b'Mladen Kolar'

research

∙ 06/05/2023

Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm

High-quality machine learning models are dependent on access to high-qua...

0 Boxin Zhao, et al. ∙

research

∙ 05/28/2023

Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching

We consider solving equality-constrained nonlinear, nonconvex optimizati...

0 Ilgee Hong, et al. ∙

research

∙ 05/23/2023

Confidence Sets for Causal Orderings

Causal discovery procedures aim to deduce causal relationships among var...

0 Y. Samuel Wang, et al. ∙

research

∙ 11/29/2022

Fully Stochastic Trust-Region Sequential Quadratic Programming for Equality-Constrained Optimization Problems

We propose a trust-region stochastic sequential quadratic programming al...

0 Yuchen Fang, et al. ∙

research

∙ 10/31/2022

Latent Multimodal Functional Graphical Model Estimation

Joint multimodal functional data acquisition, where functional data from...

0 Katherine Tsai, et al. ∙

research

∙ 08/29/2022

On the Lasso for Graphical Continuous Lyapunov Models

Graphical continuous Lyapunov models offer a new perspective on modeling...

0 Philipp Dettling, et al. ∙

research

∙ 05/31/2022

One Policy is Enough: Parallel Exploration with a Single Policy is Minimax Optimal for Reward-Free Reinforcement Learning

While parallelism has been extensively used in Reinforcement Learning (R...

0 Pedro Cisneros-Velarde, et al. ∙

research

∙ 05/05/2022

Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning

Dynamic mechanism design has garnered significant attention from both co...

1 Boxiang Lyu, et al. ∙

research

∙ 04/28/2022

Personalized Federated Learning with Multiple Known Clusters

We consider the problem of personalized federated learning when there ar...

0 Boxiang Lyu, et al. ∙

research

∙ 01/31/2022

L-SVRG and L-Katyusha with Adaptive Sampling

Stochastic gradient-based optimization methods, such as L-SVRG and its a...

0 Boxin Zhao, et al. ∙

research

∙ 12/28/2021

Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback

In federated learning (FL) problems, client sampling plays a key role in...

0 Boxin Zhao, et al. ∙

research

∙ 11/06/2021

Dynamic Regret Minimization for Control of Non-stationary Linear Dynamical Systems

We consider the problem of controlling a Linear Quadratic Regulator (LQR...

9 Yuwei Luo, et al. ∙

research

∙ 10/19/2021

Joint Gaussian Graphical Model Estimation: A Survey

Graphs from complex systems often share a partial underlying structure a...

0 Katherine Tsai, et al. ∙

research

∙ 09/23/2021

Inequality Constrained Stochastic Nonlinear Optimization via Active-Set Sequential Quadratic Programming

We study nonlinear optimization problems with stochastic objective and d...

0 Sen Na, et al. ∙

research

∙ 06/18/2021

Local AdaGrad-Type Algorithm for Stochastic Convex-Concave Minimax Problems

Large scale convex-concave minimax problems arise in numerous applicatio...

4 Luofeng Liao, et al. ∙

research

∙ 06/14/2021

Robust Inference for High-Dimensional Linear Models via Residual Randomization

We propose a residual randomization procedure designed for robust Lasso-...

0 Y. Samuel Wang, et al. ∙

research

∙ 05/06/2021

High-dimensional Functional Graphical Model Structure Learning via Neighborhood Selection Approach

Undirected graphical models have been widely used to model the condition...

0 Boxin Zhao, et al. ∙

research

∙ 02/19/2021

Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning

In offline reinforcement learning (RL) an optimal policy is learnt solel...

1 Luofeng Liao, et al. ∙

research

∙ 02/19/2021

Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques

We study the optimization aspects of personalized Federated Learning (FL...

0 Filip Hanzely, et al. ∙

research

∙ 02/10/2021

An Adaptive Stochastic Sequential Quadratic Programming with Differentiable Exact Augmented Lagrangians

We consider the problem of solving nonlinear optimization programs with ...

0 Sen Na, et al. ∙

research

∙ 12/30/2020

Provably Training Neural Network Classifiers under Fairness Constraints

Training a classifier under fairness constraints has gotten increasing a...

0 You-Lin Chen, et al. ∙

research

∙ 11/11/2020

A Nonconvex Framework for Structured Dynamic Covariance Recovery

We propose a flexible yet interpretable model for high-dimensional data ...

0 Katherine Tsai, et al. ∙

research

∙ 07/15/2020

Statistical Inference for Networks of High-Dimensional Point Processes

Fueled in part by recent applications in neuroscience, the multivariate ...

13 Xu Wang, et al. ∙

research

∙ 07/02/2020

Provably Efficient Neural Estimation of Structural Equation Model: An Adversarial Approach

Structural equation models (SEMs) are widely used in sciences, ranging f...

13 Luofeng Liao, et al. ∙

research

∙ 03/11/2020

FuDGE: Functional Differential Graph Estimation with fully and discretely observed curves

We consider the problem of estimating the difference between two functio...

14 Boxin Zhao, et al. ∙

research

∙ 03/02/2020

Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees

Graph representation learning is a ubiquitous task in machine learning w...

13 Sen Na, et al. ∙

research

∙ 02/18/2020

Post-selection inference on high-dimensional varying-coefficient quantile regression model

Quantile regression has been successfully used to study heterogeneous an...

0 Ran Dai, et al. ∙

research

∙ 02/15/2020

Posterior Ratio Estimation for Latent Variables

Density Ratio Estimation has attracted attention from machine learning c...

6 Yulong Zhang, et al. ∙

research

∙ 12/14/2019

Natural Actor-Critic Converges Globally for Hierarchical Linear Quadratic Regulator

Multi-agent reinforcement learning has been successfully applied to a nu...

7 Yuwei Luo, et al. ∙

research

∙ 11/17/2019

Constrained High Dimensional Statistical Inference

In typical high dimensional statistical inference problems, confidence i...

0 Ming Yu, et al. ∙

research

∙ 10/26/2019

Convergent Policy Optimization for Safe Reinforcement Learning

We study the safe reinforcement learning problem with nonlinear function...

29 Ming Yu, et al. ∙

research

∙ 10/22/2019

Direct Estimation of Differential Functional Graphical Models

We consider the problem of estimating the difference between two functio...

15 Boxin Zhao, et al. ∙

research

∙ 09/12/2019

Estimating Differential Latent Variable Graphical Models with Applications to Brain Connectivity

Differential graphical models are designed to represent the difference b...

0 Sen Na, et al. ∙

research

∙ 06/12/2019

Tensor Canonical Correlation Analysis

In many applications, such as classification of images or videos, it is ...

0 You-Lin Chen, et al. ∙

research

∙ 06/08/2019

Partially Linear Additive Gaussian Graphical Models

We propose a partially linear additive Gaussian graphical model (PLA-GGM...

2 Sinong Geng, et al. ∙

research

∙ 05/15/2019

Simultaneous Inference for Pairwise Graphical Models with Generalized Score Matching

Probabilistic graphical models provide a flexible yet parsimonious frame...

0 Ming Yu, et al. ∙

research

∙ 05/01/2019

Two-sample inference for high-dimensional Markov networks

Markov networks are frequently used in sciences to represent conditional...

0 Byol Kim, et al. ∙

research

∙ 11/27/2018

High-dimensional Index Volatility Models via Stein's Identity

In this paper, we consider estimating the parametric components of index...

0 Sen Na, et al. ∙

research

∙ 10/25/2018

Provable Gaussian Embedding with One Observation

The success of machine learning methods heavily relies on having an appr...

0 Ming Yu, et al. ∙

research

∙ 10/16/2018

Joint Nonparametric Precision Matrix Estimation with Confounding

We consider the problem of precision matrix estimation where, due to ext...

0 Sinong Geng, et al. ∙

research

∙ 10/16/2018

High-dimensional Varying Index Coefficient Models via Stein's Identity

We study the parameter estimation problem for a single-index varying coe...

0 Sen Na, et al. ∙

research

∙ 06/14/2018

Learning Influence-Receptivity Network Structure with Guarantee

Traditional works on community detection from observations of informatio...

0 Ming Yu, et al. ∙

research

∙ 02/20/2018

Recovery of simultaneous low rank and two-way sparse coefficient matrices, a nonconvex approach

We study the problem of recovery of matrices that are simultaneously low...

0 Ming Yu, et al. ∙

research

∙ 02/11/2018

Distributed Stochastic Multi-Task Learning with Graph Regularization

We propose methods for distributed graph-based multi-task learning that ...

0 Weiran Wang, et al. ∙

research

∙ 09/06/2017

An Influence-Receptivity Model for Topic based Information Cascades

We consider the problem of estimating the latent structure of a social n...

0 Ming Yu, et al. ∙

research

∙ 02/20/2017

Uniform Inference for High-dimensional Quantile Regression: Linear Functionals and Regression Rank Scores

Hypothesis tests in models whose dimension far exceeds the sample size c...

0 Jelena Bradic, et al. ∙

research

∙ 10/10/2016

Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and High-dimensional Data

Sketching techniques have become popular for scaling up machine learning...

0 Jialei Wang, et al. ∙

research

∙ 05/25/2016

Efficient Distributed Learning with Sparsity

We propose a novel, efficient approach for distributed sparse learning i...

0 Jialei Wang, et al. ∙

research

∙ 03/07/2016

Distributed Multi-Task Learning with Shared Representation

We study the problem of distributed multi-task learning with shared repr...

0 Jialei Wang, et al. ∙

research

∙ 12/28/2015

Post-Regularization Inference for Dynamic Nonparanormal Graphical Models

We propose a novel class of dynamic nonparanormal graphical models, whic...

0 Junwei Lu, et al. ∙

Mladen Kolar

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