
Local AdaGradType Algorithm for Stochastic ConvexConcave Minimax Problems
Large scale convexconcave minimax problems arise in numerous applicatio...
read it

Robust Inference for HighDimensional Linear Models via Residual Randomization
We propose a residual randomization procedure designed for robust Lasso...
read it

Highdimensional Functional Graphical Model Structure Learning via Neighborhood Selection Approach
Undirected graphical models have been widely used to model the condition...
read it

Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning
In offline reinforcement learning (RL) an optimal policy is learnt solel...
read it

Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques
We study the optimization aspects of personalized Federated Learning (FL...
read it

An Adaptive Stochastic Sequential Quadratic Programming with Differentiable Exact Augmented Lagrangians
We consider the problem of solving nonlinear optimization programs with ...
read it

Provably Training Neural Network Classifiers under Fairness Constraints
Training a classifier under fairness constraints has gotten increasing a...
read it

A Nonconvex Framework for Structured Dynamic Covariance Recovery
We propose a flexible yet interpretable model for highdimensional data ...
read it

Statistical Inference for Networks of HighDimensional Point Processes
Fueled in part by recent applications in neuroscience, the multivariate ...
read it

Provably Efficient Neural Estimation of Structural Equation Model: An Adversarial Approach
Structural equation models (SEMs) are widely used in sciences, ranging f...
read it

FuDGE: Functional Differential Graph Estimation with fully and discretely observed curves
We consider the problem of estimating the difference between two functio...
read it

Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees
Graph representation learning is a ubiquitous task in machine learning w...
read it

Postselection inference on highdimensional varyingcoefficient quantile regression model
Quantile regression has been successfully used to study heterogeneous an...
read it

Posterior Ratio Estimation for Latent Variables
Density Ratio Estimation has attracted attention from machine learning c...
read it

Natural ActorCritic Converges Globally for Hierarchical Linear Quadratic Regulator
Multiagent reinforcement learning has been successfully applied to a nu...
read it

Constrained High Dimensional Statistical Inference
In typical high dimensional statistical inference problems, confidence i...
read it

Convergent Policy Optimization for Safe Reinforcement Learning
We study the safe reinforcement learning problem with nonlinear function...
read it

Direct Estimation of Differential Functional Graphical Models
We consider the problem of estimating the difference between two functio...
read it

Estimating Differential Latent Variable Graphical Models with Applications to Brain Connectivity
Differential graphical models are designed to represent the difference b...
read it

Tensor Canonical Correlation Analysis
In many applications, such as classification of images or videos, it is ...
read it

Partially Linear Additive Gaussian Graphical Models
We propose a partially linear additive Gaussian graphical model (PLAGGM...
read it

Simultaneous Inference for Pairwise Graphical Models with Generalized Score Matching
Probabilistic graphical models provide a flexible yet parsimonious frame...
read it

Twosample inference for highdimensional Markov networks
Markov networks are frequently used in sciences to represent conditional...
read it

Highdimensional Index Volatility Models via Stein's Identity
In this paper, we consider estimating the parametric components of index...
read it

Provable Gaussian Embedding with One Observation
The success of machine learning methods heavily relies on having an appr...
read it

Joint Nonparametric Precision Matrix Estimation with Confounding
We consider the problem of precision matrix estimation where, due to ext...
read it

Highdimensional Varying Index Coefficient Models via Stein's Identity
We study the parameter estimation problem for a singleindex varying coe...
read it

Learning InfluenceReceptivity Network Structure with Guarantee
Traditional works on community detection from observations of informatio...
read it

Recovery of simultaneous low rank and twoway sparse coefficient matrices, a nonconvex approach
We study the problem of recovery of matrices that are simultaneously low...
read it

Distributed Stochastic MultiTask Learning with Graph Regularization
We propose methods for distributed graphbased multitask learning that ...
read it

An InfluenceReceptivity Model for Topic based Information Cascades
We consider the problem of estimating the latent structure of a social n...
read it

Uniform Inference for Highdimensional Quantile Regression: Linear Functionals and Regression Rank Scores
Hypothesis tests in models whose dimension far exceeds the sample size c...
read it

Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and Highdimensional Data
Sketching techniques have become popular for scaling up machine learning...
read it

Efficient Distributed Learning with Sparsity
We propose a novel, efficient approach for distributed sparse learning i...
read it

Distributed MultiTask Learning with Shared Representation
We study the problem of distributed multitask learning with shared repr...
read it

PostRegularization Inference for Dynamic Nonparanormal Graphical Models
We propose a novel class of dynamic nonparanormal graphical models, whic...
read it

Distributed Multitask Learning
We consider the problem of distributed multitask learning, where each m...
read it

PostRegularization Confidence Bands for High Dimensional Nonparametric Models with Local Sparsity
We propose a novel high dimensional nonparametric model named ATLAS whic...
read it

A General Framework for Robust Testing and Confidence Regions in HighDimensional Quantile Regression
We propose a robust inferential procedure for assessing uncertainties of...
read it

Inference for Sparse Conditional Precision Matrices
Given n i.i.d. observations of a random vector (X,Z), where X is a high...
read it

Optimal variable selection in multigroup sparse discriminant analysis
This article considers the problem of multigroup classification in the ...
read it

Estimating Undirected Graphs Under Weak Assumptions
We consider the problem of providing nonparametric confidence guarantees...
read it

Optimal Feature Selection in HighDimensional Discriminant Analysis
We consider the highdimensional discriminant analysis problem. For this...
read it

Graph Estimation From Multiattribute Data
Many real world network problems often concern multivariate nodal attrib...
read it

Recovering Blockstructured Activations Using Compressive Measurements
We consider the problems of detection and localization of a contiguous b...
read it

Variance function estimation in highdimensions
We consider the highdimensional heteroscedastic regression model, where...
read it

Ultrahigh Dimensional Multiple Output Learning With Simultaneous Orthogonal Matching Pursuit: A Sure Screening Approach
We propose a novel application of the Simultaneous Orthogonal Matching P...
read it

Estimating Networks With Jumps
We study the problem of estimating a temporally varying coefficient and ...
read it

Union Support Recovery in Multitask Learning
We sharply characterize the performance of different penalization scheme...
read it
Mladen Kolar
is this you? claim profile
Associate Professor at University of Chicago Booth School of Business