All reports by Author Jayadev Acharya:

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TR19-098
| 20th July 2019
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Jayadev Acharya, Clement Canonne, Yanjun Han, Ziteng Sun, Himanshu Tyagi#### Domain Compression and its Application to Randomness-Optimal Distributed Goodness-of-Fit

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TR18-079
| 19th April 2018
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Jayadev Acharya, Clement Canonne, Himanshu Tyagi#### Distributed Simulation and Distributed Inference

Revisions: 1

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TR16-186
| 19th November 2016
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Jayadev Acharya, Hirakendu Das, Alon Orlitsky, Ananda Theertha Suresh#### A Unified Maximum Likelihood Approach for Optimal Distribution Property Estimation

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TR14-156
| 26th November 2014
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Jayadev Acharya, Clement Canonne, Gautam Kamath#### A Chasm Between Identity and Equivalence Testing with Conditional Queries

Revisions: 2

Jayadev Acharya, Clement Canonne, Yanjun Han, Ziteng Sun, Himanshu Tyagi

We study goodness-of-fit of discrete distributions in the distributed setting, where samples are divided between multiple users who can only release a limited amount of information about their samples due to various information constraints. Recently, a subset of the authors showed that having access to a common random seed (i.e., ... more >>>

Jayadev Acharya, Clement Canonne, Himanshu Tyagi

Independent samples from an unknown probability distribution $\mathbf{p}$ on a domain of size $k$ are distributed across $n$ players, with each player holding one sample. Each player can communicate $\ell$ bits to a central referee in a simultaneous message passing (SMP) model of communication to help the referee infer a ... more >>>

Jayadev Acharya, Hirakendu Das, Alon Orlitsky, Ananda Theertha Suresh

The advent of data science has spurred interest in estimating properties of discrete distributions over large alphabets. Fundamental symmetric properties such as support size, support coverage, entropy, and proximity to uniformity, received most attention, with each property estimated using a different technique and often intricate analysis tools.

Motivated by the ... more >>>

Jayadev Acharya, Clement Canonne, Gautam Kamath

A recent model for property testing of probability distributions enables tremendous savings in the sample complexity of testing algorithms, by allowing them to condition the sampling on subsets of the domain.

In particular, Canonne et al. showed that, in this setting, testing identity of an unknown distribution $D$ (i.e., ...
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