Search Results for author: Jayadev Acharya

Found 32 papers, 3 papers with code

Context Aware Local Differential Privacy

no code implementations ICML 2020 Jayadev Acharya, Kallista Bonawitz, Peter Kairouz, Daniel Ramage, Ziteng Sun

The original definition of LDP assumes that all the elements in the data domain are equally sensitive.

Principal Bit Analysis: Autoencoding with Schur-Concave Loss

1 code implementation5 Jun 2021 Sourbh Bhadane, Aaron B. Wagner, Jayadev Acharya

As one application, we consider a strictly Schur-concave constraint that estimates the number of bits needed to represent the latent variables under fixed-rate encoding, a setup that we call \emph{Principal Bit Analysis (PBA)}.

Robust Testing and Estimation under Manipulation Attacks

no code implementations21 Apr 2021 Jayadev Acharya, Ziteng Sun, Huanyu Zhang

We consider both the "centralized setting" and the "distributed setting with information constraints" including communication and local privacy (LDP) constraints.

Estimating Sparse Discrete Distributions Under Local Privacy and Communication Constraints

no code implementations30 Oct 2020 Jayadev Acharya, Peter Kairouz, YuHan Liu, Ziteng Sun

We consider the problem of estimating sparse discrete distributions under local differential privacy (LDP) and communication constraints.

Unified lower bounds for interactive high-dimensional estimation under information constraints

no code implementations13 Oct 2020 Jayadev Acharya, Clément L. Canonne, Ziteng Sun, Himanshu Tyagi

We consider the task of distributed parameter estimation using interactive protocols subject to local information constraints such as bandwidth limitations, local differential privacy, and restricted measurements.

Interactive Inference under Information Constraints

no code implementations21 Jul 2020 Jayadev Acharya, Clément L. Canonne, Yu-Han Liu, Ziteng Sun, Himanshu Tyagi

We study the role of interactivity in distributed statistical inference under information constraints, e. g., communication constraints and local differential privacy.

Density Estimation

Differentially Private Assouad, Fano, and Le Cam

no code implementations14 Apr 2020 Jayadev Acharya, Ziteng Sun, Huanyu Zhang

The technical component of our paper relates coupling between distributions to the sample complexity of estimation under differential privacy.

Estimating Entropy of Distributions in Constant Space

no code implementations NeurIPS 2019 Jayadev Acharya, Sourbh Bhadane, Piotr Indyk, Ziteng Sun

We consider the task of estimating the entropy of $k$-ary distributions from samples in the streaming model, where space is limited.

Context-Aware Local Differential Privacy

no code implementations31 Oct 2019 Jayadev Acharya, Keith Bonawitz, Peter Kairouz, Daniel Ramage, Ziteng Sun

Local differential privacy (LDP) is a strong notion of privacy for individual users that often comes at the expense of a significant drop in utility.

Domain Compression and its Application to Randomness-Optimal Distributed Goodness-of-Fit

no code implementations20 Jul 2019 Jayadev Acharya, Clément L. 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.

Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters

no code implementations28 May 2019 Jayadev Acharya, Ziteng Sun

We consider the problems of distribution estimation and heavy hitter (frequency) estimation under privacy and communication constraints.

Inference under Information Constraints II: Communication Constraints and Shared Randomness

no code implementations20 May 2019 Jayadev Acharya, Clément L. Canonne, Himanshu Tyagi

We propose a general-purpose simulate-and-infer strategy that uses only private-coin communication protocols and is sample-optimal for distribution learning.

Distributed Learning with Sublinear Communication

no code implementations28 Feb 2019 Jayadev Acharya, Christopher De Sa, Dylan J. Foster, Karthik Sridharan

In distributed statistical learning, $N$ samples are split across $m$ machines and a learner wishes to use minimal communication to learn as well as if the examples were on a single machine.

Quantization

Inference under Information Constraints I: Lower Bounds from Chi-Square Contraction

no code implementations30 Dec 2018 Jayadev Acharya, Clément L. Canonne, Himanshu Tyagi

Underlying our bounds is a characterization of the contraction in chi-square distances between the observed distributions of the samples when information constraints are placed.

Test without Trust: Optimal Locally Private Distribution Testing

no code implementations7 Aug 2018 Jayadev Acharya, Clément L. Canonne, Cody Freitag, Himanshu Tyagi

We are concerned with two settings: First, when we insist on using an already deployed, general-purpose locally differentially private mechanism such as the popular RAPPOR or the recently introduced Hadamard Response for collecting data, and must build our tests based on the data collected via this mechanism; and second, when no such restriction is imposed, and we can design a bespoke mechanism specifically for testing.

Distributed Simulation and Distributed Inference

no code implementations19 Apr 2018 Jayadev Acharya, Clément L. Canonne, Himanshu Tyagi

Nonetheless, we present a Las Vegas algorithm that simulates a single sample from the unknown distribution using $O(k/2^\ell)$ samples in expectation.

INSPECTRE: Privately Estimating the Unseen

1 code implementation ICML 2018 Jayadev Acharya, Gautam Kamath, Ziteng Sun, Huanyu Zhang

We develop differentially private methods for estimating various distributional properties.

Hadamard Response: Estimating Distributions Privately, Efficiently, and with Little Communication

3 code implementations13 Feb 2018 Jayadev Acharya, Ziteng Sun, Huanyu Zhang

All previously known sample optimal algorithms require linear (in $k$) communication from each user in the high privacy regime $(\varepsilon=O(1))$, and run in time that grows as $n\cdot k$, which can be prohibitive for large domain size $k$.

A Unified Maximum Likelihood Approach for Estimating Symmetric Properties of Discrete Distributions

no code implementations ICML 2017 Jayadev Acharya, Hirakendu Das, Alon Orlitsky, Ananda Theertha Suresh

Symmetric distribution properties such as support size, support coverage, entropy, and proximity to uniformity, arise in many applications.

Differentially Private Testing of Identity and Closeness of Discrete Distributions

no code implementations NeurIPS 2018 Jayadev Acharya, Ziteng Sun, Huanyu Zhang

We propose a general framework to establish lower bounds on the sample complexity of statistical tasks under differential privacy.

A Unified Maximum Likelihood Approach for Optimal Distribution Property Estimation

no code implementations9 Nov 2016 Jayadev Acharya, Hirakendu Das, Alon Orlitsky, Ananda Theertha Suresh

The advent of data science has spurred interest in estimating properties of distributions over large alphabets.

Fast Algorithms for Segmented Regression

no code implementations14 Jul 2016 Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt

We study the fixed design segmented regression problem: Given noisy samples from a piecewise linear function $f$, we want to recover $f$ up to a desired accuracy in mean-squared error.

Optimal Testing for Properties of Distributions

no code implementations NeurIPS 2015 Jayadev Acharya, Constantinos Daskalakis, Gautam Kamath

Given samples from an unknown distribution $p$, is it possible to distinguish whether $p$ belongs to some class of distributions $\mathcal{C}$ versus $p$ being far from every distribution in $\mathcal{C}$?

Sample-Optimal Density Estimation in Nearly-Linear Time

no code implementations1 Jun 2015 Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt

Let $f$ be the density function of an arbitrary univariate distribution, and suppose that $f$ is $\mathrm{OPT}$-close in $L_1$-distance to an unknown piecewise polynomial function with $t$ interval pieces and degree $d$.

Density Estimation

A Chasm Between Identity and Equivalence Testing with Conditional Queries

no code implementations26 Nov 2014 Jayadev Acharya, Clément L. Canonne, Gautam Kamath

We answer a question of Chakraborty et al. (ITCS 2013) showing that non-adaptive uniformity testing indeed requires $\Omega(\log n)$ queries in the conditional model.

Testing Poisson Binomial Distributions

no code implementations13 Oct 2014 Jayadev Acharya, Constantinos Daskalakis

We provide a sample near-optimal algorithm for testing whether a distribution $P$ supported on $\{0,..., n\}$ to which we have sample access is a Poisson Binomial distribution, or far from all Poisson Binomial distributions.

Estimating Renyi Entropy of Discrete Distributions

no code implementations2 Aug 2014 Jayadev Acharya, Alon Orlitsky, Ananda Theertha Suresh, Himanshu Tyagi

It was recently shown that estimating the Shannon entropy $H({\rm p})$ of a discrete $k$-symbol distribution ${\rm p}$ requires $\Theta(k/\log k)$ samples, a number that grows near-linearly in the support size.

Universal Compression of Envelope Classes: Tight Characterization via Poisson Sampling

no code implementations29 May 2014 Jayadev Acharya, Ashkan Jafarpour, Alon Orlitsky, Ananda Theertha Suresh

The Poisson-sampling technique eliminates dependencies among symbol appearances in a random sequence.

Near-optimal-sample estimators for spherical Gaussian mixtures

no code implementations NeurIPS 2014 Jayadev Acharya, Ashkan Jafarpour, Alon Orlitsky, Ananda Theertha Suresh

For mixtures of any $k$ $d$-dimensional spherical Gaussians, we derive an intuitive spectral-estimator that uses $\mathcal{O}_k\bigl(\frac{d\log^2d}{\epsilon^4}\bigr)$ samples and runs in time $\mathcal{O}_{k,\epsilon}(d^3\log^5 d)$, both significantly lower than previously known.

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