Mingda Qiao

Learning Discrete Distributions from Untrusted Batches

2017 · arXiv (Cornell University)

We consider the problem of learning a discrete distribution in the presence of an $ε$ fraction of malicious data sources. Specifically, we consider the setting where there is some underlying distribution, $p$, and each data …

Towards Instance Optimal Bounds for Best Arm Identification

2016 · Conference on Learning Theory

In the classical best arm identification (Best-$1$-Arm) problem, we are given $n$ stochastic bandit arms, each associated with a reward distribution with an unknown mean. We would like to identify the arm with the largest …

Learning Discrete Distributions from Untrusted Batches

2018 · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)

We consider the problem of learning a discrete distribution in the presence of an epsilon fraction of malicious data sources. Specifically, we consider the setting where there is some underlying distribution, p, and each data …