| Support recovery with stochastic gates: Theory and application for linear models S Jana, H Li, Y Yamada, O Lindenbaum Signal Processing, 2023 | 11 | 2023 |
| Optimal empirical Bayes estimation for the Poisson model via minimum-distance methods S Jana, Y Polyanskiy, Y Wu arXiv preprint arXiv:2209.01328, 2022 | 10 | 2022 |
| A characterization of all single-integral, non-kernel divergence estimators S Jana, A Basu IEEE Transactions on Information Theory 65 (12), 7976-7984, 2019 | 10 | 2019 |
| Extrapolating the profile of a finite population S Jana, Y Polyanskiy, Y Wu Conference on Learning Theory, 2011-2033, 2020 | 8 | 2020 |
| Optimal prediction of markov chains with and without spectral gap Y Han, S Jana, Y Wu Advances in Neural Information Processing Systems 34, 11233-11246, 2021 | 6 | 2021 |
| Empirical Bayes via ERM and Rademacher complexities: the Poisson model S Jana, Y Polyanskiy, AZ Teh, Y Wu The Thirty Sixth Annual Conference on Learning Theory, 5199-5235, 2023 | 2 | 2023 |
| Adversarially robust clustering with optimality guarantees S Jana, K Yang, S Kulkarni arXiv preprint arXiv:2306.09977, 2023 | 2 | 2023 |
| Optimal prediction of Markov chains with and without spectral gap Y Han, S Jana, Y Wu IEEE Transactions on Information Theory 69 (6), 3920-3959, 2023 | 2 | 2023 |
| A general theory for robust clustering via trimmed mean S Jana, J Fan, S Kulkarni arXiv preprint arXiv:2401.05574, 2024 | | 2024 |
| Learning Non-Parametric and High-Dimensional Distributions via Information-Theoretic Methods S Jana Yale University, 2022 | | 2022 |
| Optimal prediction of Markov chains with and without spectral gap conditions Y Han, S Jana, Y Wu | | |
Yihong WuProfessor, Department of Statistics and Data Science在 yale.edu 的电子邮件经过验证
