7 citations · 11 across the 5 of their papers we have counts for
23 papers
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…
Information Theoretic Limits of Exact Recovery in Sub-hypergraph Models for Community Detection
Jiajun Liang, Chuyang Ke, Jean Honorio
In this paper, we study the information theoretic bounds for exact recovery in sub-hypergraph models for community detection. We define a general model called the uniform sub-h…
Randomized Deep Structured Prediction for Discourse-Level Processing
Manuel Widmoser, Maria Leonor Pacheco, Jean Honorio +1
Expressive text encoders such as RNNs and Transformer Networks have been at the center of NLP models in recent work. Most of the effort has focused on sentence-level tasks, capturi…
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…