Robust learning under uncertain test distributions: Relating covariate shift to model misspecification J Wen, CN Yu, R Greiner International Conference on Machine Learning, 631-639, 2014 | 137 | 2014 |
Decentralized federated learning through proxy model sharing S Kalra, J Wen, JC Cresswell, M Volkovs, HR Tizhoosh Nature communications 14 (1), 2899, 2023 | 98 | 2023 |
Domain aggregation networks for multi-source domain adaptation J Wen, R Greiner, D Schuurmans International conference on machine learning, 10214-10224, 2020 | 91 | 2020 |
Universal successor representations for transfer reinforcement learning C Ma, J Wen, Y Bengio arXiv preprint arXiv:1804.03758, 2018 | 36 | 2018 |
Universal successor features for transfer reinforcement learning C Ma, DR Ashley, J Wen, Y Bengio arXiv preprint arXiv:2001.04025, 2020 | 33 | 2020 |
Batch stationary distribution estimation J Wen, B Dai, L Li, D Schuurmans arXiv preprint arXiv:2003.00722, 2020 | 31 | 2020 |
Few-shot self reminder to overcome catastrophic forgetting J Wen, Y Cao, R Huang arXiv preprint arXiv:1812.00543, 2018 | 24 | 2018 |
Correcting covariate shift with the frank-wolfe algorithm J Wen, R Greiner, D Schuurmans Twenty-fourth International Joint Conference on Artificial Intelligence, 2015 | 20 | 2015 |
Characterizing the gap between actor-critic and policy gradient J Wen, S Kumar, R Gummadi, D Schuurmans International Conference on Machine Learning, 11101-11111, 2021 | 18 | 2021 |
Find your friends: Personalized federated learning with the right collaborators Y Sui, J Wen, Y Lau, BL Ross, JC Cresswell arXiv preprint arXiv:2210.06597, 2022 | 15 | 2022 |
Proxyfl: decentralized federated learning through proxy model sharing S Kalra, J Wen, J Cresswell, M Volkovs, H Tizhoosh | 14 | 2021 |
Optimal estimation of multivariate ARMA models M White, J Wen, M Bowling, D Schuurmans Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015 | 12 | 2015 |
Convex two-layer modeling with latent structure V Ganapathiraman, X Zhang, Y Yu, J Wen Advances in Neural Information Processing Systems 29, 2016 | 5 | 2016 |
System and method for improving deep neural network performance Y Cao, R Huang, J Wen US Patent App. 16/562,067, 2020 | 4* | 2020 |
Weighted gaussian process for estimating treatment effect J Wen, N Hassanpour, R Greiner Proceedings of the 30th Annual Conference on Neural Information Processing …, 2018 | 4 | 2018 |
Shared model training with privacy protections S Kalra, JC Cresswell, J Wen, M Volkovs, HR Tizhoosh US Patent App. 17/987,761, 2023 | 2 | 2023 |
An Enhanced Combinatorial Contextual Neural Bandit Approach for Client Selection in Federated Learning X Ma, W Shi, J Wen European Interdisciplinary Cybersecurity Conference, 171-178, 2024 | 1 | 2024 |
Inverse Reinforcement Learning to Study Motivation in Mouse Behavioral Paradigms A Telfer, AA Shamsabadi, G Savin, J Wen, A Abizaid bioRxiv, 2024.06. 13.598948, 2024 | | 2024 |
An MRP Formulation for Supervised Learning: Generalized Temporal Difference Learning Models Y Pan, J Wen, C Xiao, P Torr arXiv preprint arXiv:2404.15518, 2024 | | 2024 |
A Comparison Between Common And Reinforcement Learning-Based Supply Air Temperature Reset Strategies With Varying Occupant Temperature Preferences H Elehwany, B Gunay, M Ouf, N Cotrufo, JS Venne, J Wen | | 2024 |