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...
share
research
∙
05/28/2023
Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching
We consider solving equality-constrained nonlinear, nonconvex optimizati...
research
∙
05/23/2023
Confidence Sets for Causal Orderings
Causal discovery procedures aim to deduce causal relationships among var...
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...
research
∙
10/31/2022
Latent Multimodal Functional Graphical Model Estimation
Joint multimodal functional data acquisition, where functional data from...
research
∙
08/29/2022
On the Lasso for Graphical Continuous Lyapunov Models
Graphical continuous Lyapunov models offer a new perspective on modeling...
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...
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...
research
∙
04/28/2022
Personalized Federated Learning with Multiple Known Clusters
We consider the problem of personalized federated learning when there ar...
research
∙
01/31/2022
L-SVRG and L-Katyusha with Adaptive Sampling
Stochastic gradient-based optimization methods, such as L-SVRG and its a...
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...
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...
research
∙
10/19/2021
Joint Gaussian Graphical Model Estimation: A Survey
Graphs from complex systems often share a partial underlying structure a...
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...
research
∙
06/18/2021
Local AdaGrad-Type Algorithm for Stochastic Convex-Concave Minimax Problems
Large scale convex-concave minimax problems arise in numerous applicatio...
research
∙
06/14/2021
Robust Inference for High-Dimensional Linear Models via Residual Randomization
We propose a residual randomization procedure designed for robust Lasso-...
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...
research
∙
02/19/2021
Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning
In offline reinforcement learning (RL) an optimal policy is learnt solel...
research
∙
02/19/2021
Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques
We study the optimization aspects of personalized Federated Learning (FL...
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 ...
research
∙
12/30/2020
Provably Training Neural Network Classifiers under Fairness Constraints
Training a classifier under fairness constraints has gotten increasing a...
research
∙
11/11/2020
A Nonconvex Framework for Structured Dynamic Covariance Recovery
We propose a flexible yet interpretable model for high-dimensional data ...
research
∙
07/15/2020
Statistical Inference for Networks of High-Dimensional Point Processes
Fueled in part by recent applications in neuroscience, the multivariate ...
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...
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...
research
∙
03/02/2020
Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees
Graph representation learning is a ubiquitous task in machine learning w...
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...
research
∙
02/15/2020
Posterior Ratio Estimation for Latent Variables
Density Ratio Estimation has attracted attention from machine learning c...
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...
research
∙
11/17/2019
Constrained High Dimensional Statistical Inference
In typical high dimensional statistical inference problems, confidence i...
research
∙
10/26/2019
Convergent Policy Optimization for Safe Reinforcement Learning
We study the safe reinforcement learning problem with nonlinear function...
research
∙
10/22/2019
Direct Estimation of Differential Functional Graphical Models
We consider the problem of estimating the difference between two functio...
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...
research
∙
06/12/2019
Tensor Canonical Correlation Analysis
In many applications, such as classification of images or videos, it is ...
research
∙
06/08/2019
Partially Linear Additive Gaussian Graphical Models
We propose a partially linear additive Gaussian graphical model (PLA-GGM...
research
∙
05/15/2019
Simultaneous Inference for Pairwise Graphical Models with Generalized Score Matching
Probabilistic graphical models provide a flexible yet parsimonious frame...
research
∙
05/01/2019
Two-sample inference for high-dimensional Markov networks
Markov networks are frequently used in sciences to represent conditional...
research
∙
11/27/2018
High-dimensional Index Volatility Models via Stein's Identity
In this paper, we consider estimating the parametric components of index...
research
∙
10/25/2018
Provable Gaussian Embedding with One Observation
The success of machine learning methods heavily relies on having an appr...
research
∙
10/16/2018
Joint Nonparametric Precision Matrix Estimation with Confounding
We consider the problem of precision matrix estimation where, due to ext...
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...
research
∙
06/14/2018
Learning Influence-Receptivity Network Structure with Guarantee
Traditional works on community detection from observations of informatio...
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...
research
∙
02/11/2018
Distributed Stochastic Multi-Task Learning with Graph Regularization
We propose methods for distributed graph-based multi-task learning that ...
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...
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...
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...
research
∙
05/25/2016
Efficient Distributed Learning with Sparsity
We propose a novel, efficient approach for distributed sparse learning i...
research
∙
03/07/2016
Distributed Multi-Task Learning with Shared Representation
We study the problem of distributed multi-task learning with shared repr...
research
∙
12/28/2015