25 citations · 62 across the 7 of their papers we have counts for
8 papers
E-detectors: a nonparametric framework for sequential change detection
Jaehyeok Shin, Aaditya Ramdas, Alessandro Rinaldo
Sequential change detection is a classical problem with a variety of applications. However, the majority of prior work has been parametric, for example, focusing on exponential fam…
Nonparametric iterated-logarithm extensions of the sequential generalized likelihood ratio test
Jaehyeok Shin, Aaditya Ramdas, Alessandro Rinaldo
We develop a nonparametric extension of the sequential generalized likelihood ratio (GLR) test and corresponding time-uniform confidence sequences for the mean of a univariate dist…
On conditional versus marginal bias in multi-armed bandits
Jaehyeok Shin, Aaditya Ramdas, Alessandro Rinaldo
The bias of the sample means of the arms in multi-armed bandits is an important issue in adaptive data analysis that has recently received considerable attention in the literature.…
Are sample means in multi-armed bandits positively or negatively biased?
Jaehyeok Shin, Aaditya Ramdas, Alessandro Rinaldo
It is well known that in stochastic multi-armed bandits (MAB), the sample mean of an arm is typically not an unbiased estimator of its true mean. In this paper, we decouple three d…
Predictive clustering
Jaehyeok Shin, Alessandro Rinaldo, Larry Wasserman
We show how to convert any clustering into a prediction set. This has the effect of converting the clustering into a (possibly overlapping) union of spheres or ellipsoids. The tuni…
Homotopy Reconstruction via the Cech Complex and the Vietoris-Rips Complex
Jisu Kim, Jaehyeok Shin, Frédéric Chazal +2
We derive conditions under which the reconstruction of a target space is topologically correct via the Čech complex or the Vietoris-Rips complex obtained from possibly noisy point…
On the bias, risk and consistency of sample means in multi-armed bandits
Jaehyeok Shin, Aaditya Ramdas, Alessandro Rinaldo
The sample mean is among the most well studied estimators in statistics, having many desirable properties such as unbiasedness and consistency. However, when analyzing data collect…
Uniform Convergence Rate of the Kernel Density Estimator Adaptive to Intrinsic Volume Dimension
Jisu Kim, Jaehyeok Shin, Alessandro Rinaldo +1
We derive concentration inequalities for the supremum norm of the difference between a kernel density estimator (KDE) and its point-wise expectation that hold uniformly over the se…