Andrew Patterson

According to our database, Andrew Patterson authored at least 28 papers between 2003 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

The Cross-environment Hyperparameter Setting Benchmark for Reinforcement Learning.

Andrew Patterson, Samuel Neumann, Raksha Kumaraswamy, Martha White, Adam White

CoRR, 2024

Optimal Control using Composite Bernstein Approximants.

Gage MacLin, Venanzio Cichella, Andrew Patterson, Michael J. Acheson, Irene M. Gregory

CoRR, 2024

Investigating the Interplay of Prioritized Replay and Generalization.

Parham Mohammad Panahi, Andrew Patterson, Martha White, Adam White

CoRR, 2024

2023

Robust Losses for Learning Value Functions.

Andrew Patterson, Victor Liao, Martha White

IEEE Trans. Pattern Anal. Mach. Intell., May, 2023

When is Offline Policy Selection Sample Efficient for Reinforcement Learning?

Vincent Liu, Prabhat Nagarajan, Andrew Patterson, Martha White

CoRR, 2023

Empirical Design in Reinforcement Learning.

Andrew Patterson, Samuel Neumann, Martha White, Adam White

CoRR, 2023

2022

A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning.

Andrew Patterson, Adam White, Martha White

J. Mach. Learn. Res., 2022

A Temporal-Difference Approach to Policy Gradient Estimation.

Samuele Tosatto, Andrew Patterson, Martha White, Rupam Mahmood

Proceedings of the International Conference on Machine Learning, 2022

2021

General Value Function Networks.

Matthew Schlegel, Andrew Jacobsen, Zaheer Abbas, Andrew Patterson, Adam White, Martha White

J. Artif. Intell. Res., 2021

A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning.

Andrew Patterson, Adam White, Sina Ghiassian, Martha White

CoRR, 2021

Contraction ℒ₁-Adaptive Control using Gaussian Processes.

Aditya Gahlawat, Arun Lakshmanan, Lin Song, Andrew Patterson, Zhuohuan Wu, Naira Hovakimyan, Evangelos A. Theodorou

Proceedings of the 3rd Annual Conference on Learning for Dynamics and Control, 2021

2020

RL₁-GP: Safe Simultaneous Learning and Control.

Aditya Gahlawat, Arun Lakshmanan, Lin Song, Andrew Patterson, Zhuohuan Wu, Naira Hovakimyan, Evangelos A. Theodorou

CoRR, 2020

$\mathcal{L}_1$-$\mathcal{GP}$: $\mathcal{L}_1$ Adaptive Control with Bayesian Learning.

Aditya Gahlawat, Pan Zhao, Andrew Patterson, Naira Hovakimyan, Evangelos A. Theodorou

CoRR, 2020

Learning Probabilistic Intersection Traffic Models for Trajectory Prediction.

Andrew Patterson, Aditya Gahlawat, Naira Hovakimyan

CoRR, 2020

Design Solutions for 5G Power Amplifiers using 0.15μm and 0.25μm GaN HEMTs.

Yi-Qi Lin, Andrew Patterson

Proceedings of the 2020 International Symposium on VLSI Design, Automation and Test, 2020

L1-GP: L1 Adaptive Control with Bayesian Learning.

Aditya Gahlawat, Pan Zhao, Andrew Patterson, Naira Hovakimyan, Evangelos A. Theodorou

Proceedings of the 2nd Annual Conference on Learning for Dynamics and Control, 2020

Gradient Temporal-Difference Learning with Regularized Corrections.

Sina Ghiassian, Andrew Patterson, Shivam Garg, Dhawal Gupta, Adam White, Martha White

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Proximity Queries for Absolutely Continuous Parametric Curves.

Arun Lakshmanan, Andrew Patterson, Venanzio Cichella, Naira Hovakimyan

Proceedings of the Robotics: Science and Systems XV, 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

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Intent-Aware Probabilistic Trajectory Estimation for Collision Prediction with Uncertainty Quantification.

Andrew Patterson, Arun Lakshmanan, Naira Hovakimyan

Proceedings of the 58th IEEE Conference on Decision and Control, 2019

2018

Online Off-policy Prediction.

Sina Ghiassian, Andrew Patterson, Martha White, Richard S. Sutton, Adam White

CoRR, 2018

General Value Function Networks.

Matthew Schlegel, Adam White, Andrew Patterson, Martha White

CoRR, 2018

Supervised autoencoders: Improving generalization performance with unsupervised regularizers.

Lei Le, Andrew Patterson, Martha White

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 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

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

2014

An interactive interface for visualizing events on Twitter.

Andrew James McMinn, Daniel Tsvetkov, Tsvetan R. Yordanov, Andrew Patterson, Rrobi Szk, Jesus A. Rodriguez Perez, Joemon M. Jose

Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2014

2004

Going Interactive: Combining Ad-Hoc and Regression Testing.

Michael Kölling, Andrew Patterson

Proceedings of the Extreme Programming and Agile Processes in Software Engineering, 2004

2003

The BlueJ System and its Pedagogy.

Michael Kölling, Bruce Quig, Andrew Patterson, John Rosenberg

Comput. Sci. Educ., 2003

Introducing unit testing with BlueJ.

Andrew Patterson, Michael Kölling, John Rosenberg

Proceedings of the 8th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education, 2003