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Given samples from an unknown distribution p, is it possible to distinguish whether p belongs to some class of distributions C versus p being far from every distribution in C? This fundamentalâ€¦ (More)

- Bryan Cai, Constantinos Daskalakis, Gautam Kamath
- ICML
- 2017

We develop differentially private hypothesis testing methods for the small sample regime. Given a sample D from a categorical distribution p over some domain Î£, an explicitly described distribution qâ€¦ (More)

- Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Zheng Li, Ankur Moitra, Alistair Stewart
- 2016 IEEE 57th Annual Symposium on Foundations ofâ€¦
- 2016

We study high-dimensional distribution learning in an agnostic setting where an adversary is allowed to arbitrarily corrupt an epsilon fraction of the samples. Such questions have a rich historyâ€¦ (More)

We analyze the Schelling model of segregation in which a society of n individuals live in a ring. Each individual is one of two races and is only satisfied with his location so long as at least halfâ€¦ (More)

- Constantinos Daskalakis, Gautam Kamath
- COLT
- 2014

We provide an algorithm for properly learning mixtures of two single-dimensional Gaussians without any separability assumptions. Given Ã•(1/Îµ) samples from an unknown mixture, our algorithm outputs aâ€¦ (More)

- Constantinos Daskalakis, Gautam Kamath, Christos Tzamos
- 2015 IEEE 56th Annual Symposium on Foundations ofâ€¦
- 2015

An (n, k)-Poisson Multinomial Distribution (PMD) is the distribution of the sum of n independent random vectors supported on the set Bk={e<sub>1</sub>,...,ek} of standard basis vectors inâ€¦ (More)

An (<i>n</i>,<i>k</i>)-<em>Poisson Multinomial Distribution</em> (PMD) is the distribution of the sum of <i>n</i> independent random vectors supported on the set <b>â€¦ (More)

- Jayadev Acharya, ClÃ©ment L. Canonne, Gautam Kamath
- APPROX-RANDOM
- 2014

A recent model for property testing of probability distributions [CFGM13, CRS15] enables tremendous savings in the sample complexity of testing algorithms, by allowing them to condition the samplingâ€¦ (More)

Given samples from an unknown multivariate distribution p, is it possible to distinguish whether p is the product of its marginals versus p being far from every product distribution? Similarly, is itâ€¦ (More)

Robust estimation is much more challenging in high dimensions than it is in one dimension: Most techniques either lead to intractable optimization problems or estimators that can tolerate only a tinyâ€¦ (More)