2018 Supervised Autoencoders: Improving Generalization Performance with Unsupervised Regularizers Lei Le, Andrew Patterson, Martha White

AAAI 2017 Accelerated Gradient Temporal Difference Learning Yangchen Pan, Adam White, Martha White

ICML 2017 Adapting Kernel Representations Online Using Submodular Maximization Matthew Schlegel, Yangchen Pan, Jiecao Chen, Martha White

UAI 2017 Effective Sketching Methods for Value Function Approximation Yangchen Pan, Erfan Sadeqi Azer, Martha White

IJCAI 2017 Learning Sparse Representations in Reinforcement Learning with Sparse Coding Lei Le, Raksha Kumaraswamy, Martha White

NeurIPS 2017 Multi-View Matrix Factorization for Linear Dynamical System Estimation Mahdi Karami, Martha White, Dale Schuurmans, Csaba Szepesvari

AAAI 2017 Recovering True Classifier Performance in Positive-Unlabeled Learning Shantanu Jain, Martha White, Predrag Radivojac

ICML 2017 Unifying Task Specification in Reinforcement Learning Martha White

JMLR 2016 An Emphatic Approach to the Problem of Off-Policy Temporal-Difference Learning Richard S. Sutton, A. Rupam Mahmood, Martha White

NeurIPS 2016 Estimating the Class Prior and Posterior from Noisy Positives and Unlabeled Data Shantanu Jain, Martha White, Predrag Radivojac

IJCAI 2016 Incremental Truncated LSTD Clement Gehring, Yangchen Pan, Martha White

AAAI 2015 Optimal Estimation of Multivariate ARMA Models Martha White, Junfeng Wen, Michael Bowling, Dale Schuurmans

ECML-PKDD 2015 Scalable Metric Learning for Co-Embedding Farzaneh Mirzazadeh, Martha White, András György, Dale Schuurmans

NeurIPS 2012 Convex Multi-View Subspace Learning Martha White, Xinhua Zhang, Dale Schuurmans, Yao-liang Yu

AISTATS 2012 Generalized Optimal Reverse Prediction Martha White, Dale Schuurmans

ICML 2012 Linear Off-Policy Actor-Critic Thomas Degris, Martha White, Richard S. Sutton

AAAI 2011 Convex Sparse Coding, Subspace Learning, and Semi-Supervised Extensions Xinhua Zhang, Yaoliang Yu, Martha White, Ruitong Huang, Dale Schuurmans

NeurIPS 2010 Interval Estimation for Reinforcement-Learning Algorithms in Continuous-State Domains Martha White, Adam White

NeurIPS 2010 Relaxed Clipping: A Global Training Method for Robust Regression and Classification Min Yang, Linli Xu, Martha White, Dale Schuurmans, Yao-liang Yu

IJCAI 2009 Learning a Value Analysis Tool for Agent Evaluation Martha White, Michael H. Bowling

ICML 2009 Optimal Reverse Prediction: A Unified Perspective on Supervised, Unsupervised and Semi-Supervised Learning Linli Xu, Martha White, Dale Schuurmans