Patterson, Andrew

10 publications

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

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

L4DC 2021 Contraction L1-Adaptive Control Using Gaussian Processes Aditya Gahlawat, Arun Lakshmanan, Lin Song, Andrew Patterson, Zhuohuan Wu, Naira Hovakimyan, Evangelos A. Theodorou

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

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

L4DC 2020 L1-GP: L1 Adaptive Control with Bayesian Learning Aditya Gahlawat, Pan Zhao, Andrew Patterson, Naira Hovakimyan, Evangelos Theodorou

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

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

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