| 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 | 225 | 2018 |
| Asymptotics of cross-validation M Austern, W Zhou arXiv preprint arXiv:2001.11111, 2020 | 49 | 2020 |
| Debiased machine learning without sample-splitting for stable estimators Q Chen, V Syrgkanis, M Austern Advances in Neural Information Processing Systems 35, 3096-3109, 2022 | 36 | 2022 |
| Compressibility and generalization in large-scale deep learning W Zhou, V Veitch, M Austern, RP Adams, P Orbanz arXiv preprint arXiv:1804.05862 2, 2018 | 20 | 2018 |
| Efficient concentration with Gaussian approximation M Austern, L Mackey arXiv preprint arXiv:2208.09922, 2022 | 19 | 2022 |
| Limit theorems for distributions invariant under groups of transformations M Austern, P Orbanz The Annals of Statistics 50 (4), 1960-1991, 2022 | 18 | 2022 |
| Quantifying the effects of data augmentation KH Huang, P Orbanz, M Austern arXiv preprint arXiv:2202.09134 1, 2022 | 13 | 2022 |
| 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 |
| Asymptotics of network embeddings learned via subsampling A Davison, M Austern Journal of Machine Learning Research 24 (138), 1-120, 2023 | 10 | 2023 |
| Asymptotics of the empirical bootstrap method beyond asymptotic normality M Austern, V Syrgkanis arXiv preprint arXiv:2011.11248, 2020 | 6 | 2020 |
| Wasserstein-p bounds in the central limit theorem under local dependence T Liu, M Austern Electronic Journal of Probability 28, 1-47, 2023 | 4 | 2023 |
| On the gaussianity of kolmogorov complexity of mixing sequences M Austern, A Maleki IEEE Transactions on Information Theory 66 (2), 1232-1247, 2019 | 4 | 2019 |
| Statistical Guarantees for Link Prediction using Graph Neural Networks A Chung, A Saberi, M Austern arXiv preprint arXiv:2402.02692, 2024 | 3 | 2024 |
| Gaussian universality for approximately polynomial functions of high-dimensional data KH Huang, M Austern, P Orbanz arXiv preprint arXiv:2403.10711, 2024 | 2 | 2024 |
| Random Geometric Graph Alignment with Graph Neural Networks S Liu, M Austern arXiv preprint arXiv:2402.07340, 2024 | 2 | 2024 |
| Smooth Edgeworth Expansion and Wasserstein- Bounds for Mixing Random Fields T Liu, M Austern arXiv preprint arXiv:2309.07031, 2023 | 2 | 2023 |
| Inference on Optimal Dynamic Policies via Softmax Approximation Q Chen, M Austern, V Syrgkanis arXiv preprint arXiv:2303.04416, 2023 | 2 | 2023 |
| Wasserstein-p Bounds in the Central Limit Theorem Under Weak Dependence T Liu, M Austern arXiv preprint arXiv:2209.09377, 2022 | 2 | 2022 |
| A free central-limit theorem for dynamical systems M Austern arXiv preprint arXiv:2005.10923, 2020 | 2 | 2020 |
| Limit theorems for invariant distributions M Austern, P Orbanz arXiv preprint arXiv:1806.10661, 2018 | 2 | 2018 |
