Task-Specific Functional Brain Geometry from Model Maps. - Georg Langs; Dimitris Samaras; Nikos Paragios; Jean Honorio; Nelly Alia-Klein; Dardo Tomasi; Nora D. Volkow; Rita Z. Goldstein (MICCAI (1), 2008)

Sparse and Locally Constant Gaussian Graphical Models. - Jean Honorio; Luis E. Ortiz; Dimitris Samaras; Nikos Paragios; Rita Z. Goldstein (NIPS, 2009)

Multi-Task Learning of Gaussian Graphical Models. - Jean Honorio; Dimitris Samaras (ICML, 2010)

Simple fully automated group classification on brain FMRI. - Jean Honorio; Dimitris Samaras; Dardo Tomasi; Rita Z. Goldstein (ISBI, 2010)

Lipschitz Parametrization of Probabilistic Graphical Models. - Jean Honorio (UAI, 2011)

Digital Analysis and Visualization of Swimming Motion. - Can Kirmizibayrak; Jean Honorio; Xiaolong Jiang; Mark Russell; James K. Hahn (Int. J. Virtual Real., 2011)

Two-person interaction detection using body-pose features and multiple instance learning. - Kiwon Yun; Jean Honorio; Debaleena Chattopadhyay; Tamara L. Berg; Dimitris Samaras (CVPR Workshops, 2012)

Convergence Rates of Biased Stochastic Optimization for Learning Sparse Ising Models. - Jean Honorio (ICML, 2012)

Simultaneous and Group-Sparse Multi-Task Learning of Gaussian Graphical Models - Jean Honorio; Dimitris Samaras (CoRR, 2012)

Variable Selection for Gaussian Graphical Models. - Jean Honorio; Dimitris Samaras; Irina Rish; Guillermo A. Cecchi (AISTATS, 2012)

Can a Single Brain Region Predict a Disorder? - Jean Honorio; Dardo Tomasi; Rita Z. Goldstein; Hoi-Chung Leung; Dimitris Samaras (IEEE Trans. Medical Imaging, 2012)

Two-Sided Exponential Concentration Bounds for Bayes Error Rate and Shannon Entropy. - Jean Honorio; Tommi S. Jaakkola (ICML (3), 2013)

FMRI analysis of cocaine addiction using k-support sparsity. - Katerina Gkirtzou; Jean Honorio; Dimitris Samaras; Rita Z. Goldstein; Matthew B. Blaschko (ISBI, 2013)

fMRI Analysis with Sparse Weisfeiler-Lehman Graph Statistics. - Katerina Gkirtzou; Jean Honorio; Dimitris Samaras; Rita Z. Goldstein; Matthew B. Blaschko (MLMI, 2013)

Inverse Covariance Estimation for High-Dimensional Data in Linear Time and Space: Spectral Methods for Riccati and Sparse Models. - Jean Honorio; Tommi S. Jaakkola (UAI, 2013)

Tight Bounds for the Expected Risk of Linear Classifiers and PAC-Bayes Finite-Sample Guarantees. - Jean Honorio; Tommi S. Jaakkola (AISTATS, 2014)

A Unified Framework for Consistency of Regularized Loss Minimizers. - Jean Honorio; Tommi S. Jaakkola (ICML, 2014)

Predictive sparse modeling of fMRI data for improved classification, regression, and visualization using the k-support norm. - Eugene Belilovsky; Katerina Gkirtzou; Michail Misyrlis; Anna B. Konova; Jean Honorio; Nelly Alia-Klein; Rita Z. Goldstein; Dimitris Samaras; Matthew B. Blaschko (Comput. Medical Imaging Graph., 2015)

Learning the structure and parameters of large-population graphical games from behavioral data. - Jean Honorio; Luis E. Ortiz (J. Mach. Learn. Res., 2015)

From behavior to sparse graphical games: Efficient recovery of equilibria. - Asish Ghoshal; Jean Honorio (Allerton, 2016)

Information-theoretic lower bounds for recovery of diffusion network structures. - Keehwan Park; Jean Honorio (ISIT, 2016)

Structured Prediction: From Gaussian Perturbations to Linear-Time Principled Algorithms. - Jean Honorio; Tommi S. Jaakkola (UAI, 2016)

Information-theoretic lower bounds on learning the structure of Bayesian networks. - Asish Ghoshal; Jean Honorio (CoRR, 2016)

On the Sample Complexity of Learning Sparse Graphical Games. - Jean Honorio (CoRR, 2016)

Information-theoretic limits of Bayesian network structure learning. - Asish Ghoshal; Jean Honorio (AISTATS, 2017)

Learning Graphical Games from Behavioral Data: Sufficient and Necessary Conditions. - Asish Ghoshal; Jean Honorio (AISTATS, 2017)

On the sample complexity of learning graphical games. - Jean Honorio (Allerton, 2017)

Information theoretic limits for linear prediction with graph-structured sparsity. - Adarsh Barik; Jean Honorio; Mohit Tawarmalani (ISIT, 2017)

Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity. - Asish Ghoshal; Jean Honorio (NIPS, 2017)

Learning Bayes networks using interventional path queries in polynomial time and sample complexity. - Kevin Bello; Jean Honorio (CoRR, 2017)

Learning Sparse Potential Games in Polynomial Time and Sample Complexity. - Asish Ghoshal; Jean Honorio (CoRR, 2017)

Learning linear structural equation models in polynomial time and sample complexity. - Asish Ghoshal; Jean Honorio (AISTATS, 2018)

Learning Sparse Polymatrix Games in Polynomial Time and Sample Complexity. - Asish Ghoshal; Jean Honorio (AISTATS, 2018)

On the Statistical Efficiency of Compositional Nonparametric Prediction. - Yixi Xu; Jean Honorio; Xiao Wang 045 (AISTATS, 2018)

Statistically and Computationally Efficient Variance Estimator for Kernel Ridge Regression. - Meimei Liu 002; Jean Honorio; Guang Cheng 003 (Allerton, 2018)

Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time. - Asish Ghoshal; Jean Honorio (ICML, 2018)

Learning latent variable structured prediction models with Gaussian perturbations. - Kevin Bello; Jean Honorio (NeurIPS, 2018)

Computationally and statistically efficient learning of causal Bayes nets using path queries. - Kevin Bello; Jean Honorio (NeurIPS, 2018)

Information-theoretic Limits for Community Detection in Network Models. - Chuyang Ke; Jean Honorio (NeurIPS, 2018)

On the Sample Complexity of Learning from a Sequence of Experiments. - Longyun Guo; Jean Honorio; John Morgan (CoRR, 2018)

Learning Binary Bayesian Networks in Polynomial Time and Sample Complexity. - Adarsh Barik; Jean Honorio (CoRR, 2018)

Regularized Loss Minimizers with Local Data Obfuscation. - Zitao Li; Jean Honorio (CoRR, 2018)

Reconstructing a Bounded-Degree Directed Tree Using Path Queries. - Zhaosen Wang; Jean Honorio (Allerton, 2019)

Optimality Implies Kernel Sum Classifiers are Statistically Efficient. - Raphael A. Meyer; Jean Honorio (ICML, 2019)

Cost-Aware Learning for Improved Identifiability with Multiple Experiments. - Longyun Guo; Jean Honorio; John Morgan (ISIT, 2019)

Learning Bayesian Networks with Low Rank Conditional Probability Tables. - Adarsh Barik; Jean Honorio (NeurIPS, 2019)

Exact inference in structured prediction. - Kevin Bello; Jean Honorio (NeurIPS, 2019)

On the Correctness and Sample Complexity of Inverse Reinforcement Learning. - Abi Komanduru; Jean Honorio (NeurIPS, 2019)

On the Statistical Efficiency of Optimal Kernel Sum Classifiers. - Raphael Arkady Meyer; Jean Honorio (CoRR, 2019)

Exact Recovery in the Latent Space Model. - Chuyang Ke; Jean Honorio (CoRR, 2019)

Minimax bounds for structured prediction. - Kevin Bello; Asish Ghoshal; Jean Honorio (CoRR, 2019)

Minimax Bounds for Structured Prediction Based on Factor Graphs. - Kevin Bello; Asish Ghoshal; Jean Honorio (AISTATS, 2020)

Provable Efficient Skeleton Learning of Encodable Discrete Bayes Nets in Poly-Time and Sample Complexity. - Adarsh Bank; Jean Honorio (ISIT, 2020)

Fairness constraints can help exact inference in structured prediction. - Kevin Bello; Jean Honorio (NeurIPS, 2020)

Novel Change of Measure Inequalities and PAC-Bayesian Bounds. - Yuki Ohnishi; Jean Honorio (CoRR, 2020)

Exact Support Recovery in Federated Regression with One-shot Communication. - Adarsh Barik; Jean Honorio (CoRR, 2020)

Support Union Recovery in Meta Learning of Gaussian Graphical Models. - Qian Zhang 067; Yilin Zheng; Jean Honorio (CoRR, 2020)

Fundamental Limits of Adversarial Learning. - Kevin Bello; Qiuling Xu; Jean Honorio (CoRR, 2020)

Information Theoretic Sample Complexity Lower Bound for Feed-Forward Fully-Connected Deep Networks. - Xiaochen Yang; Jean Honorio (CoRR, 2020)

Novel Change of Measure Inequalities with Applications to PAC-Bayesian Bounds and Monte Carlo Estimation. - Yuki Ohnishi; Jean Honorio (AISTATS, 2021)

The Sample Complexity of Meta Sparse Regression. - Zhanyu Wang; Jean Honorio (AISTATS, 2021)

Randomized Deep Structured Prediction for Discourse-Level Processing. - Manuel Widmoser; Maria Leonor Pacheco; Jean Honorio; Dan Goldwasser (EACL, 2021)

A Lower Bound for the Sample Complexity of Inverse Reinforcement Learning. - Abi Komanduru; Jean Honorio (ICML, 2021)

Meta Learning for Support Recovery in High-dimensional Precision Matrix Estimation. - Qian Zhang 067; Yilin Zheng; Jean Honorio (ICML, 2021)

Information-Theoretic Bounds for Integral Estimation. - Donald Q. Adams; Adarsh Barik; Jean Honorio (ISIT, 2021)

Information-theoretic lower bounds for zero-order stochastic gradient estimation. - Abdulrahman Alabdulkareem; Jean Honorio (ISIT, 2021)

First Order Methods take Exponential Time to Converge to Global Minimizers of Non-Convex Functions. - Krishna Reddy Kesari; Jean Honorio (ISIT, 2021)

Regularized Loss Minimizers with Local Data Perturbation: Consistency and Data Irrecoverability. - Zitao Li; Jean Honorio (ISIT, 2021)

Information Theoretic Limits of Exact Recovery in Sub-hypergraph Models for Community Detection. - Jiajun Liang; Chuyang Ke; Jean Honorio (ISIT, 2021)

A Le Cam Type Bound for Adversarial Learning and Applications. - Qiuling Xu; Kevin Bello; Jean Honorio (ISIT, 2021)

Fair Sparse Regression with Clustering: An Invex Relaxation for a Combinatorial Problem. - Adarsh Barik; Jean Honorio (NeurIPS, 2021)

Inverse Reinforcement Learning in a Continuous State Space with Formal Guarantees. - Gregory Dexter; Kevin Bello; Jean Honorio (NeurIPS, 2021)

PrivSyn: Differentially Private Data Synthesis. - Zhikun Zhang 001; Tianhao Wang 001; Ninghui Li; Jean Honorio; Michael Backes 001; Shibo He; Jiming Chen 001; Yang Zhang 016 (USENIX Security Symposium, 2021)

Inverse Reinforcement Learning in the Continuous Setting with Formal Guarantees. - Gregory Dexter; Kevin Bello; Jean Honorio (CoRR, 2021)

A Thorough View of Exact Inference in Graphs from the Degree-4 Sum-of-Squares Hierarchy. - Kevin Bello; Chuyang Ke; Jean Honorio (CoRR, 2021)

A View of Exact Inference in Graphs from the Degree-4 Sum-of-Squares Hierarchy. - Kevin Bello; Chuyang Ke; Jean Honorio (AISTATS, 2022)

Federated Myopic Community Detection with One-shot Communication. - Chuyang Ke; Jean Honorio (AISTATS, 2022)

Provable Sample Complexity Guarantees For Learning Of Continuous-Action Graphical Games With Nonparametric Utilities. - Adarsh Barik; Jean Honorio (ICASSP, 2022)

Information Theoretic Limits For Standard and One-Bit Compressed Sensing with Graph-Structured Sparsity. - Adarsh Barik; Jean Honorio (ICASSP, 2022)

Exact Partitioning of High-Order Planted Models with A Tensor Nuclear Norm Constraint. - Chuyang Ke; Jean Honorio (ICASSP, 2022)

Sparse Mixed Linear Regression with Guarantees: Taming an Intractable Problem with Invex Relaxation. - Adarsh Barik; Jean Honorio (ICML, 2022)

A Simple Unified Framework for High Dimensional Bandit Problems. - Wenjie Li; Adarsh Barik; Jean Honorio (ICML, 2022)

On the Fundamental Limits of Exact Inference in Structured Prediction. - Hanbyul Lee 001; Kevin Bello; Jean Honorio (ISIT, 2022)

Support Recovery in Sparse PCA with Incomplete Data. - Hanbyul Lee 001; Qifan Song; Jean Honorio (NeurIPS, 2022)

Dual Convexified Convolutional Neural Networks. - Site Bai; Chuyang Ke; Jean Honorio (CoRR, 2022)

Meta Sparse Principal Component Analysis. - Imon Banerjee; Jean Honorio (CoRR, 2022)

A Novel Plug-and-Play Approach for Adversarially Robust Generalization. - Deepak Maurya; Adarsh Barik; Jean Honorio (CoRR, 2022)

Meta Learning for High-dimensional Ising Model Selection Using $ ell_1$-regularized Logistic Regression. - Huiming Xie; Jean Honorio (CoRR, 2022)

Distributional Robustness Bounds Generalization Errors. - Shixiong Wang; Haowei Wang; Jean Honorio (CoRR, 2022)

Exact Partitioning of High-order Models with a Novel Convex Tensor Cone Relaxation. - Chuyang Ke; Jean Honorio (J. Mach. Learn. Res., 2022)

MEDIC: Remove Model Backdoors via Importance Driven Cloning. - Qiuling Xu; Guanhong Tao 001; Jean Honorio; Yingqi Liu; Shengwei An; Guangyu Shen; Siyuan Cheng 005; Xiangyu Zhang 001 (CVPR, 2023)

Provable Computational and Statistical Guarantees for Efficient Learning of Continuous-Action Graphical Games. - Adarsh Barik; Jean Honorio (ICASSP, 2023)

Exact Inference in High-order Structured Prediction. - Chuyang Ke; Jean Honorio (ICML, 2023)

Learning Against Distributional Uncertainty: On the Trade-off Between Robustness and Specificity. - Shixiong Wang; Haowei Wang; Jean Honorio (CoRR, 2023)

Support Recovery in Sparse PCA with Non-Random Missing Data. - Hanbyul Lee 001; Qifan Song; Jean Honorio (CoRR, 2023)

PyXAB - A Python Library for X-Armed Bandit and Online Blackbox Optimization Algorithms. - Wenjie Li; Haoze Li; Jean Honorio; Qifan Song (CoRR, 2023)

Matrix Completion from General Deterministic Sampling Patterns. - Hanbyul Lee 001; Rahul Mazumder; Qifan Song; Jean Honorio (CoRR, 2023)

Partial Inference in Structured Prediction. - Chuyang Ke; Jean Honorio (CoRR, 2023)

Outlier-robust Estimation of a Sparse Linear Model Using Invexity. - Adarsh Barik; Jean Honorio (CoRR, 2023)

Invex Programs: First Order Algorithms and Their Convergence. - Adarsh Barik; Suvrit Sra; Jean Honorio (CoRR, 2023)

Federated X-armed Bandit. - Wenjie Li; Qifan Song; Jean Honorio; Guang Lin 001 (AAAI, 2024)

Personalized Federated X-armed Bandit. - Wenjie Li; Qifan Song; Jean Honorio (AISTATS, 2024)

Support Recovery in Sparse PCA with General Missing Data. - Hanbyul Lee 001; Qifan Song; Jean Honorio (UAI, 2024)

Identifying Causal Changes Between Linear Structural Equation Models. - Vineet Malik; Kevin Bello; Asish Ghoshal; Jean Honorio (UAI, 2024)

Optimal bounds on a tree inference algorithm. - Jack Gardiner; Lachlan L. H. Andrew; Junhao Gan; Jean Honorio; Seeun William Umboh (CoRR, 2024)

PyXAB - A Python Library for mathcal{X}-Armed Bandit and Online Blackbox Optimization Algorithms. - Wenjie Li; Haoze Li; Qifan Song; Jean Honorio (J. Open Source Softw., 2024)

On the Dual Problem of Convexified Convolutional Neural Networks. - Site Bai; Chuyang Ke; Jean Honorio (Trans. Mach. Learn. Res., 2024)

Recovering Exact Support in Federated lasso without Optimization. - Adarsh Barik; Jean Honorio (Trans. Mach. Learn. Res., 2024)

Provable Guarantees for Sparsity Recovery with Deterministic Missing Data Patterns. - Chuyang Ke; Jean Honorio (Trans. Mach. Learn. Res., 2024)

A Theoretical Study of The Effects of Adversarial Attacks on Sparse Regression. - Deepak Maurya; Jean Honorio (Trans. Mach. Learn. Res., 2024)

Meta Learning for Support Recovery of High-Dimensional Ising Models. - Huiming Xie; Jean Honorio (Trans. Mach. Learn. Res., 2024)

Partial Inference in Structured Prediction. - Chuyang Ke; Deepak Maurya; Jean Honorio (ICASSP, 2025)

Exact Solutions of the Inner Optimization Problem of Adversarial Robustness. - Deepak Maurya; Adarsh Barik; Jean Honorio (ICASSP, 2025)