Andrew Patterson

Publication Activity (10 Years)

Years Active: 2003-2024
Publications (10 Years): 30

Top Topics

Reinforcement Learning

Continuous State

Adaptive Control

Gradient Estimation

Top Venues

Publications

Parham Mohammad Panahi, Andrew Patterson, Martha White, Adam White
Investigating the Interplay of Prioritized Replay and Generalization. CoRR (2024)
Gage MacLin, Venanzio Cichella, Andrew Patterson, Michael J. Acheson, Irene M. Gregory
Optimal Control using Composite Bernstein Approximants. CoRR (2024)
Andrew Patterson, Samuel Neumann, Raksha Kumaraswamy, Martha White, Adam White
The Cross-environment Hyperparameter Setting Benchmark for Reinforcement Learning. CoRR (2024)
Vincent Liu, Prabhat Nagarajan, Andrew Patterson, Martha White
When is Offline Policy Selection Sample Efficient for Reinforcement Learning? CoRR (2023)
Andrew Patterson, Victor Liao, Martha White
Robust Losses for Learning Value Functions. IEEE Trans. Pattern Anal. Mach. Intell. 45 (5) (2023)
Andrew Patterson, Samuel Neumann, Martha White, Adam White
Empirical Design in Reinforcement Learning. CoRR (2023)
Samuele Tosatto, Andrew Patterson, Martha White, Rupam Mahmood
A Temporal-Difference Approach to Policy Gradient Estimation. ICML (2022)
Andrew Patterson, Victor Liao, Martha White
Robust Losses for Learning Value Functions. CoRR (2022)
Samuele Tosatto, Andrew Patterson, Martha White, A. Rupam Mahmood
A Temporal-Difference Approach to Policy Gradient Estimation. CoRR (2022)
Andrew Patterson, Adam White, Martha White
A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning. J. Mach. Learn. Res. 23 (2022)
Aditya Gahlawat, Arun Lakshmanan, Lin Song, Andrew Patterson, Zhuohuan Wu, Naira Hovakimyan, Evangelos A. Theodorou
-Adaptive Control using Gaussian Processes. L4DC (2021)
Matthew Schlegel, Andrew Jacobsen, Zaheer Abbas, Andrew Patterson, Adam White, Martha White
General Value Function Networks. J. Artif. Intell. Res. 70 (2021)
Andrew Patterson, Adam White, Sina Ghiassian, Martha White
A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning. CoRR (2021)
Aditya Gahlawat, Arun Lakshmanan, Lin Song, Andrew Patterson, Zhuohuan Wu, Naira Hovakimyan, Evangelos A. Theodorou
-GP: Safe Simultaneous Learning and Control. CoRR (2020)
Andrew Patterson, Aditya Gahlawat, Naira Hovakimyan
Learning Probabilistic Intersection Traffic Models for Trajectory Prediction. CoRR (2020)
Sina Ghiassian, Andrew Patterson, Shivam Garg, Dhawal Gupta, Adam White, Martha White
Gradient Temporal-Difference Learning with Regularized Corrections. ICML (2020)
Sina Ghiassian, Andrew Patterson, Shivam Garg, Dhawal Gupta, Adam White, Martha White
Gradient Temporal-Difference Learning with Regularized Corrections. CoRR (2020)
Yi-Qi Lin, Andrew Patterson
Design Solutions for 5G Power Amplifiers using 0.15μm and 0.25μm GaN HEMTs. VLSI-DAT (2020)
Aditya Gahlawat, Pan Zhao, Andrew Patterson, Naira Hovakimyan, Evangelos A. Theodorou
$\mathcal{L}_1$-$\mathcal{GP}$: $\mathcal{L}_1$ Adaptive Control with Bayesian Learning. CoRR (2020)
Aditya Gahlawat, Pan Zhao, Andrew Patterson, Naira Hovakimyan, Evangelos A. Theodorou
L1-GP: L1 Adaptive Control with Bayesian Learning. L4DC (2020)
Andrew Patterson, Arun Lakshmanan, Naira Hovakimyan
Intent-Aware Probabilistic Trajectory Estimation for Collision Prediction with Uncertainty Quantification. CoRR (2019)
Andrew Patterson, Arun Lakshmanan, Naira Hovakimyan
Intent-Aware Probabilistic Trajectory Estimation for Collision Prediction with Uncertainty Quantification. CDC (2019)
Arun Lakshmanan, Andrew Patterson, Venanzio Cichella, Naira Hovakimyan
Proximity Queries for Absolutely Continuous Parametric Curves. Robotics: Science and Systems (2019)
Arun Lakshmanan, Andrew Patterson, Venanzio Cichella, Naira Hovakimyan
Proximity Queries for Absolutely Continuous Parametric Curves. CoRR (2019)
Farzane Aminmansour, Andrew Patterson, Lei Le, Yisu Peng, Daniel Mitchell, Franco Pestilli, Cesar F. Caiafa, Russell Greiner, Martha White
Learning Macroscopic Brain Connectomes via Group-Sparse Factorization. NeurIPS (2019)
Matthew Schlegel, Adam White, Andrew Patterson, Martha White
General Value Function Networks. CoRR (2018)
Yangchen Pan, Muhammad Zaheer, Adam White, Andrew Patterson, Martha White
Organizing Experience: a Deeper Look at Replay Mechanisms for Sample-Based Planning in Continuous State Domains. IJCAI (2018)
Lei Le, Andrew Patterson, Martha White
Supervised autoencoders: Improving generalization performance with unsupervised regularizers. NeurIPS (2018)
Sina Ghiassian, Andrew Patterson, Martha White, Richard S. Sutton, Adam White
Online Off-policy Prediction. CoRR (2018)
Yangchen Pan, Muhammad Zaheer, Adam White, Andrew Patterson, Martha White
Organizing Experience: A Deeper Look at Replay Mechanisms for Sample-based Planning in Continuous State Domains. CoRR (2018)
Andrew James McMinn, Daniel Tsvetkov, Tsvetan Yordanov, Andrew Patterson, Rrobi Szk, Jesus A. Rodriguez Perez, Joemon M. Jose
An interactive interface for visualizing events on Twitter. SIGIR (2014)
Michael Kölling, Andrew Patterson
Going Interactive: Combining Ad-Hoc and Regression Testing. XP (2004)
Andrew Patterson, Michael Kölling, John Rosenberg
Introducing unit testing with BlueJ. ITiCSE (2003)
Michael Kölling, Bruce Quig, Andrew Patterson, John Rosenberg
The BlueJ System and its Pedagogy. Comput. Sci. Educ. 13 (4) (2003)