Introducing unit testing with BlueJ. - Andrew Patterson; Michael Kölling; John Rosenberg (ITiCSE, 2003)

The BlueJ System and its Pedagogy. - Michael Kölling; Bruce Quig; Andrew Patterson; John Rosenberg (Comput. Sci. Educ., 2003)

Going Interactive: Combining Ad-Hoc and Regression Testing. - Michael Kölling; Andrew Patterson (XP, 2004)

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 (SIGIR, 2014)

Organizing Experience: a Deeper Look at Replay Mechanisms for Sample-Based Planning in Continuous State Domains. - Yangchen Pan; Muhammad Zaheer; Adam White 001; Andrew Patterson; Martha White (IJCAI, 2018)

Supervised autoencoders: Improving generalization performance with unsupervised regularizers. - Lei Le; Andrew Patterson; Martha White (NeurIPS, 2018)

General Value Function Networks. - Matthew Schlegel; Adam White 001; Andrew Patterson; Martha White (CoRR, 2018)

Online Off-policy Prediction. - Sina Ghiassian; Andrew Patterson; Martha White; Richard S. Sutton; Adam White 001 (CoRR, 2018)

Intent-Aware Probabilistic Trajectory Estimation for Collision Prediction with Uncertainty Quantification. - Andrew Patterson; Arun Lakshmanan; Naira Hovakimyan (CDC, 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 (NeurIPS, 2019)

Proximity Queries for Absolutely Continuous Parametric Curves. - Arun Lakshmanan; Andrew Patterson; Venanzio Cichella; Naira Hovakimyan (Robotics: Science and Systems, 2019)

Gradient Temporal-Difference Learning with Regularized Corrections. - Sina Ghiassian; Andrew Patterson; Shivam Garg 006; Dhawal Gupta; Adam White 001; Martha White (ICML, 2020)

L1-GP: L1 Adaptive Control with Bayesian Learning. - Aditya Gahlawat; Pan Zhao 003; Andrew Patterson; Naira Hovakimyan; Evangelos A. Theodorou (L4DC, 2020)

Design Solutions for 5G Power Amplifiers using 0.15μm and 0.25μm GaN HEMTs. - Yi-Qi Lin; Andrew Patterson (VLSI-DAT, 2020)

Learning Probabilistic Intersection Traffic Models for Trajectory Prediction. - Andrew Patterson; Aditya Gahlawat; Naira Hovakimyan (CoRR, 2020)

$ mathcal{L}_1$-$ mathcal{GP}$: $ mathcal{L}_1$ Adaptive Control with Bayesian Learning. - Aditya Gahlawat; Pan Zhao 003; Andrew Patterson; Naira Hovakimyan; Evangelos A. Theodorou (CoRR, 2020)

RL1-GP: Safe Simultaneous Learning and Control. - Aditya Gahlawat; Arun Lakshmanan; Lin Song 001; Andrew Patterson; Zhuohuan Wu; Naira Hovakimyan; Evangelos A. Theodorou (CoRR, 2020)

Contraction ℒ1-Adaptive Control using Gaussian Processes. - Aditya Gahlawat; Arun Lakshmanan; Lin Song 001; Andrew Patterson; Zhuohuan Wu; Naira Hovakimyan; Evangelos A. Theodorou (L4DC, 2021)

A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning. - Andrew Patterson; Adam White 001; Sina Ghiassian; Martha White (CoRR, 2021)

General Value Function Networks. - Matthew Schlegel; Andrew Jacobsen; Zaheer Abbas; Andrew Patterson; Adam White 001; Martha White (J. Artif. Intell. Res., 2021)

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

A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning. - Andrew Patterson; Adam White 001; Martha White (J. Mach. Learn. Res., 2022)

Empirical Design in Reinforcement Learning. - Andrew Patterson; Samuel Neumann; Martha White; Adam White 001 (CoRR, 2023)

When is Offline Policy Selection Sample Efficient for Reinforcement Learning? - Vincent Liu; Prabhat Nagarajan; Andrew Patterson; Martha White (CoRR, 2023)

Robust Losses for Learning Value Functions. - Andrew Patterson; Victor Liao; Martha White (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Optimal Control using Composite Bernstein Approximants. - Gage MacLin; Venanzio Cichella; Andrew Patterson; Michael J. Acheson; Irene M. Gregory (CDC, 2024)

Investigating the Interplay of Prioritized Replay and Generalization. - Parham Mohammad Panahi; Andrew Patterson; Martha White; Adam White 001 (RLJ, 2024)

Cross-environment Hyperparameter Tuning for Reinforcement Learning. - Andrew Patterson; Samuel Neumann; Raksha Kumaraswamy; Martha White; Adam White 001 (RLJ, 2024)

The Cross-environment Hyperparameter Setting Benchmark for Reinforcement Learning. - Andrew Patterson; Samuel Neumann; Raksha Kumaraswamy; Martha White; Adam White 001 (CoRR, 2024)

The Cell Must Go On: Agar.io for Continual Reinforcement Learning. - Mohamed A. Mohamed; Kateryna Nekhomiazh; Vedant Vyas; Marcos M. Jose; Andrew Patterson; Marlos C. Machado (CoRR, 2025)

Deep Reinforcement Learning with Gradient Eligibility Traces. - Esraa Elelimy; Brett Daley; Andrew Patterson; Marlos C. Machado; Adam White 001; Martha White (CoRR, 2025)