| Are sample means in multi-armed bandits positively or negatively biased? J Shin, A Ramdas, A Rinaldo Advances in Neural Information Processing Systems 32, 2019 | 44 | 2019 |
| On the bias, risk, and consistency of sample means in multi-armed bandits J Shin, A Ramdas, A Rinaldo SIAM Journal on Mathematics of Data Science 3 (4), 1278-1300, 2021 | 42 | 2021 |
| Homotopy reconstruction via the cech complex and the vietoris-rips complex J Kim, J Shin, F Chazal, A Rinaldo, L Wasserman arXiv preprint arXiv:1903.06955, 2019 | 36 | 2019 |
| Uniform convergence rate of the kernel density estimator adaptive to intrinsic dimension J Kim, J Shin, A Rinaldo, L Wasserman arXiv preprint arXiv:1810.05935, 2018 | 34* | 2018 |
| Probabilistic interpretations of recurrent neural networks YJ Choe, J Shin, N Spencer Probabilistic Graphical Models, 2017 | 16 | 2017 |
| On conditional versus marginal bias in multi-armed bandits J Shin, A Ramdas, A Rinaldo International Conference on Machine Learning, 8852-8861, 2020 | 15 | 2020 |
| E-detectors: a nonparametric framework for online changepoint detection J Shin, A Ramdas, A Rinaldo The New England Journal of Statistics in Data Science 2 (2), 229--260, 2023 | 14* | 2023 |
| Predictive clustering J Shin, A Rinaldo, L Wasserman arXiv preprint arXiv:1903.08125, 2019 | 8 | 2019 |
| Nonparametric iterated-logarithm extensions of the sequential generalized likelihood ratio test J Shin, A Ramdas, A Rinaldo IEEE Journal on Selected Areas in Information Theory 2 (2), 691-704, 2021 | 5 | 2021 |
| Confidence sets for persistent homology of the KDE filtration J Shin, J Kim, A Rinaldo, L Wasserman | 1 | |
| A qualitative and quantitative analysis of the bias caused by adaptivity in multi-armed bandits J Shin Carnegie Mellon University, 2020 | | 2020 |
