25 citations · 143 across the 71 of their papers we have counts for
75 papers
Optimal bounds on a tree inference algorithm
Jack Gardiner, Lachlan L. H. Andrew, Junhao Gan +2
This paper tightens the best known analysis of Hein's 1989 algorithm to infer the topology of a weighted tree based on the lengths of paths between its leaves. It shows that the nu…
Invex Programs: First Order Algorithms and Their Convergence
Adarsh Barik, Suvrit Sra, Jean Honorio
Invex programs are a special kind of non-convex problems which attain global minima at every stationary point. While classical first-order gradient descent methods can solve them,…
Outlier-robust Estimation of a Sparse Linear Model Using Invexity
Adarsh Barik, Jean Honorio
In this paper, we study problem of estimating a sparse regression vector with correct support in the presence of outlier samples. The inconsistency of lasso-type methods is well kn…
Matrix Completion from General Deterministic Sampling Patterns
Hanbyul Lee, Rahul Mazumder, Qifan Song +1
Most of the existing works on provable guarantees for low-rank matrix completion algorithms rely on some unrealistic assumptions such that matrix entries are sampled randomly or th…
PyXAB -- A Python Library for -Armed Bandit and Online Blackbox Optimization Algorithms
Wenjie Li, Haoze Li, Jean Honorio +1
We introduce a Python open-source library for -armed bandit and online blackbox optimization named PyXAB. PyXAB contains the implementations for more than 10 $\mathcal…
Exact Inference in High-order Structured Prediction
Chuyang Ke, Jean Honorio
In this paper, we study the problem of inference in high-order structured prediction tasks. In the context of Markov random fields, the goal of a high-order inference task is to ma…
Support Recovery in Sparse PCA with Non-Random Missing Data
Hanbyul Lee, Qifan Song, Jean Honorio
We analyze a practical algorithm for sparse PCA on incomplete and noisy data under a general non-random sampling scheme. The algorithm is based on a semidefinite relaxation of the…
Learning Against Distributional Uncertainty: On the Trade-off Between Robustness and Specificity
Shixiong Wang, Haowei Wang, Xinke Li +1
Trustworthy machine learning aims at combating distributional uncertainties in training data distributions compared to population distributions. Typical treatment frameworks includ…
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…
Federated Myopic Community Detection with One-shot Communication
Chuyang Ke, Jean Honorio
In this paper, we study the problem of recovering the community structure of a network under federated myopic learning. Under this paradigm, we have several clients, each of them h…
A Lower Bound for the Sample Complexity of Inverse Reinforcement Learning
Abi Komanduru, Jean Honorio
Inverse reinforcement learning (IRL) is the task of finding a reward function that generates a desired optimal policy for a given Markov Decision Process (MDP). This paper develops…
Information-Theoretic Bounds for Integral Estimation
Donald Q. Adams, Adarsh Barik, Jean Honorio
In this paper, we consider a zero-order stochastic oracle model of estimating definite integrals. In this model, integral estimation methods may query an oracle function for a fixe…
On the Fundamental Limits of Exact Inference in Structured Prediction
Hanbyul Lee, Kevin Bello, Jean Honorio
Inference is a main task in structured prediction and it is naturally modeled with a graph. In the context of Markov random fields, noisy observations corresponding to nodes and ed…
Fair Sparse Regression with Clustering: An Invex Relaxation for a Combinatorial Problem
Adarsh Barik, Jean Honorio
In this paper, we study the problem of fair sparse regression on a biased dataset where bias depends upon a hidden binary attribute. The presence of a hidden attribute adds an extr…
A Simple Unified Framework for High Dimensional Bandit Problems
Wenjie Li, Adarsh Barik, Jean Honorio
Stochastic high dimensional bandit problems with low dimensional structures are useful in different applications such as online advertising and drug discovery. In this work, we pro…