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07/30/2023
On Neural Network approximation of ideal adversarial attack and convergence of adversarial training
Adversarial attacks are usually expressed in terms of a gradient-based o...
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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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02/03/2023
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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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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02/14/2022
Unlabeled Data Help: Minimax Analysis and Adversarial Robustness
The recent proposed self-supervised learning (SSL) approaches successful...
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08/09/2021
Data-guided Treatment Recommendation with Feature Scores
In this paper, we consider the use of large-scale genomics data for trea...
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05/11/2021
A Langevinized Ensemble Kalman Filter for Large-Scale Static and Dynamic Learning
The Ensemble Kalman Filter (EnKF) has achieved great successes in data a...
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02/25/2021
Consistent Sparse Deep Learning: Theory and Computation
Deep learning has been the engine powering many successes of data scienc...
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11/15/2020
Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee
Sparse deep learning aims to address the challenge of huge storage consu...
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10/24/2020
Nearly Optimal Variational Inference for High Dimensional Regression with Shrinkage Priors
We propose a variational Bayesian (VB) procedure for high-dimensional li...
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09/20/2020
Stochastic Gradient Langevin Dynamics Algorithms with Adaptive Drifts
Bayesian deep learning offers a principled way to address many issues co...
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08/15/2020
On the Generalization Properties of Adversarial Training
Modern machine learning and deep learning models are shown to be vulnera...
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04/11/2020
Bayesian Shrinkage towards Sharp Minimaxity
Shrinkage prior are becoming more and more popular in Bayesian modeling ...
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02/13/2020
Predictive Power of Nearest Neighbors Algorithm under Random Perturbation
We consider a data corruption scenario in the classical k Nearest Neighb...
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02/07/2020
Extended Stochastic Gradient MCMC for Large-Scale Bayesian Variable Selection
Stochastic gradient Markov chain Monte Carlo (MCMC) algorithms have rece...
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10/10/2019
Rate Optimal Variational Bayesian Inference for Sparse DNN
Sparse deep neural network (DNN) has drawn much attention in recent stud...
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09/25/2019
Benefit of Interpolation in Nearest Neighbor Algorithms
The over-parameterized models attract much attention in the era of data ...
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12/27/2018
Bayesian Fusion Estimation via t-Shrinkage
Shrinkage prior has gained great successes in many data analysis, howeve...
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12/25/2018
Optimal False Discovery Control of Minimax Estimator
In the analysis of high dimensional regression models, there are two imp...
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10/05/2018
Statistical Optimality of Interpolated Nearest Neighbor Algorithms
In the era of deep learning, understanding over-fitting phenomenon becom...
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12/24/2017
