Andrew Patterson

41 papers A* 6B 2Journal 26Unranked 7

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Deep Reinforcement Learning with Gradient Eligibility Traces. CoRR Esraa Elelimy, Brett Daley, Andrew Patterson, Marlos C. Machado, Adam White, Martha White
2025	—	`conf`	Real-Time Optimal Control via Transformer Networks and Bernstein Polynomials. CDC Gage MacLin, Venanzio Cichella, Andrew Patterson, Irene M. Gregory
2025	J	`jnl`	The Cell Must Go On: Agar.io for Continual Reinforcement Learning. CoRR Mohamed A. Mohamed, Kateryna Nekhomiazh, Vedant Vyas, Marcos M. Jose, Andrew Patterson, Marlos C. Machado
2024	J	`jnl`	Cross-environment Hyperparameter Tuning for Reinforcement Learning. RLJ Andrew Patterson, Samuel Neumann, Raksha Kumaraswamy, Martha White, Adam White
2024	J	`jnl`	Empirical Design in Reinforcement Learning. J. Mach. Learn. Res. Andrew Patterson, Samuel Neumann, Martha White, Adam White
2024	J	`jnl`	Investigating the Interplay of Prioritized Replay and Generalization. RLJ Parham Mohammad Panahi, Andrew Patterson, Martha White, Adam White
2024	J	`jnl`	Investigating the Interplay of Prioritized Replay and Generalization. CoRR Parham Mohammad Panahi, Andrew Patterson, Martha White, Adam White
2024	—	`conf`	Optimal Control using Composite Bernstein Approximants. CDC Gage MacLin, Venanzio Cichella, Andrew Patterson, Michael J. Acheson, Irene M. Gregory
2024	J	`jnl`	Optimal Control using Composite Bernstein Approximants. CoRR Gage MacLin, Venanzio Cichella, Andrew Patterson, Michael J. Acheson, Irene M. Gregory
2024	J	`jnl`	The Cross-environment Hyperparameter Setting Benchmark for Reinforcement Learning. CoRR Andrew Patterson, Samuel Neumann, Raksha Kumaraswamy, Martha White, Adam White
2023	J	`jnl`	Empirical Design in Reinforcement Learning. CoRR Andrew Patterson, Samuel Neumann, Martha White, Adam White
2023	J	`jnl`	Robust Losses for Learning Value Functions. IEEE Trans. Pattern Anal. Mach. Intell. Andrew Patterson, Victor Liao, Martha White
2023	J	`jnl`	When is Offline Policy Selection Sample Efficient for Reinforcement Learning? CoRR Vincent Liu, Prabhat Nagarajan, Andrew Patterson, Martha White
2022	J	`jnl`	A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning. J. Mach. Learn. Res. Andrew Patterson, Adam White, Martha White
2022	A*	`conf`	A Temporal-Difference Approach to Policy Gradient Estimation. ICML Samuele Tosatto, Andrew Patterson, Martha White, Rupam Mahmood
2022	J	`jnl`	A Temporal-Difference Approach to Policy Gradient Estimation. CoRR Samuele Tosatto, Andrew Patterson, Martha White, A. Rupam Mahmood
2022	J	`jnl`	Robust Losses for Learning Value Functions. CoRR Andrew Patterson, Victor Liao, Martha White
2021	J	`jnl`	A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning. CoRR Andrew Patterson, Adam White, Sina Ghiassian, Martha White
2021	—	`conf`	Contraction ℒ L4DC Aditya Gahlawat, Arun Lakshmanan, Lin Song, Andrew Patterson, Zhuohuan Wu, Naira Hovakimyan, Evangelos A. Theodorou
2021	J	`jnl`	General Value Function Networks. J. Artif. Intell. Res. Matthew Schlegel, Andrew Jacobsen, Zaheer Abbas, Andrew Patterson, Adam White, Martha White
2020	J	`jnl`	$\mathcal{L}_1$-$\mathcal{GP}$: $\mathcal{L}_1$ Adaptive Control with Bayesian Learning. CoRR Aditya Gahlawat, Pan Zhao, Andrew Patterson, Naira Hovakimyan, Evangelos A. Theodorou
2020	—	`conf`	Design Solutions for 5G Power Amplifiers using 0.15μm and 0.25μm GaN HEMTs. VLSI-DAT Yi-Qi Lin, Andrew Patterson
2020	A*	`conf`	Gradient Temporal-Difference Learning with Regularized Corrections. ICML Sina Ghiassian, Andrew Patterson, Shivam Garg, Dhawal Gupta, Adam White, Martha White
2020	J	`jnl`	Gradient Temporal-Difference Learning with Regularized Corrections. CoRR Sina Ghiassian, Andrew Patterson, Shivam Garg, Dhawal Gupta, Adam White, Martha White
2020	—	`conf`	L1-GP: L1 Adaptive Control with Bayesian Learning. L4DC Aditya Gahlawat, Pan Zhao, Andrew Patterson, Naira Hovakimyan, Evangelos A. Theodorou
2020	J	`jnl`	Learning Probabilistic Intersection Traffic Models for Trajectory Prediction. CoRR Andrew Patterson, Aditya Gahlawat, Naira Hovakimyan
2020	J	`jnl`	RL CoRR Aditya Gahlawat, Arun Lakshmanan, Lin Song, Andrew Patterson, Zhuohuan Wu, Naira Hovakimyan, Evangelos A. Theodorou
2019	—	`conf`	Intent-Aware Probabilistic Trajectory Estimation for Collision Prediction with Uncertainty Quantification. CDC Andrew Patterson, Arun Lakshmanan, Naira Hovakimyan
2019	J	`jnl`	Intent-Aware Probabilistic Trajectory Estimation for Collision Prediction with Uncertainty Quantification. CoRR Andrew Patterson, Arun Lakshmanan, Naira Hovakimyan
2019	A*	`conf`	Learning Macroscopic Brain Connectomes via Group-Sparse Factorization. NeurIPS Farzane Aminmansour, Andrew Patterson, Lei Le, Yisu Peng, Daniel Mitchell, Franco Pestilli, Cesar F. Caiafa, Russell Greiner, Martha White
2019	—	`conf`	Proximity Queries for Absolutely Continuous Parametric Curves. Robotics: Science and Systems Arun Lakshmanan, Andrew Patterson, Venanzio Cichella, Naira Hovakimyan
2019	J	`jnl`	Proximity Queries for Absolutely Continuous Parametric Curves. CoRR Arun Lakshmanan, Andrew Patterson, Venanzio Cichella, Naira Hovakimyan
2018	J	`jnl`	General Value Function Networks. CoRR Matthew Schlegel, Adam White, Andrew Patterson, Martha White
2018	J	`jnl`	Online Off-policy Prediction. CoRR Sina Ghiassian, Andrew Patterson, Martha White, Richard S. Sutton, Adam White
2018	J	`jnl`	Organizing Experience: A Deeper Look at Replay Mechanisms for Sample-based Planning in Continuous State Domains. CoRR Yangchen Pan, Muhammad Zaheer, Adam White, Andrew Patterson, Martha White
2018	A*	`conf`	Organizing Experience: a Deeper Look at Replay Mechanisms for Sample-Based Planning in Continuous State Domains. IJCAI Yangchen Pan, Muhammad Zaheer, Adam White, Andrew Patterson, Martha White
2018	A*	`conf`	Supervised autoencoders: Improving generalization performance with unsupervised regularizers. NeurIPS Lei Le, Andrew Patterson, Martha White
2014	A*	`conf`	An interactive interface for visualizing events on Twitter. SIGIR Andrew James McMinn, Daniel Tsvetkov, Tsvetan R. Yordanov, Andrew Patterson, Rrobi Szk, Jesus A. Rodriguez Perez, Joemon M. Jose
2004	B	`conf`	Going Interactive: Combining Ad-Hoc and Regression Testing. XP Michael Kölling, Andrew Patterson
2003	B	`conf`	Introducing unit testing with BlueJ. ITiCSE Andrew Patterson, Michael Kölling, John Rosenberg
2003	J	`jnl`	The BlueJ System and its Pedagogy. Comput. Sci. Educ. Michael Kölling, Bruce Quig, Andrew Patterson, John Rosenberg