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01/30/2023
Robust empirical risk minimization via Newton's method
We study a variant of Newton's method for empirical risk minimization, w...
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01/09/2023
Simple Binary Hypothesis Testing under Local Differential Privacy and Communication Constraints
We study simple binary hypothesis testing under both local differential ...
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06/06/2022
Communication-constrained hypothesis testing: Optimality, robustness, and reverse data processing inequalities
We study hypothesis testing under communication constraints, where each ...
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01/31/2022
On the identifiability of mixtures of ranking models
Mixtures of ranking models are standard tools for ranking problems. Howe...
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03/19/2021
Differentially private inference via noisy optimization
We propose a general optimization-based framework for computing differen...
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01/20/2021
Robust W-GAN-Based Estimation Under Wasserstein Contamination
Robust estimation is an important problem in statistics which aims at pr...
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09/27/2020
Robust regression with covariate filtering: Heavy tails and adversarial contamination
We study the problem of linear regression where both covariates and resp...
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06/16/2020
Theory of Machine Learning Debugging via M-estimation
We investigate problems in penalized M-estimation, inspired by applicati...
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01/31/2020
Boosting Algorithms for Estimating Optimal Individualized Treatment Rules
We present nonparametric algorithms for estimating optimal individualize...
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01/29/2020
Adaptive Estimation and Statistical Inference for High-Dimensional Graph-Based Linear Models
We consider adaptive estimation and statistical inference for high-dimen...
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10/15/2019
Extracting robust and accurate features via a robust information bottleneck
We propose a novel strategy for extracting features in supervised learni...
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08/01/2019
Robustifying deep networks for image segmentation
Purpose: The purpose of this study is to investigate the robustness of a...
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07/06/2019
Estimating location parameters in entangled single-sample distributions
We consider the problem of estimating the common mean of independently s...
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05/08/2019
Does Data Augmentation Lead to Positive Margin?
Data augmentation (DA) is commonly used during model training, as it sig...
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11/06/2018
Scale calibration for high-dimensional robust regression
We present a new method for high-dimensional linear regression when a sc...
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10/22/2018
Adversarial Risk Bounds for Binary Classification via Function Transformation
We derive new bounds for a notion of adversarial risk, characterizing th...
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06/13/2018
A theory of maximum likelihood for weighted infection graphs
We study the problem of parameter estimation based on infection data fro...
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04/01/2018
Online learning with graph-structured feedback against adaptive adversaries
We derive upper and lower bounds for the policy regret of T-round online...
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02/13/2018
Graph-Based Ascent Algorithms for Function Maximization
We study the problem of finding the maximum of a function defined on the...
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01/12/2018
Generalization Error Bounds for Noisy, Iterative Algorithms
In statistical learning theory, generalization error is used to quantify...
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11/01/2016
Computationally Efficient Influence Maximization in Stochastic and Adversarial Models: Algorithms and Analysis
We consider the problem of influence maximization in fixed networks, for...
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10/19/2015
Confidence Sets for the Source of a Diffusion in Regular Trees
We study the problem of identifying the source of a diffusion spreading ...
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01/10/2015
On model misspecification and KL separation for Gaussian graphical models
We establish bounds on the KL divergence between two multivariate Gaussi...
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01/01/2015
Statistical consistency and asymptotic normality for high-dimensional robust M-estimators
We study theoretical properties of regularized robust M-estimators, appl...
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12/17/2014
Support recovery without incoherence: A case for nonconvex regularization
We demonstrate that the primal-dual witness proof method may be used to ...
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10/26/2014
Concavity of reweighted Kikuchi approximation
We analyze a reweighted version of the Kikuchi approximation for estimat...
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11/14/2013
High-dimensional learning of linear causal networks via inverse covariance estimation
We establish a new framework for statistical estimation of directed acyc...
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05/10/2013
Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima
We provide novel theoretical results regarding local optima of regulariz...
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12/03/2012
Structure estimation for discrete graphical models: Generalized covariance matrices and their inverses
We investigate the relationship between the structure of a discrete grap...
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09/16/2011
