32 citations · 96 across the 21 of their papers we have counts for
29 papers
On the Sample Complexity of Robust Binary Hypothesis Testing
Shankar Vallinayagam, Ankit Pensia, Varun Jog
We study the sample complexity of robust binary hypothesis testing under three standard contamination models: -additive (Huber), -subtractive, and $\varep…
Communication-constrained hypothesis testing: Optimality, robustness, and reverse data processing inequalities
Ankit Pensia, Varun Jog, Po-Ling Loh
We study hypothesis testing under communication constraints, where each sample is quantized before being revealed to a statistician. Without communication constraints, it is well k…
Robust regression with covariate filtering: Heavy tails and adversarial contamination
Ankit Pensia, Varun Jog, Po-Ling Loh
We study the problem of linear regression where both covariates and responses are potentially (i) heavy-tailed and (ii) adversarially contaminated. Several computationally efficien…
Reverse Euclidean and Gaussian isoperimetric inequalities for parallel sets with applications
Varun Jog
The -parallel set of a measurable set is the set of all points whose distance from is at most . In this paper, we show that the surface area of…
Adversarial Risk via Optimal Transport and Optimal Couplings
Muni Sreenivas Pydi, Varun Jog
Modern machine learning algorithms perform poorly on adversarially manipulated data. Adversarial risk quantifies the error of classifiers in adversarial settings; adversarial class…
Extracting robust and accurate features via a robust information bottleneck
Ankit Pensia, Varun Jog, Po-Ling Loh
We propose a novel strategy for extracting features in supervised learning that can be used to construct a classifier which is more robust to small perturbations in the input space…
Robustifying deep networks for image segmentation
Zheng Liu, Jinnian Zhang, Varun Jog +2
Purpose: The purpose of this study is to investigate the robustness of a commonly-used convolutional neural network for image segmentation with respect to visually-subtle adversari…
Estimating location parameters in entangled single-sample distributions
Ankit Pensia, Varun Jog, Po-Ling Loh
We consider the problem of estimating the common mean of independently sampled data, where samples are drawn in a possibly non-identical manner from symmetric, unimodal distributio…
Channels, Remote Estimation and Queueing Systems With A Utilization-Dependent Component: A Unifying Survey Of Recent Results
Varun Jog, Richard J. La, Michael Lin +1
In this article, we survey the main models, techniques, concepts, and results centered on the design and performance evaluation of engineered systems that rely on a utilization-dep…
Dual Loomis-Whitney inequalities via information theory
Jing Hao, Varun Jog
We establish lower bounds on the volume and the surface area of a geometric body using the size of its slices along different directions. In the first part of the paper, we derive…
Teaching and learning in uncertainty
Varun Jog, Po-Ling Loh
We investigate a simple model for social learning with two agents: a teacher and a student. The teacher's goal is to teach the student the state of the world; however, the teacher…
Unifying the Brascamp-Lieb Inequality and the Entropy Power Inequality
Venkat Anantharam, Varun Jog, Chandra Nair
The entropy power inequality (EPI) and the Brascamp-Lieb inequality (BLI) are fundamental inequalities concerning the differential entropies of linear transformations of random vec…
Convexity of mutual information along the Ornstein-Uhlenbeck flow
Andre Wibisono, Varun Jog
We study the convexity of mutual information as a function of time along the flow of the Ornstein-Uhlenbeck process. We prove that if the initial distribution is strongly log-conca…
Graph-Based Ascent Algorithms for Function Maximization
Muni Sreenivas Pydi, Varun Jog, Po-Ling Loh
We study the problem of finding the maximum of a function defined on the nodes of a connected graph. The goal is to identify a node where the function obtains its maximum. We focus…
Generalization Error Bounds for Noisy, Iterative Algorithms
Ankit Pensia, Varun Jog, Po-Ling Loh
In statistical learning theory, generalization error is used to quantify the degree to which a supervised machine learning algorithm may overfit to training data. Recent work [Xu a…
An entropy inequality for symmetric random variables
Jing Hao, Varun Jog
We establish a lower bound on the entropy of weighted sums of (possibly dependent) random variables possessing a symmetric joint distribution. Our lower bo…
Convexity of mutual information along the heat flow
Andre Wibisono, Varun Jog
We study the convexity of mutual information along the evolution of the heat equation. We prove that if the initial distribution is log-concave, then mutual information is always a…
Optimal Rates for Community Estimation in the Weighted Stochastic Block Model
Min Xu, Varun Jog, Po-Ling Loh
Community identification in a network is an important problem in fields such as social science, neuroscience, and genetics. Over the past decade, stochastic block models (SBMs) hav…
Information and estimation in Fokker-Planck channels
Andre Wibisono, Varun Jog, Po-Ling Loh
We study the relationship between information- and estimation-theoretic quantities in time-evolving systems. We focus on the Fokker-Planck channel defined by a general stochastic d…
Intrinsic entropies of log-concave distributions
Varun Jog, Venkat Anantharam
The entropy of a random variable is well-known to equal the exponential growth rate of the volumes of its typical sets. In this paper, we show that for any log-concave random varia…
Adversarial Influence Maximization
Justin Khim, Varun Jog, Po-Ling Loh
We consider the problem of influence maximization in fixed networks for contagion models in an adversarial setting. The goal is to select an optimal set of nodes to seed the influe…
Analysis of centrality in sublinear preferential attachment trees via the CMJ branching process
Varun Jog, Po-Ling Loh
We investigate centrality and root-inference properties in a class of growing random graphs known as sublinear preferential attachment trees. We show that a continuous time branchi…
Persistence of centrality in random growing trees
Varun Jog, Po-Ling Loh
We investigate properties of node centrality in random growing tree models. We focus on a measure of centrality that computes the maximum subtree size of the tree rooted at each no…
Information-theoretic bounds for exact recovery in weighted stochastic block models using the Renyi divergence
Varun Jog, Po-Ling Loh
We derive sharp thresholds for exact recovery of communities in a weighted stochastic block model, where observations are collected in the form of a weighted adjacency matrix, and…