25 citations · 143 across the 71 of their papers we have counts for
10 papers · 1 filter
A Theoretical Study of The Effects of Adversarial Attacks on Sparse Regression
Deepak Maurya, Jean Honorio
This paper analyzes regularized linear regression under the challenging scenario of having only adversarially corrupted data for training. We use the primal-dual witness p…
Distributional Robustness Bounds Generalization Errors
Shixiong Wang, Haowei Wang
Bayesian methods, distributionally robust optimization methods, and regularization methods are three pillars of trustworthy machine learning combating distributional uncertainty, e…
Meta Learning for High-dimensional Ising Model Selection Using -regularized Logistic Regression
Huiming Xie, Jean Honorio
In this paper, we consider the meta learning problem for estimating the graphs associated with high-dimensional Ising models, using the method of -regularized logistic regr…
Meta Sparse Principal Component Analysis
Imon Banerjee, Jean Honorio
We study the meta-learning for support (i.e. the set of non-zero entries) recovery in high-dimensional Principal Component Analysis. We reduce the sufficient sample complexity in a…
A Novel Plug-and-Play Approach for Adversarially Robust Generalization
Deepak Maurya, Adarsh Barik, Jean Honorio
In this work, we propose a robust framework that employs adversarially robust training to safeguard the ML models against perturbed testing data. Our contributions can be seen from…
Provable Guarantees for Sparsity Recovery with Deterministic Missing Data Patterns
Chuyang Ke, Jean Honorio
We study the problem of consistently recovering the sparsity pattern of a regression parameter vector from correlated observations governed by deterministic missing data patterns u…
Sparse Mixed Linear Regression with Guarantees: Taming an Intractable Problem with Invex Relaxation
Adarsh Barik, Jean Honorio
In this paper, we study the problem of sparse mixed linear regression on an unlabeled dataset that is generated from linear measurements from two different regression parameter vec…
Support Recovery in Sparse PCA with Incomplete Data
Hanbyul Lee, Qifan Song, Jean Honorio
We study a practical algorithm for sparse principal component analysis (PCA) of incomplete and noisy data. Our algorithm is based on the semidefinite program (SDP) relaxation of th…
Federated X-Armed Bandit
Wenjie Li, Qifan Song, Jean Honorio +1
This work establishes the first framework of federated -armed bandit, where different clients face heterogeneous local objective functions defined on the same domain a…
Dual Convexified Convolutional Neural Networks
Site Bai, Chuyang Ke, Jean Honorio
We propose the framework of dual convexified convolutional neural networks (DCCNNs). In this framework, we first introduce a primal learning problem motivated by convexified convol…