Andrew Patterson
Andrew Patterson
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Supervised autoencoders: Improving generalization performance with unsupervised regularizers
L Le, A Patterson, M White
Advances in neural information processing systems 31, 2018
The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services
P Avesani, B McPherson, S Hayashi, CF Caiafa, R Henschel, ...
Scientific data 6 (1), 69, 2019
Organizing experience: a deeper look at replay mechanisms for sample-based planning in continuous state domains
Y Pan, M Zaheer, A White, A Patterson, M White
International Joint Conference on Artificial Intelligence, 2018
Gradient Temporal-Difference Learning with Regularized Corrections
S Ghiassian, A Patterson, S Garg, D Gupta, A White, M White
International Conference on Machine Learning, 2020
General value function networks
M Schlegel, A Jacobsen, M Zaheer, A Patterson, A White, M White
arXiv preprint arXiv:1807.06763, 2018
Online off-policy prediction
S Ghiassian, A Patterson, M White, RS Sutton, A White
arXiv preprint arXiv:1811.02597, 2018
A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning
A Patterson, A White, M White
arXiv preprint arXiv:2104.13844, 2021
Learning macroscopic brain connectomes via group-sparse factorization
F Aminmansour, A Patterson, L Le, Y Peng, D Mitchell, F Pestilli, ...
Advances in Neural Information Processing Systems 32, 2019
Robust losses for learning value functions
A Patterson, V Liao, M White
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022
Discovery of Predictive Representations With a Network of General Value Functions
M Schlegel, A Patterson, A White, M White
Empirical Design in Reinforcement Learning
A Patterson, S Neumann, M White, A White
arXiv preprint arXiv:2304.01315, 2023
A Temporal-Difference Approach to Policy Gradient Estimation
S Tosatto, A Patterson, M White, R Mahmood
International Conference on Machine Learning, 21609-21632, 2022
The cross-environment hyperparameter setting benchmark for reinforcement learning
A Patterson, S Neumann, A White, R Kumaraswamy, M White
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