25 citations · 143 across the 71 of their papers we have counts for
12 papers · 1 filter
A Novel Tool for the Accurate and Affordable Early Diagnosis of Pancreatic Cancer via Machine Learning and Bioinformatics
Siya Goel, Clark Gedney, Jean Honorio
Pancreatic cancer (PC) is the fourth leading cause of cancer death in the United States due to its five-year survival rate of 10%. Late diagnosis, affiliated with the asymptomatic…
Information Theoretic Lower Bounds for Feed-Forward Fully-Connected Deep Networks
Xiaochen Yang, Jean Honorio
In this paper, we study the sample complexity lower bounds for the exact recovery of parameters and for a positive excess risk of a feed-forward, fully-connected neural network for…
A Le Cam Type Bound for Adversarial Learning and Applications
Qiuling Xu, Kevin Bello, Jean Honorio
Robustness of machine learning methods is essential for modern practical applications. Given the arms race between attack and defense methods, one may be curious regarding the fund…
Fairness constraints can help exact inference in structured prediction
Kevin Bello, Jean Honorio
Many inference problems in structured prediction can be modeled as maximizing a score function on a space of labels, where graphs are a natural representation to decompose the tota…
Exact Support Recovery in Federated Regression with One-shot Communication
Adarsh Barik, Jean Honorio
Federated learning provides a framework to address the challenges of distributed computing, data ownership and privacy over a large number of distributed clients with low computati…
Exact Partitioning of High-order Planted Models with a Tensor Nuclear Norm Constraint
Chuyang Ke, Jean Honorio
We study the problem of efficient exact partitioning of the hypergraphs generated by high-order planted models. A high-order planted model assumes some underlying cluster structure…
Meta Learning for Support Recovery in High-dimensional Precision Matrix Estimation
Qian Zhang, Yilin Zheng, Jean Honorio
In this paper, we study meta learning for support (i.e., the set of non-zero entries) recovery in high-dimensional precision matrix estimation where we reduce the sufficient sample…
Provable Sample Complexity Guarantees for Learning of Continuous-Action Graphical Games with Nonparametric Utilities
Adarsh Barik, Jean Honorio
In this paper, we study the problem of learning the exact structure of continuous-action games with non-parametric utility functions. We propose an regularized method whic…
Information-Theoretic Lower Bounds for Zero-Order Stochastic Gradient Estimation
Abdulrahman Alabdulkareem, Jean Honorio
In this paper we analyze the necessary number of samples to estimate the gradient of any multidimensional smooth (possibly non-convex) function in a zero-order stochastic oracle mo…
First Order Methods take Exponential Time to Converge to Global Minimizers of Non-Convex Functions
Krishna Reddy Kesari, Jean Honorio
Machine learning algorithms typically perform optimization over a class of non-convex functions. In this work, we provide bounds on the fundamental hardness of identifying the glob…
Novel Change of Measure Inequalities with Applications to PAC-Bayesian Bounds and Monte Carlo Estimation
Yuki Ohnishi, Jean Honorio
We introduce several novel change of measure inequalities for two families of divergences: -divergences and -divergences. We show how the variational representation for -d…
The Sample Complexity of Meta Sparse Regression
Zhanyu Wang, Jean Honorio
This paper addresses the meta-learning problem in sparse linear regression with infinite tasks. We assume that the learner can access several similar tasks. The goal of the learner…