Jean Honorio

Information-theoretic lower bounds on learning the structure of Bayesian networks.

2016 · arXiv (Cornell University)

In this paper, we study the information theoretic limits of learning the structure of Bayesian networks from data. We show that for Bayesian networks on continuous as well as discrete random variables, there exists a …

Regularized Loss Minimizers with Local Data Obfuscation.

2018 · arXiv (Cornell University)

We introduce a new concept, data irrecoverability, and show that the well-studied concept of data privacy is sufficient but not necessary for data irrecoverability. We show that there are several regularized loss minimization problems that …

On the Fundamental Limits of Exact Inference in Structured Prediction

2022 · 2022 IEEE International Symposium on Information Theory (ISIT)

Inference in structured prediction is naturally modeled with a graph, where the goal is to recover the unknown true label for each node given noisy observations corresponding to nodes and edges. The focus of this …

The Sample Complexity of Meta Sparse Regression

2021 · International Conference on Artificial Intelligence and Statistics

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 is to transfer knowledge from the prior …