We stand with Ukraine

We stand with Ukraine

Jean Honorio

Orcid: 0000-0002-6448-0598

According to our database¹, Jean Honorio authored at least 107 papers between 2008 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of three.

Timeline

Legend:

Book

In proceedings

Article

PhD thesis

Dataset

Other

Links

Online presence:

on orcid.org

On csauthors.net:

Bibliography

2024

Provable Guarantees for Sparsity Recovery with Deterministic Missing Data Patterns.

[BibT_eX]

[DOI]

,

Trans. Mach. Learn. Res., 2024

Recovering Exact Support in Federated lasso without Optimization.

[BibT_eX]

[DOI]

,

Trans. Mach. Learn. Res., 2024

On the Dual Problem of Convexified Convolutional Neural Networks.

[BibT_eX]

[DOI]

,

,

Trans. Mach. Learn. Res., 2024

PyXAB - A Python Library for \mathcal{X}-Armed Bandit and Online Blackbox Optimization Algorithms.

[BibT_eX]

[DOI]

,

,

,

J. Open Source Softw., 2024

Personalized Federated X-armed Bandit.

[BibT_eX]

[DOI]

,

,

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Federated X-armed Bandit.

[BibT_eX]

[DOI]

,

,

,

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Invex Programs: First Order Algorithms and Their Convergence.

[BibT_eX]

[DOI]

,

,

CoRR, 2023

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

[BibT_eX]

[DOI]

,

CoRR, 2023

Partial Inference in Structured Prediction.

[BibT_eX]

[DOI]

,

CoRR, 2023

Matrix Completion from General Deterministic Sampling Patterns.

[BibT_eX]

[DOI]

,

,

,

CoRR, 2023

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

[BibT_eX]

[DOI]

,

,

,

CoRR, 2023

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

[BibT_eX]

[DOI]

,

,

CoRR, 2023

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

[BibT_eX]

[DOI]

,

,

CoRR, 2023

Exact Inference in High-order Structured Prediction.

[BibT_eX]

[DOI]

,

Proceedings of the International Conference on Machine Learning, 2023

Provable Computational and Statistical Guarantees for Efficient Learning of Continuous-Action Graphical Games.

[BibT_eX]

[DOI]

,

Proceedings of the IEEE International Conference on Acoustics, 2023

MEDIC: Remove Model Backdoors via Importance Driven Cloning.

[BibT_eX]

[DOI]

,

,

,

,

,

,

,

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.

[BibT_eX]

[DOI]

,

J. Mach. Learn. Res., 2022

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

[BibT_eX]

[DOI]

,

CoRR, 2022

Distributional Robustness Bounds Generalization Errors.

[BibT_eX]

[DOI]

,

,

CoRR, 2022

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

[BibT_eX]

[DOI]

,

CoRR, 2022

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

[BibT_eX]

[DOI]

,

,

CoRR, 2022

Meta Sparse Principal Component Analysis.

[BibT_eX]

[DOI]

,

CoRR, 2022

Dual Convexified Convolutional Neural Networks.

[BibT_eX]

[DOI]

,

,

CoRR, 2022

Support Recovery in Sparse PCA with Incomplete Data.

[BibT_eX]

[DOI]

,

,

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.

[BibT_eX]

[DOI]

,

,

Proceedings of the IEEE International Symposium on Information Theory, 2022

A Simple Unified Framework for High Dimensional Bandit Problems.

[BibT_eX]

[DOI]

,

,

Proceedings of the International Conference on Machine Learning, 2022

Sparse Mixed Linear Regression with Guarantees: Taming an Intractable Problem with Invex Relaxation.

[BibT_eX]

[DOI]

,

Proceedings of the International Conference on Machine Learning, 2022

Exact Partitioning of High-Order Planted Models with A Tensor Nuclear Norm Constraint.

[BibT_eX]

[DOI]

,

Proceedings of the IEEE International Conference on Acoustics, 2022

Information Theoretic Limits For Standard and One-Bit Compressed Sensing with Graph-Structured Sparsity.

[BibT_eX]

[DOI]

,

Proceedings of the IEEE International Conference on Acoustics, 2022

Provable Sample Complexity Guarantees For Learning Of Continuous-Action Graphical Games With Nonparametric Utilities.

[BibT_eX]

[DOI]

,

Proceedings of the IEEE International Conference on Acoustics, 2022

Federated Myopic Community Detection with One-shot Communication.

[BibT_eX]

[DOI]

,

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.

[BibT_eX]

[DOI]

,

,

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.

[BibT_eX]

[DOI]

,

,

CoRR, 2021

Inverse Reinforcement Learning in the Continuous Setting with Formal Guarantees.

[BibT_eX]

[DOI]

,

,

CoRR, 2021

PrivSyn: Differentially Private Data Synthesis.

[BibT_eX]

[DOI]

,

,

,

,

,

,

,

Proceedings of the 30th USENIX Security Symposium, 2021

Inverse Reinforcement Learning in a Continuous State Space with Formal Guarantees.

[BibT_eX]

[DOI]

,

,

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.

[BibT_eX]

[DOI]

,

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.

[BibT_eX]

[DOI]

,

,

Proceedings of the IEEE International Symposium on Information Theory, 2021

Information Theoretic Limits of Exact Recovery in Sub-hypergraph Models for Community Detection.

[BibT_eX]

[DOI]

,

,

Proceedings of the IEEE International Symposium on Information Theory, 2021

Regularized Loss Minimizers with Local Data Perturbation: Consistency and Data Irrecoverability.

[BibT_eX]

[DOI]

,

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.

[BibT_eX]

[DOI]

Krishna Reddy Kesari

,

Proceedings of the IEEE International Symposium on Information Theory, 2021

Information-theoretic lower bounds for zero-order stochastic gradient estimation.

[BibT_eX]

[DOI]

Abdulrahman Alabdulkareem

,

Proceedings of the IEEE International Symposium on Information Theory, 2021

Information-Theoretic Bounds for Integral Estimation.

[BibT_eX]

[DOI]

Donald Q. Adams

,

,

Proceedings of the IEEE International Symposium on Information Theory, 2021

Meta Learning for Support Recovery in High-dimensional Precision Matrix Estimation.

[BibT_eX]

[DOI]

,

,

Proceedings of the 38th International Conference on Machine Learning, 2021

A Lower Bound for the Sample Complexity of Inverse Reinforcement Learning.

[BibT_eX]

[DOI]

,

Proceedings of the 38th International Conference on Machine Learning, 2021

Randomized Deep Structured Prediction for Discourse-Level Processing.

[BibT_eX]

[DOI]

Manuel Widmoser

,

Maria Leonor Pacheco

,

,

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.

[BibT_eX]

[DOI]

,

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.

[BibT_eX]

[DOI]

,

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.

[BibT_eX]

[DOI]

,

CoRR, 2020

Fundamental Limits of Adversarial Learning.

[BibT_eX]

[DOI]

,

,

CoRR, 2020

Support Union Recovery in Meta Learning of Gaussian Graphical Models.

[BibT_eX]

[DOI]

,

,

CoRR, 2020

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

[BibT_eX]

[DOI]

,

CoRR, 2020

Novel Change of Measure Inequalities and PAC-Bayesian Bounds.

[BibT_eX]

[DOI]

,

CoRR, 2020

Fairness constraints can help exact inference in structured prediction.

[BibT_eX]

[DOI]

,

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.

[BibT_eX]

[DOI]

,

Proceedings of the IEEE International Symposium on Information Theory, 2020

Minimax Bounds for Structured Prediction Based on Factor Graphs.

[BibT_eX]

[DOI]

,

,

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Minimax bounds for structured prediction.

[BibT_eX]

[DOI]

,

,

CoRR, 2019

Exact Recovery in the Latent Space Model.

[BibT_eX]

[DOI]

,

CoRR, 2019

On the Statistical Efficiency of Optimal Kernel Sum Classifiers.

[BibT_eX]

[DOI]

Raphael Arkady Meyer

,

CoRR, 2019

On the Correctness and Sample Complexity of Inverse Reinforcement Learning.

[BibT_eX]

[DOI]

,

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Exact inference in structured prediction.

[BibT_eX]

[DOI]

,

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.

[BibT_eX]

[DOI]

,

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.

[BibT_eX]

[DOI]

,

,

Proceedings of the IEEE International Symposium on Information Theory, 2019

Optimality Implies Kernel Sum Classifiers are Statistically Efficient.

[BibT_eX]

[DOI]

Raphael A. Meyer

,

Proceedings of the 36th International Conference on Machine Learning, 2019

Reconstructing a Bounded-Degree Directed Tree Using Path Queries.

[BibT_eX]

[DOI]

,

Proceedings of the 57th Annual Allerton Conference on Communication, 2019

2018

Regularized Loss Minimizers with Local Data Obfuscation.

[BibT_eX]

[DOI]

,

CoRR, 2018

Learning Binary Bayesian Networks in Polynomial Time and Sample Complexity.

[BibT_eX]

[DOI]

,

CoRR, 2018

On the Sample Complexity of Learning from a Sequence of Experiments.

[BibT_eX]

[DOI]

,

,

CoRR, 2018

Information-theoretic Limits for Community Detection in Network Models.

[BibT_eX]

[DOI]

,

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.

[BibT_eX]

[DOI]

,

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.

[BibT_eX]

[DOI]

,

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.

[BibT_eX]

[DOI]

,

Proceedings of the 35th International Conference on Machine Learning, 2018

Statistically and Computationally Efficient Variance Estimator for Kernel Ridge Regression.

[BibT_eX]

[DOI]

,

,

Proceedings of the 56th Annual Allerton Conference on Communication, 2018

On the Statistical Efficiency of Compositional Nonparametric Prediction.

[BibT_eX]

[DOI]

,

,

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

Learning Sparse Polymatrix Games in Polynomial Time and Sample Complexity.

[BibT_eX]

[DOI]

,

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

Learning linear structural equation models in polynomial time and sample complexity.

[BibT_eX]

[DOI]

,

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Learning Sparse Potential Games in Polynomial Time and Sample Complexity.

[BibT_eX]

[DOI]

,

CoRR, 2017

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

[BibT_eX]

[DOI]

,

CoRR, 2017

Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity.

[BibT_eX]

[DOI]

,

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.

[BibT_eX]

[DOI]

,

,

Mohit Tawarmalani

Proceedings of the 2017 IEEE International Symposium on Information Theory, 2017

On the sample complexity of learning graphical games.

[BibT_eX]

[DOI]

Proceedings of the 55th Annual Allerton Conference on Communication, 2017

Learning Graphical Games from Behavioral Data: Sufficient and Necessary Conditions.

[BibT_eX]

[DOI]

,

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

Information-theoretic limits of Bayesian network structure learning.

[BibT_eX]

[DOI]

,

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

On the Sample Complexity of Learning Sparse Graphical Games.

[BibT_eX]

[DOI]

CoRR, 2016

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

[BibT_eX]

[DOI]

,

CoRR, 2016

Structured Prediction: From Gaussian Perturbations to Linear-Time Principled Algorithms.

[BibT_eX]

[DOI]

,

Tommi S. Jaakkola

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Information-theoretic lower bounds for recovery of diffusion network structures.

[BibT_eX]

[DOI]

,

Proceedings of the IEEE International Symposium on Information Theory, 2016

From behavior to sparse graphical games: Efficient recovery of equilibria.

[BibT_eX]

[DOI]

,

Proceedings of the 54th Annual Allerton Conference on Communication, 2016

2015

Learning the structure and parameters of large-population graphical games from behavioral data.

[BibT_eX]

[DOI]

,

J. Mach. Learn. Res., 2015

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

[BibT_eX]

[DOI]

Eugene Belilovsky

,

Katerina Gkirtzou

,

Michail Misyrlis

,

,

,

Nelly Alia-Klein

,

Rita Z. Goldstein

,

Dimitris Samaras

,

Matthew B. Blaschko

Comput. Medical Imaging Graph., 2015

2014

A Unified Framework for Consistency of Regularized Loss Minimizers.

[BibT_eX]

[DOI]

,

Tommi S. Jaakkola

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.

[BibT_eX]

[DOI]

,

Tommi S. Jaakkola

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.

[BibT_eX]

[DOI]

,

Tommi S. Jaakkola

Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

fMRI Analysis with Sparse Weisfeiler-Lehman Graph Statistics.

[BibT_eX]

[DOI]

Katerina Gkirtzou

,

,

Dimitris Samaras

,

Rita Z. Goldstein

,

Matthew B. Blaschko

Proceedings of the Machine Learning in Medical Imaging - 4th International Workshop, 2013

FMRI analysis of cocaine addiction using k-support sparsity.

[BibT_eX]

[DOI]

Katerina Gkirtzou

,

,

Dimitris Samaras

,

Rita Z. Goldstein

,

Matthew B. Blaschko

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.

[BibT_eX]

[DOI]

,

Tommi S. Jaakkola

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

Can a Single Brain Region Predict a Disorder?

[BibT_eX]

[DOI]

,

,

Rita Z. Goldstein

,

Hoi-Chung Leung

,

Dimitris Samaras

IEEE Trans. Medical Imaging, 2012

Variable Selection for Gaussian Graphical Models.

[BibT_eX]

[DOI]

,

Dimitris Samaras

,

,

Guillermo A. Cecchi

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

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

[BibT_eX]

[DOI]

,

Dimitris Samaras

CoRR, 2012

Convergence Rates of Biased Stochastic Optimization for Learning Sparse Ising Models.

[BibT_eX]

[DOI]

Proceedings of the 29th International Conference on Machine Learning, 2012

Two-person interaction detection using body-pose features and multiple instance learning.

[BibT_eX]

[DOI]

,

,

Debaleena Chattopadhyay

,

,

Dimitris Samaras

Proceedings of the 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2012

2011

Digital Analysis and Visualization of Swimming Motion.

[BibT_eX]

[DOI]

Can Kirmizibayrak

,

,

,

,

Int. J. Virtual Real., 2011

Lipschitz Parametrization of Probabilistic Graphical Models.

[BibT_eX]

[DOI]

Proceedings of the UAI 2011, 2011

2010

Simple fully automated group classification on brain FMRI.

[BibT_eX]

[DOI]

,

Dimitris Samaras

,

,

Rita Z. Goldstein

Proceedings of the 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2010

Multi-Task Learning of Gaussian Graphical Models.

[BibT_eX]

[DOI]

,

Dimitris Samaras

Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

2009

Sparse and Locally Constant Gaussian Graphical Models.

[BibT_eX]

[DOI]

,

,

Dimitris Samaras

,

,

Rita Z. Goldstein

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.

[BibT_eX]

[DOI]

,

Dimitris Samaras

,

,

,

Nelly Alia-Klein

,

,

,

Rita Z. Goldstein

Proceedings of the Medical Image Computing and Computer-Assisted Intervention, 2008

Loading...