Jean Honorio

Orcid: 0000-0002-6448-0598

According to our database1, Jean Honorio authored at least 106 papers between 2008 and 2024.

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Bibliography

2024
Provable Guarantees for Sparsity Recovery with Deterministic Missing Data Patterns.
Trans. Mach. Learn. Res., 2024

Recovering Exact Support in Federated lasso without Optimization.
Trans. Mach. Learn. Res., 2024

On the Dual Problem of Convexified Convolutional Neural Networks.
Trans. Mach. Learn. Res., 2024

Personalized Federated X-armed Bandit.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Federated X-armed Bandit.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Invex Programs: First Order Algorithms and Their Convergence.
CoRR, 2023

Outlier-robust Estimation of a Sparse Linear Model Using Invexity.
CoRR, 2023

Partial Inference in Structured Prediction.
CoRR, 2023

Matrix Completion from General Deterministic Sampling Patterns.
CoRR, 2023

PyXAB - A Python Library for X-Armed Bandit and Online Blackbox Optimization Algorithms.
CoRR, 2023

Support Recovery in Sparse PCA with Non-Random Missing Data.
CoRR, 2023

Learning Against Distributional Uncertainty: On the Trade-off Between Robustness and Specificity.
CoRR, 2023

Exact Inference in High-order Structured Prediction.
Proceedings of the International Conference on Machine Learning, 2023

Provable Computational and Statistical Guarantees for Efficient Learning of Continuous-Action Graphical Games.
Proceedings of the IEEE International Conference on Acoustics, 2023

MEDIC: Remove Model Backdoors via Importance Driven Cloning.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
Exact Partitioning of High-order Models with a Novel Convex Tensor Cone Relaxation.
J. Mach. Learn. Res., 2022

A Theoretical Study of The Effects of Adversarial Attacks on Sparse Regression.
CoRR, 2022

Distributional Robustness Bounds Generalization Errors.
CoRR, 2022

Meta Learning for High-dimensional Ising Model Selection Using $\ell_1$-regularized Logistic Regression.
CoRR, 2022

A Novel Plug-and-Play Approach for Adversarially Robust Generalization.
CoRR, 2022

Meta Sparse Principal Component Analysis.
CoRR, 2022

Dual Convexified Convolutional Neural Networks.
CoRR, 2022

Support Recovery in Sparse PCA with Incomplete Data.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On the Fundamental Limits of Exact Inference in Structured Prediction.
Proceedings of the IEEE International Symposium on Information Theory, 2022

A Simple Unified Framework for High Dimensional Bandit Problems.
Proceedings of the International Conference on Machine Learning, 2022

Sparse Mixed Linear Regression with Guarantees: Taming an Intractable Problem with Invex Relaxation.
Proceedings of the International Conference on Machine Learning, 2022

Exact Partitioning of High-Order Planted Models with A Tensor Nuclear Norm Constraint.
Proceedings of the IEEE International Conference on Acoustics, 2022

Information Theoretic Limits For Standard and One-Bit Compressed Sensing with Graph-Structured Sparsity.
Proceedings of the IEEE International Conference on Acoustics, 2022

Provable Sample Complexity Guarantees For Learning Of Continuous-Action Graphical Games With Nonparametric Utilities.
Proceedings of the IEEE International Conference on Acoustics, 2022

Federated Myopic Community Detection with One-shot Communication.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

A View of Exact Inference in Graphs from the Degree-4 Sum-of-Squares Hierarchy.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
A Thorough View of Exact Inference in Graphs from the Degree-4 Sum-of-Squares Hierarchy.
CoRR, 2021

Inverse Reinforcement Learning in the Continuous Setting with Formal Guarantees.
CoRR, 2021

PrivSyn: Differentially Private Data Synthesis.
Proceedings of the 30th USENIX Security Symposium, 2021

Inverse Reinforcement Learning in a Continuous State Space with Formal Guarantees.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Fair Sparse Regression with Clustering: An Invex Relaxation for a Combinatorial Problem.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

A Le Cam Type Bound for Adversarial Learning and Applications.
Proceedings of the IEEE International Symposium on Information Theory, 2021

Information Theoretic Limits of Exact Recovery in Sub-hypergraph Models for Community Detection.
Proceedings of the IEEE International Symposium on Information Theory, 2021

Regularized Loss Minimizers with Local Data Perturbation: Consistency and Data Irrecoverability.
Proceedings of the IEEE International Symposium on Information Theory, 2021

First Order Methods take Exponential Time to Converge to Global Minimizers of Non-Convex Functions.
Proceedings of the IEEE International Symposium on Information Theory, 2021

Information-theoretic lower bounds for zero-order stochastic gradient estimation.
Proceedings of the IEEE International Symposium on Information Theory, 2021

Information-Theoretic Bounds for Integral Estimation.
Proceedings of the IEEE International Symposium on Information Theory, 2021

Meta Learning for Support Recovery in High-dimensional Precision Matrix Estimation.
Proceedings of the 38th International Conference on Machine Learning, 2021

A Lower Bound for the Sample Complexity of Inverse Reinforcement Learning.
Proceedings of the 38th International Conference on Machine Learning, 2021

Randomized Deep Structured Prediction for Discourse-Level Processing.
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, 2021

The Sample Complexity of Meta Sparse Regression.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Novel Change of Measure Inequalities with Applications to PAC-Bayesian Bounds and Monte Carlo Estimation.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Information Theoretic Sample Complexity Lower Bound for Feed-Forward Fully-Connected Deep Networks.
CoRR, 2020

Fundamental Limits of Adversarial Learning.
CoRR, 2020

Support Union Recovery in Meta Learning of Gaussian Graphical Models.
CoRR, 2020

Exact Support Recovery in Federated Regression with One-shot Communication.
CoRR, 2020

Novel Change of Measure Inequalities and PAC-Bayesian Bounds.
CoRR, 2020

Fairness constraints can help exact inference in structured prediction.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Provable Efficient Skeleton Learning of Encodable Discrete Bayes Nets in Poly-Time and Sample Complexity.
Proceedings of the IEEE International Symposium on Information Theory, 2020

Minimax Bounds for Structured Prediction Based on Factor Graphs.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Minimax bounds for structured prediction.
CoRR, 2019

Exact Recovery in the Latent Space Model.
CoRR, 2019

On the Statistical Efficiency of Optimal Kernel Sum Classifiers.
CoRR, 2019

On the Correctness and Sample Complexity of Inverse Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Exact inference in structured prediction.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Learning Bayesian Networks with Low Rank Conditional Probability Tables.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Cost-Aware Learning for Improved Identifiability with Multiple Experiments.
Proceedings of the IEEE International Symposium on Information Theory, 2019

Optimality Implies Kernel Sum Classifiers are Statistically Efficient.
Proceedings of the 36th International Conference on Machine Learning, 2019

Reconstructing a Bounded-Degree Directed Tree Using Path Queries.
Proceedings of the 57th Annual Allerton Conference on Communication, 2019

2018
Regularized Loss Minimizers with Local Data Obfuscation.
CoRR, 2018

Learning Binary Bayesian Networks in Polynomial Time and Sample Complexity.
CoRR, 2018

On the Sample Complexity of Learning from a Sequence of Experiments.
CoRR, 2018

Information-theoretic Limits for Community Detection in Network Models.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Computationally and statistically efficient learning of causal Bayes nets using path queries.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Learning latent variable structured prediction models with Gaussian perturbations.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time.
Proceedings of the 35th International Conference on Machine Learning, 2018

Statistically and Computationally Efficient Variance Estimator for Kernel Ridge Regression.
Proceedings of the 56th Annual Allerton Conference on Communication, 2018

On the Statistical Efficiency of Compositional Nonparametric Prediction.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

Learning Sparse Polymatrix Games in Polynomial Time and Sample Complexity.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

Learning linear structural equation models in polynomial time and sample complexity.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
Learning Sparse Potential Games in Polynomial Time and Sample Complexity.
CoRR, 2017

Learning Bayes networks using interventional path queries in polynomial time and sample complexity.
CoRR, 2017

Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Information theoretic limits for linear prediction with graph-structured sparsity.
Proceedings of the 2017 IEEE International Symposium on Information Theory, 2017

On the sample complexity of learning graphical games.
Proceedings of the 55th Annual Allerton Conference on Communication, 2017

Learning Graphical Games from Behavioral Data: Sufficient and Necessary Conditions.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

Information-theoretic limits of Bayesian network structure learning.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016
On the Sample Complexity of Learning Sparse Graphical Games.
CoRR, 2016

Information-theoretic lower bounds on learning the structure of Bayesian networks.
CoRR, 2016

Structured Prediction: From Gaussian Perturbations to Linear-Time Principled Algorithms.
Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Information-theoretic lower bounds for recovery of diffusion network structures.
Proceedings of the IEEE International Symposium on Information Theory, 2016

From behavior to sparse graphical games: Efficient recovery of equilibria.
Proceedings of the 54th Annual Allerton Conference on Communication, 2016

2015
Learning the structure and parameters of large-population graphical games from behavioral data.
J. Mach. Learn. Res., 2015

Predictive sparse modeling of fMRI data for improved classification, regression, and visualization using the k-support norm.
Comput. Medical Imaging Graph., 2015

2014
A Unified Framework for Consistency of Regularized Loss Minimizers.
Proceedings of the 31th International Conference on Machine Learning, 2014

Tight Bounds for the Expected Risk of Linear Classifiers and PAC-Bayes Finite-Sample Guarantees.
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013
Inverse Covariance Estimation for High-Dimensional Data in Linear Time and Space: Spectral Methods for Riccati and Sparse Models.
Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

fMRI Analysis with Sparse Weisfeiler-Lehman Graph Statistics.
Proceedings of the Machine Learning in Medical Imaging - 4th International Workshop, 2013

FMRI analysis of cocaine addiction using k-support sparsity.
Proceedings of the 10th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2013

Two-Sided Exponential Concentration Bounds for Bayes Error Rate and Shannon Entropy.
Proceedings of the 30th International Conference on Machine Learning, 2013

2012
Can a Single Brain Region Predict a Disorder?
IEEE Trans. Medical Imaging, 2012

Variable Selection for Gaussian Graphical Models.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Simultaneous and Group-Sparse Multi-Task Learning of Gaussian Graphical Models
CoRR, 2012

Convergence Rates of Biased Stochastic Optimization for Learning Sparse Ising Models.
Proceedings of the 29th International Conference on Machine Learning, 2012

Two-person interaction detection using body-pose features and multiple instance learning.
Proceedings of the 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2012

2011
Digital Analysis and Visualization of Swimming Motion.
Int. J. Virtual Real., 2011

Lipschitz Parametrization of Probabilistic Graphical Models.
Proceedings of the UAI 2011, 2011

2010
Simple fully automated group classification on brain FMRI.
Proceedings of the 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2010

Multi-Task Learning of Gaussian Graphical Models.
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

2009
Sparse and Locally Constant Gaussian Graphical Models.
Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

2008
Task-Specific Functional Brain Geometry from Model Maps.
Proceedings of the Medical Image Computing and Computer-Assisted Intervention, 2008


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