| 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 | 120 | 2014 |
| Domain aggregation networks for multi-source domain adaptation J Wen, R Greiner, D Schuurmans International conference on machine learning, 10214-10224, 2020 | 72 | 2020 |
| Universal successor representations for transfer reinforcement learning C Ma, J Wen, Y Bengio arXiv preprint arXiv:1804.03758, 2018 | 33 | 2018 |
| Decentralized federated learning through proxy model sharing S Kalra, J Wen, JC Cresswell, M Volkovs, HR Tizhoosh Nature communications 14 (1), 2899, 2023 | 28 | 2023 |
| Batch stationary distribution estimation J Wen, B Dai, L Li, D Schuurmans arXiv preprint arXiv:2003.00722, 2020 | 24 | 2020 |
| Few-shot self reminder to overcome catastrophic forgetting J Wen, Y Cao, R Huang arXiv preprint arXiv:1812.00543, 2018 | 23 | 2018 |
| Universal successor features for transfer reinforcement learning C Ma, DR Ashley, J Wen, Y Bengio arXiv preprint arXiv:2001.04025, 2020 | 21 | 2020 |
| 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 | 16 | 2021 |
| Correcting covariate shift with the frank-wolfe algorithm J Wen, R Greiner, D Schuurmans Twenty-fourth International Joint Conference on Artificial Intelligence, 2015 | 15 | 2015 |
| 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 |
| Proxyfl: decentralized federated learning through proxy model sharing S Kalra, J Wen, JC Cresswell, M Volkovs, HR Tizhoosh arXiv preprint arXiv:2111.11343, 2021 | 11 | 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 | 5 | 2022 |
| 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 |
| 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 |
| System and method for improving deep neural network performance Y Cao, R Huang, J Wen US Patent App. 16/562,067, 2020 | 3* | 2020 |
| 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 |
| Distributed model training with collaboration weights for private data sets JC Cresswell, BL Ross, KHY Lau, J Wen, Y Sui US Patent App. 18/202,459, 2023 | | 2023 |
| Generalized Temporal Difference Learning Models for Supervised Learning Y Pan, J Wen, C Xiao, P Torr | | 2023 |
| Shared model training with privacy protections S Kalra, JC Cresswell, J Wen, M Volkovs, HR Tizhoosh US Patent App. 17/987,761, 2023 | | 2023 |
| A Parametric Class of Approximate Gradient Updates for Policy Optimization R Gummadi, S Kumar, J Wen, D Schuurmans International Conference on Machine Learning, 7998-8015, 2022 | | 2022 |
Dale SchuurmansUniversity of Alberta, Google DeepMindVahvistettu sähköpostiosoite verkkotunnuksessa cs.ualberta.ca
