activity
20152026
most citedComparison of Channels: Criteria for Domination by a Symmetric Channel

38 citations · 176 across the 26 of their papers we have counts for

collaborators

26 papers

cs.LG2026

On the Oracle Complexity of Interpolation-Based Gradient Descent

Dongmin Lee, William Lu, Anuran Makur

Recent work on first-order optimizers for empirical risk minimization (ERM) has suggested that smoothness of ERM loss functions in the training data, rather than in the optimizatio…

cs.IT2026

Doeblin Curves

Dongmin Lee, William Lu, Anuran Makur +1

Recent research on Doeblin coefficients has shed light on their usefulness as a multi-way generalization of the Dobrushin contraction coefficient for TV distance, in a separate vei…

cs.LG2026

Entrywise Error Bounds for Spectral Ranking with Semi-Random Adversaries

Dongmin Lee, Anuran Makur, Japneet Singh

Bradley-Terry-Luce (BTL) model estimation is a well-established strategy to rank a collection of items given a dataset of pairwise comparisons. Although the theoretical performance…

cs.LG2026

Unified High-Probability Analysis of Stochastic Variance-Reduced Estimation

Zhankun Luo, Antesh Upadhyay, M. Berk Sahin +3

Stochastic estimators are fundamental to large-scale optimization, where population quantities must be inferred from noisy oracle observations. Although influential methods such as…

cs.GT2026

Fair Division Under Inaccurate Preferences

Trung Dang, Daniel Halpern, Anuran Makur +3

The fair allocation of scarce resources is a central problem in mathematics, computer science, operations research, and economics. While much of the fair-division literature assume…

cs.IT2025

Bounds on Maximal Leakage over Bayesian Networks

Anuran Makur, Japneet Singh

Maximal leakage quantifies the leakage of information from data due to an observation . While fundamental properties of maximal leakage, such as data process…

cs.LG2025

Hypothesis Testing for Generalized Thurstone Models

Anuran Makur, Japneet Singh

In this work, we develop a hypothesis testing framework to determine whether pairwise comparison data is generated by an underlying \emph{generalized Thurstone model} $\mathcal{T}_…

cs.LG2024★ 2 cited

Minimax Hypothesis Testing for the Bradley-Terry-Luce Model

Anuran Makur, Japneet Singh

The Bradley-Terry-Luce (BTL) model is one of the most widely used models for ranking a collection of items or agents based on pairwise comparisons among them. Given agents, the…

cs.GT2023

On the Robustness of Mechanism Design under Total Variation Distance

Anuran Makur, Marios Mertzanidis, Alexandros Psomas +1

We study the problem of designing mechanisms when agents' valuation functions are drawn from unknown and correlated prior distributions. In particular, we are given a prior distrib…

cs.IT2023★ 3 cited

Doeblin Coefficients and Related Measures

Anuran Makur, Japneet Singh

Doeblin coefficients are a classical tool for analyzing the ergodicity and exponential convergence rates of Markov chains. Propelled by recent works on contraction coefficients of…

cs.IT2023★ 7 cited

Permutation Capacity Region of Adder Multiple-Access Channels

William Lu, Anuran Makur

Point-to-point permutation channels are useful models of communication networks and biological storage mechanisms and have received theoretical attention in recent years. Propelled…

cs.LG2022★ 2 cited

Gradient Descent for Low-Rank Functions

Romain Cosson, Ali Jadbabaie, Anuran Makur +2

Several recent empirical studies demonstrate that important machine learning tasks, e.g., training deep neural networks, exhibit low-rank structure, where the loss function varies…

cs.LG2022★ 1 cited

Functional Linear Regression of Cumulative Distribution Functions

Qian Zhang, Anuran Makur, Kamyar Azizzadenesheli

The estimation of cumulative distribution functions (CDF) is an important learning task with a great variety of downstream applications, such as risk assessments in predictions and…

cs.LG2022★ 12 cited

Federated Optimization of Smooth Loss Functions

Ali Jadbabaie, Anuran Makur, Devavrat Shah

In this work, we study empirical risk minimization (ERM) within a federated learning framework, where a central server minimizes an ERM objective function using training data that…

eess.SY2021

Inference in Opinion Dynamics under Social Pressure

Ali Jadbabaie, Anuran Makur, Elchanan Mossel +1

We introduce a new opinion dynamics model where a group of agents holds two kinds of opinions: inherent and declared. Each agent's inherent opinion is fixed and unobservable by the…

cs.LG2020★ 4 cited

Gradient-Based Empirical Risk Minimization using Local Polynomial Regression

Ali Jadbabaie, Anuran Makur, Devavrat Shah

In this paper, we consider the problem of empirical risk minimization (ERM) of smooth, strongly convex loss functions using iterative gradient-based methods. A major goal of this l…

math.PR2020★ 5 cited

Broadcasting on Two-Dimensional Regular Grids

Anuran Makur, Elchanan Mossel, Yury Polyanskiy

We study a specialization of the problem of broadcasting on directed acyclic graphs, namely, broadcasting on 2D regular grids. Consider a 2D regular grid with source vertex at…

cs.IT2020★ 37 cited

Coding Theorems for Noisy Permutation Channels

Anuran Makur

In this paper, we formally define and analyze the class of noisy permutation channels. The noisy permutation channel model constitutes a standard discrete memoryless channel (DMC)…

stat.ML2020★ 1 cited

Estimation of Skill Distributions

Ali Jadbabaie, Anuran Makur, Devavrat Shah

In this paper, we study the problem of learning the skill distribution of a population of agents from observations of pairwise games in a tournament. These games are played among r…

cs.LG2019★ 31 cited

On Universal Features for High-Dimensional Learning and Inference

Shao-Lun Huang, Anuran Makur, Gregory W. Wornell +1

We consider the problem of identifying universal low-dimensional features from high-dimensional data for inference tasks in settings involving learning. For such problems, we intro…

cs.IT2018★ 20 cited

Broadcasting on Random Directed Acyclic Graphs

Anuran Makur, Elchanan Mossel, Yury Polyanskiy

We study a generalization of the well-known model of broadcasting on trees. Consider a directed acyclic graph (DAG) with a unique source vertex , and suppose all other vertices…

cs.LG2018

Probabilistic Clustering Using Maximal Matrix Norm Couplings

David Qiu, Anuran Makur, Lizhong Zheng

In this paper, we present a local information theoretic approach to explicitly learn probabilistic clustering of a discrete random variable. Our formulation yields a convex maximiz…

cs.IT2018★ 1 cited

Broadcasting on Bounded Degree DAGs

Anuran Makur, Elchanan Mossel, Yury Polyanskiy

We study the following generalization of the well-known model of broadcasting on trees. Consider an infinite directed acyclic graph (DAG) with a unique source node . Let the col…

cs.IT2016★ 38 cited

Comparison of Channels: Criteria for Domination by a Symmetric Channel

Anuran Makur, Yury Polyanskiy

This paper studies the basic question of whether a given channel can be dominated (in the precise sense of being more noisy) by a -ary symmetric channel. The concept of "les…