b'Jean Honorio'

research

∙ 06/06/2023

Partial Inference in Structured Prediction

In this paper, we examine the problem of partial inference in the contex...

0 Chuyang Ke, et al. ∙

research

∙ 06/04/2023

Matrix Completion from General Deterministic Sampling Patterns

Most of the existing works on provable guarantees for low-rank matrix co...

0 Hanbyul Lee, et al. ∙

research

∙ 03/07/2023

PyXAB – A Python Library for 𝒳-Armed Bandit and Online Blackbox Optimization Algorithms

We introduce a Python open-source library for 𝒳-armed bandit and online ...

0 Wenjie Li, et al. ∙

research

∙ 02/07/2023

Exact Inference in High-order Structured Prediction

In this paper, we study the problem of inference in high-order structure...

0 Chuyang Ke, et al. ∙

research

∙ 02/03/2023

Support Recovery in Sparse PCA with Non-Random Missing Data

We analyze a practical algorithm for sparse PCA on incomplete and noisy ...

0 Hanbyul Lee, et al. ∙

research

∙ 01/31/2023

Learning Against Distributional Uncertainty: On the Trade-off Between Robustness and Specificity

Trustworthy machine learning aims at combating distributional uncertaint...

0 Shixiong Wang, et al. ∙

research

∙ 12/21/2022

A Theoretical Study of The Effects of Adversarial Attacks on Sparse Regression

This paper analyzes ℓ_1 regularized linear regression under the challeng...

0 Deepak Maurya, et al. ∙

research

∙ 12/20/2022

Distributional Robustness Bounds Generalization Errors

Bayesian methods, distributionally robust optimization methods, and regu...

0 Shixiong Wang, et al. ∙

research

∙ 08/19/2022

Meta Learning for High-dimensional Ising Model Selection Using ℓ_1-regularized Logistic Regression

In this paper, we consider the meta learning problem for estimating the ...

0 Huiming Xie, et al. ∙

research

∙ 08/19/2022

A Novel Plug-and-Play Approach for Adversarially Robust Generalization

In this work, we propose a robust framework that employs adversarially r...

0 Deepak Maurya, et al. ∙

research

∙ 08/18/2022

Meta Sparse Principal Component Analysis

We study the meta-learning for support (i.e. the set of non-zero entries...

0 Imon Banerjee, et al. ∙

research

∙ 06/10/2022

Provable Guarantees for Sparsity Recovery with Deterministic Missing Data Patterns

We study the problem of consistently recovering the sparsity pattern of ...

0 Chuyang Ke, et al. ∙

research

∙ 06/02/2022

Sparse Mixed Linear Regression with Guarantees: Taming an Intractable Problem with Invex Relaxation

In this paper, we study the problem of sparse mixed linear regression on...

0 Adarsh Barik, et al. ∙

research

∙ 05/30/2022

Federated X-Armed Bandit

This work establishes the first framework of federated 𝒳-armed bandit, w...

0 Wenjie Li, et al. ∙

research

∙ 05/30/2022

Support Recovery in Sparse PCA with Incomplete Data

We study a practical algorithm for sparse principal component analysis (...

0 Hanbyul Lee, et al. ∙

research

∙ 05/27/2022

Dual Convexified Convolutional Neural Networks

We propose the framework of dual convexified convolutional neural networ...

0 Site Bai, et al. ∙

research

∙ 06/14/2021

Federated Myopic Community Detection with One-shot Communication

In this paper, we study the problem of recovering the community structur...

0 Chuyang Ke, et al. ∙

research

∙ 03/07/2021

A Lower Bound for the Sample Complexity of Inverse Reinforcement Learning

Inverse reinforcement learning (IRL) is the task of finding a reward fun...

0 Abi Komanduru, et al. ∙

research

∙ 02/19/2021

Information-Theoretic Bounds for Integral Estimation

In this paper, we consider a zero-order stochastic oracle model of estim...

0 Donald Q. Adams, et al. ∙

research

∙ 02/19/2021

Fair Sparse Regression with Clustering: An Invex Relaxation for a Combinatorial Problem

In this paper, we study the problem of fair sparse regression on a biase...

0 Adarsh Barik, et al. ∙

research

∙ 02/18/2021

A Simple Unified Framework for High Dimensional Bandit Problems

Stochastic high dimensional bandit problems with low dimensional structu...

0 Wenjie Li, et al. ∙

research

∙ 02/17/2021

On the Fundamental Limits of Exact Inference in Structured Prediction

Inference is a main task in structured prediction and it is naturally mo...

0 Hanbyul Lee, et al. ∙

research

∙ 02/16/2021

A Thorough View of Exact Inference in Graphs from the Degree-4 Sum-of-Squares Hierarchy

Performing inference in graphs is a common task within several machine l...

0 Kevin Bello, et al. ∙

research

∙ 02/16/2021

Inverse Reinforcement Learning in the Continuous Setting with Formal Guarantees

Inverse Reinforcement Learning (IRL) is the problem of finding a reward ...

0 Gregory Dexter, et al. ∙

research

∙ 01/29/2021

Information Theoretic Limits of Exact Recovery in Sub-hypergraph Models for Community Detection

In this paper, we study the information theoretic bounds for exact recov...

0 Jiajun Liang, et al. ∙

research

∙ 01/25/2021

Randomized Deep Structured Prediction for Discourse-Level Processing

Expressive text encoders such as RNNs and Transformer Networks have been...

0 Manuel Widmoser, et al. ∙

research

∙ 12/30/2020

PrivSyn: Differentially Private Data Synthesis

In differential privacy (DP), a challenging problem is to generate synth...

0 Zhikun Zhang, et al. ∙

research

∙ 07/01/2020

Information Theoretic Sample Complexity Lower Bound for Feed-Forward Fully-Connected Deep Networks

In this paper, we study the sample complexity lower bound of a d-layer f...

0 Xiaochen Yang, et al. ∙

research

∙ 07/01/2020

Fundamental Limits of Adversarial Learning

Robustness of machine learning methods is essential for modern practical...

0 Kevin Bello, et al. ∙

research

∙ 07/01/2020

Fairness constraints can help exact inference in structured prediction

Many inference problems in structured prediction can be modeled as maxim...

0 Kevin Bello, et al. ∙

research

∙ 06/22/2020

Support Union Recovery in Meta Learning of Gaussian Graphical Models

In this paper we study Meta learning of Gaussian graphical models. In ou...

0 Qian Zhang, et al. ∙

research

∙ 06/22/2020

Exact Support Recovery in Federated Regression with One-shot Communication

Federated learning provides a framework to address the challenges of dis...

0 Adarsh Barik, et al. ∙

research

∙ 06/20/2020

Exact Partitioning of High-order Planted Models with a Tensor Nuclear Norm Constraint

We study the problem of efficient exact partitioning of the hypergraphs ...

0 Chuyang Ke, et al. ∙

research

∙ 04/01/2020

Provable Sample Complexity Guarantees for Learning of Continuous-Action Graphical Games with Nonparametric Utilities

In this paper, we study the problem of learning the exact structure of c...

0 Adarsh Barik, et al. ∙

research

∙ 03/31/2020

Information-Theoretic Lower Bounds for Zero-Order Stochastic Gradient Estimation

In this paper we analyze the necessary number of samples to estimate the...

0 Abdulrahman Alabdulkareem, et al. ∙

research

∙ 02/28/2020

First Order Methods take Exponential Time to Converge to Global Minimizers of Non-Convex Functions

Machine learning algorithms typically perform optimization over a class ...

17 Krishna Reddy Kesari, et al. ∙

research

∙ 02/25/2020

Novel Change of Measure Inequalities and PAC-Bayesian Bounds

PAC-Bayesian theory has received a growing attention in the machine lear...

0 Yuki Ohnishi, et al. ∙

research

∙ 02/22/2020

The Sample Complexity of Meta Sparse Regression

This paper addresses the meta-learning problem in sparse linear regressi...

0 Zhanyu Wang, et al. ∙

research

∙ 11/08/2019

Provable Computational and Statistical Guarantees for Efficient Learning of Continuous-Action Graphical Games

In this paper, we study the problem of learning the set of pure strategy...

0 Adarsh Barik, et al. ∙

research

∙ 11/06/2019

Exact Partitioning of High-order Models with a Novel Convex Tensor Cone Relaxation

In this paper we propose the first correct poly-time algorithm for exact...

0 Chuyang Ke, et al. ∙

research

∙ 06/28/2019

Direct Estimation of Difference Between Structural Equation Models in High Dimensions

Discovering cause-effect relationships between variables from observatio...

0 Asish Ghoshal, et al. ∙

research

∙ 06/02/2019

Exact inference in structured prediction

Structured prediction can be thought of as a simultaneous prediction of ...

0 Kevin Bello, et al. ∙

research

∙ 06/02/2019

Minimax bounds for structured prediction

Structured prediction can be considered as a generalization of many stan...

0 Kevin Bello, et al. ∙

research

∙ 06/02/2019

On the Correctness and Sample Complexity of Inverse Reinforcement Learning

Inverse reinforcement learning (IRL) is the problem of finding a reward ...

0 Abi Komanduru, et al. ∙

research

∙ 05/29/2019

Learning Bayesian Networks with Low Rank Conditional Probability Tables

In this paper, we provide a method to learn the directed structure of a ...

0 Adarsh Barik, et al. ∙

research

∙ 01/25/2019

On the Statistical Efficiency of Optimal Kernel Sum Classifiers

We propose a novel combination of optimization tools with learning theor...

0 Raphael Arkady Meyer, et al. ∙

research

∙ 11/16/2018

Information Theoretic Limits for Standard and One-Bit Compressed Sensing with Graph-Structured Sparsity

In this paper, we analyze the information theoretic lower bound on the n...

0 Adarsh Barik, et al. ∙

research

∙ 09/17/2018

Statistically and Computationally Efficient Variance Estimator for Kernel Ridge Regression

In this paper, we propose a random projection approach to estimate varia...

0 Meimei Liu, et al. ∙

research

∙ 05/23/2018

Learning latent variable structured prediction models with Gaussian perturbations

The standard margin-based structured prediction commonly uses a maximum ...

0 Kevin Bello, et al. ∙

research

∙ 05/21/2018

Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time

MAP perturbation models have emerged as a powerful framework for inferen...

0 Asish Ghoshal, et al. ∙

Jean Honorio

Featured Co-authors