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07/10/2023
Invex Programs: First Order Algorithms and Their Convergence
Invex programs are a special kind of non-convex problems which attain gl...
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06/22/2023
Outlier-robust Estimation of a Sparse Linear Model Using Invexity
In this paper, we study problem of estimating a sparse regression vector...
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06/06/2023
Partial Inference in Structured Prediction
In this paper, we examine the problem of partial inference in the contex...
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06/04/2023
Matrix Completion from General Deterministic Sampling Patterns
Most of the existing works on provable guarantees for low-rank matrix co...
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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 ...
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02/07/2023
Exact Inference in High-order Structured Prediction
In this paper, we study the problem of inference in high-order structure...
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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 ...
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01/31/2023
Learning Against Distributional Uncertainty: On the Trade-off Between Robustness and Specificity
Trustworthy machine learning aims at combating distributional uncertaint...
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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...
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12/20/2022
Distributional Robustness Bounds Generalization Errors
Bayesian methods, distributionally robust optimization methods, and regu...
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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 ...
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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...
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08/18/2022
Meta Sparse Principal Component Analysis
We study the meta-learning for support (i.e. the set of non-zero entries...
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06/10/2022
Provable Guarantees for Sparsity Recovery with Deterministic Missing Data Patterns
We study the problem of consistently recovering the sparsity pattern of ...
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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...
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05/30/2022
Federated X-Armed Bandit
This work establishes the first framework of federated 𝒳-armed bandit, w...
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05/30/2022
Support Recovery in Sparse PCA with Incomplete Data
We study a practical algorithm for sparse principal component analysis (...
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05/27/2022
Dual Convexified Convolutional Neural Networks
We propose the framework of dual convexified convolutional neural networ...
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06/14/2021
Federated Myopic Community Detection with One-shot Communication
In this paper, we study the problem of recovering the community structur...
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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...
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02/19/2021
Information-Theoretic Bounds for Integral Estimation
In this paper, we consider a zero-order stochastic oracle model of estim...
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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...
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02/18/2021
A Simple Unified Framework for High Dimensional Bandit Problems
Stochastic high dimensional bandit problems with low dimensional structu...
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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...
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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...
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02/16/2021
Inverse Reinforcement Learning in the Continuous Setting with Formal Guarantees
Inverse Reinforcement Learning (IRL) is the problem of finding a reward ...
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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...
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01/25/2021
Randomized Deep Structured Prediction for Discourse-Level Processing
Expressive text encoders such as RNNs and Transformer Networks have been...
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12/30/2020
PrivSyn: Differentially Private Data Synthesis
In differential privacy (DP), a challenging problem is to generate synth...
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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...
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07/01/2020
Fundamental Limits of Adversarial Learning
Robustness of machine learning methods is essential for modern practical...
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07/01/2020
Fairness constraints can help exact inference in structured prediction
Many inference problems in structured prediction can be modeled as maxim...
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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...
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06/22/2020
Exact Support Recovery in Federated Regression with One-shot Communication
Federated learning provides a framework to address the challenges of dis...
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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 ...
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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...
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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...
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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 ...
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02/25/2020
Novel Change of Measure Inequalities and PAC-Bayesian Bounds
PAC-Bayesian theory has received a growing attention in the machine lear...
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02/22/2020
The Sample Complexity of Meta Sparse Regression
This paper addresses the meta-learning problem in sparse linear regressi...
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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...
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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...
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06/28/2019
Direct Estimation of Difference Between Structural Equation Models in High Dimensions
Discovering cause-effect relationships between variables from observatio...
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06/02/2019
Exact inference in structured prediction
Structured prediction can be thought of as a simultaneous prediction of ...
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06/02/2019
Minimax bounds for structured prediction
Structured prediction can be considered as a generalization of many stan...
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06/02/2019
On the Correctness and Sample Complexity of Inverse Reinforcement Learning
Inverse reinforcement learning (IRL) is the problem of finding a reward ...
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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 ...
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01/25/2019
On the Statistical Efficiency of Optimal Kernel Sum Classifiers
We propose a novel combination of optimization tools with learning theor...
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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...
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09/17/2018
