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Jaehyeok Shin
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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
422019
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
33*2018
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
312019
On the bias, risk and consistency of sample means in multi-armed bandits
J Shin, A Ramdas, A Rinaldo
arXiv preprint arXiv:1902.00746, 2019
272019
Probabilistic interpretations of recurrent neural networks
YJ Choe, J Shin, N Spencer
Probabilistic Graphical Models, 2017
152017
On conditional versus marginal bias in multi-armed bandits
J Shin, A Ramdas, A Rinaldo
International Conference on Machine Learning, 8852-8861, 2020
142020
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
132021
E-detectors: a nonparametric framework for online changepoint detection
J Shin, A Ramdas, A Rinaldo
arXiv preprint arXiv:2203.03532, 2022
92022
Predictive clustering
J Shin, A Rinaldo, L Wasserman
arXiv preprint arXiv:1903.08125, 2019
82019
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
42021
E-detectors: a nonparametric framework for sequential change detection
J Shin, A Ramdas, A Rinaldo
arXiv preprint arXiv:2203.03532, 2022
12022
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
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Articles 1–13