Search Results for author: Anand D. Sarwate

Found 17 papers, 2 papers with code

A Minimax Lower Bound for Low-Rank Matrix-Variate Logistic Regression

no code implementations31 May 2021 Batoul Taki, Mohsen Ghassemi, Anand D. Sarwate, Waheed U. Bajwa

The focus of this paper is on derivation of a minimax risk lower bound for low-rank coefficient matrices.

Influencers and the Giant Component: the Fundamental Hardness in Privacy Protection for Socially Contagious Attributes

no code implementations22 Dec 2020 Aria Rezaei, Jie Gao, Anand D. Sarwate

Experiments demonstrate that a giant connected component of infected nodes can and does appear in real-world networks and that a simple inference attack can reveal the status of a good fraction of nodes.

Inference Attack Social and Information Networks

Quantile Multi-Armed Bandits: Optimal Best-Arm Identification and a Differentially Private Scheme

no code implementations11 Jun 2020 Kontantinos E. Nikolakakis, Dionysios S. Kalogerias, Or Sheffet, Anand D. Sarwate

First, we propose a (non-private) successive elimination algorithm for strictly optimal best-arm identification, we show that our algorithm is $\delta$-PAC and we characterize its sample complexity.

Multi-Armed Bandits

Improved Differentially Private Decentralized Source Separation for fMRI Data

no code implementations28 Oct 2019 Hafiz Imtiaz, Jafar Mohammadi, Rogers Silva, Bradley Baker, Sergey M. Plis, Anand D. Sarwate, Vince Calhoun

In this work, we propose a differentially private algorithm for performing ICA in a decentralized data setting.

Optimal Rates for Learning Hidden Tree Structures

no code implementations20 Sep 2019 Konstantinos E. Nikolakakis, Dionysios S. Kalogerias, Anand D. Sarwate

Specifically, we show that the finite sample complexity of the Chow-Liu algorithm for ensuring exact structure recovery from noisy data is inversely proportional to the information threshold squared (provided it is positive), and scales almost logarithmically relative to the number of nodes over a given probability of failure.

Distributed Differentially Private Computation of Functions with Correlated Noise

2 code implementations22 Apr 2019 Hafiz Imtiaz, Jafar Mohammadi, Anand D. Sarwate

CAPE can be used in conjunction with the functional mechanism for statistical and machine learning optimization problems.

Learning Mixtures of Separable Dictionaries for Tensor Data: Analysis and Algorithms

1 code implementation22 Mar 2019 Mohsen Ghassemi, Zahra Shakeri, Anand D. Sarwate, Waheed U. Bajwa

This work addresses the problem of learning sparse representations of tensor data using structured dictionary learning.

Dictionary Learning

Predictive Learning on Hidden Tree-Structured Ising Models

no code implementations11 Dec 2018 Konstantinos E. Nikolakakis, Dionysios S. Kalogerias, Anand D. Sarwate

In the absence of noise, predictive learning on Ising models was recently studied by Bresler and Karzand (2020); this paper quantifies how noise in the hidden model impacts the tasks of structure recovery and marginal distribution estimation by proving upper and lower bounds on the sample complexity.

Identifiability of Kronecker-structured Dictionaries for Tensor Data

no code implementations10 Dec 2017 Zahra Shakeri, Anand D. Sarwate, Waheed U. Bajwa

This paper derives sufficient conditions for local recovery of coordinate dictionaries comprising a Kronecker-structured dictionary that is used for representing $K$th-order tensor data.

STARK: Structured Dictionary Learning Through Rank-one Tensor Recovery

no code implementations13 Nov 2017 Mohsen Ghassemi, Zahra Shakeri, Anand D. Sarwate, Waheed U. Bajwa

In recent years, a class of dictionaries have been proposed for multidimensional (tensor) data representation that exploit the structure of tensor data by imposing a Kronecker structure on the dictionary underlying the data.

Dictionary Learning

Minimax Lower Bounds for Kronecker-Structured Dictionary Learning

no code implementations17 May 2016 Zahra Shakeri, Waheed U. Bajwa, Anand D. Sarwate

This paper finds fundamental limits on the sample complexity of estimating dictionaries for tensor data by proving a lower bound on the minimax risk.

Dictionary Learning

High Dimensional Inference with Random Maximum A-Posteriori Perturbations

no code implementations10 Feb 2016 Tamir Hazan, Francesco Orabona, Anand D. Sarwate, Subhransu Maji, Tommi Jaakkola

This paper shows that the expected value of perturb-max inference with low dimensional perturbations can be used sequentially to generate unbiased samples from the Gibbs distribution.

Learning from Data with Heterogeneous Noise using SGD

no code implementations17 Dec 2014 Shuang Song, Kamalika Chaudhuri, Anand D. Sarwate

In this paper, we adopt instead a model in which data is observed through heterogeneous noise, where the noise level reflects the quality of the data source.

On Measure Concentration of Random Maximum A-Posteriori Perturbations

no code implementations15 Oct 2013 Francesco Orabona, Tamir Hazan, Anand D. Sarwate, Tommi Jaakkola

Applying the general result to MAP perturbations can yield a more efficient algorithm to approximate sampling from the Gibbs distribution.

Auditing: Active Learning with Outcome-Dependent Query Costs

no code implementations NeurIPS 2013 Sivan Sabato, Anand D. Sarwate, Nathan Srebro

We term the setting auditing, and consider the auditing complexity of an algorithm: the number of negative labels the algorithm requires in order to learn a hypothesis with low relative error.

Active Learning Fraud Detection +1

Near-Optimal Algorithms for Differentially-Private Principal Components

no code implementations12 Jul 2012 Kamalika Chaudhuri, Anand D. Sarwate, Kaushik Sinha

In this paper we investigate the theory and empirical performance of differentially private approximations to PCA and propose a new method which explicitly optimizes the utility of the output.

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