| Non-vacuous generalization bounds at the imagenet scale: a PAC-bayesian compression approach W Zhou, V Veitch, M Austern, RP Adams, P Orbanz arXiv preprint arXiv:1804.05862, 2018 | 234 | 2018 |
| Sketchgraphs: A large-scale dataset for modeling relational geometry in computer-aided design A Seff, Y Ovadia, W Zhou, RP Adams arXiv preprint arXiv:2007.08506, 2020 | 68 | 2020 |
| Vitruvion: A generative model of parametric cad sketches A Seff, W Zhou, N Richardson, RP Adams arXiv preprint arXiv:2109.14124, 2021 | 52 | 2021 |
| Autobahn: Automorphism-based graph neural nets E Thiede, W Zhou, R Kondor Advances in Neural Information Processing Systems 34, 29922-29934, 2021 | 51 | 2021 |
| Asymptotics of cross-validation M Austern, W Zhou arXiv preprint arXiv:2001.11111, 2020 | 43 | 2020 |
| Discrete object generation with reversible inductive construction A Seff, W Zhou, F Damani, A Doyle, RP Adams Advances in neural information processing systems 32, 2019 | 35 | 2019 |
| Approximate leave-one-out for fast parameter tuning in high dimensions S Wang, W Zhou, H Lu, A Maleki, V Mirrokni International Conference on Machine Learning, 5228-5237, 2018 | 31 | 2018 |
| Error bounds in estimating the out-of-sample prediction error using leave-one-out cross validation in high-dimensions KR Rad, W Zhou, A Maleki International Conference on Artificial Intelligence and Statistics, 4067-4077, 2020 | 22 | 2020 |
| Approximate leave-one-out for high-dimensional non-differentiable learning problems S Wang, W Zhou, A Maleki, H Lu, V Mirrokni arXiv preprint arXiv:1810.02716, 2018 | 21 | 2018 |
| Empirical risk minimization and stochastic gradient descent for relational data V Veitch, M Austern, W Zhou, DM Blei, P Orbanz The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 13 | 2019 |
| Compressed sensing in the presence of speckle noise W Zhou, S Jalali, A Maleki IEEE Transactions on Information Theory 68 (10), 6964-6980, 2022 | 6 | 2022 |
| Analysis of genotype by methylation interactions through sparsity-inducing regularized regression W Zhou, SH Lo BMC proceedings 12 (Suppl 9), 40, 2018 | 6 | 2018 |
| Graph neural networks for biochemistry that incorporate substructure EH Thiede, W Zhou, R Kondor Biophysical Journal 121 (3), 531a, 2022 | 3 | 2022 |
| Towards theoretically-founded learning-based denoising W Zhou, S Jalali 2019 IEEE International Symposium on Information Theory (ISIT), 2714-2718, 2019 | 3 | 2019 |
| The challenge of detecting genotype-by-methylation interaction: GAW20 M De Andrade, E Warwick Daw, AT Kraja, V Fisher, L Wang, K Hu, J Li, ... BMC genetics 19, 119-125, 2018 | 2 | 2018 |
| Denoising of structured random processes W Zhou, S Jalali arXiv preprint arXiv:1901.05937, 2019 | 1 | 2019 |
| Correction to” Compressed sensing in the presence of speckle noise” W Zhou, S Jalali, A Maleki IEEE Transactions on Information Theory, 2024 | | 2024 |
| Bayesian denoising of structured sources and its implications on learning-based denoising W Zhou, J Wabnig, S Jalali Information and Inference: A Journal of the IMA 12 (4), 2503-2545, 2023 | | 2023 |
| Graph Neural Networks that incorporate Physical Structure E Thiede, W Zhou, R Kondor APS March Meeting Abstracts 2022, S01. 002, 2022 | | 2022 |
| New Perspectives in Cross-Validation W Zhou Columbia University, 2020 | | 2020 |
