Jean Honorio

Assistant Professor in the Computer Science Department at Purdue.
Assistant Professor in the Statistics Department (by courtesy) at Purdue.
Lawson Building 2142-J, West Lafayette, IN 47907
e-mail: jhonorio at purdue.edu

Modern statistical problems are high dimensional (big data). My research in this area focus on developing computationally and statistically efficient algorithms, understanding their behavior using concepts such as convergence, sample complexity, and privacy, and designing new modeling paradigms such as models rooted in game theory. My theoretical and algorithmic work is directly motivated by, and contributes to, applications in political science (affiliation and influence), neuroscience (brain disorders such as addiction), and genetics (diseases such as cancer). [vita]

Prior to joining Purdue, I was a postdoctoral associate at MIT CSAIL, working with Tommi Jaakkola. My Erdös number is 3: Jean Honorio → Tommi Jaakkola → Noga Alon → Paul Erdös.

People (my students* and student coauthors)

Current: Adarsh Barik*, Kevin Bello*, Asish Ghoshal*, Longyun Guo, Chuyang Ke*, Zitao Li, Raphael Meyer, Keehwan Park, Yixi Xu, Past: Yu-Jun Li, Meimei Liu, Zhaosen Wang
Here is a note for prospective students that are considering working with me.

News

9/5/18. Kevin got 2 papers, Chuyang got 1 paper accepted at NIPS.
8/3/18. Meimei got 1 paper accepted at Allerton.
5/11/18. Asish got 1 paper accepted at ICML.
12/22/17. Asish got 2 papers, Yixi got 1 paper accepted at AISTATS.
9/4/17. Asish got 1 paper accepted at NIPS.
8/17/17. NSF RI:Small grant awarded to do research on structured prediction.
6/26/17. Coorganizing the 3rd Workshop on Algorithmic Game Theory and Data Science at ACM EC.

Teaching

CS 69000-SML: Statistical Machine Learning II: Spring 2019, also offered on Spring 2017
CS 37300: Data Mining And Machine Learning: Fall 2018
CS 59000-HLT: Hands-On Learning Theory: Fall 2018, also offered on Fall 2017, Fall 2016 and Fall 2015
CS 57800: Statistical Machine Learning: Spring 2018, also offered on Fall 2017 and Fall 2016
CS 52000: Computational Methods In Optimization: Spring 2016

Publications (by year / by topic)

2019

Exact Recovery in the Latent Space Model. (Preprint)
Ke C., Honorio J.
(Under submission.)

Information Theoretic Limits for Standard and One-Bit Compressed Sensing with Graph-Structured Sparsity. (Preprint)
Barik A., Honorio J.
(Under submission.)

Learning Discrete Bayesian Networks in Polynomial Time and Sample Complexity. (Preprint)
Barik A., Honorio J.
(Under submission.)

Cost-Aware Learning for Improved Identifiability with Multiple Experiments. (Preprint)
Guo L., Honorio J., Morgan J.
(Under submission.)

Regularized Loss Minimizers with Local Data Perturbation: Consistency and Data Irrecoverability. (Preprint)
Li Z., Honorio J.
(Under submission.)

On the Statistical Efficiency of Optimal Kernel Sum Classifiers. (Preprint)
Meyer R., Honorio J.
(Under submission.)

The Error Probability of Random Fourier Features is Dimensionality Independent. (Preprint)
Li Y., Honorio J.
(Under submission.)

Reconstructing a Bounded-Degree Directed Tree Using Path Queries. (Preprint)
Wang Z., Honorio J.
(Under submission.)

On the Statistical Efficiency of L_1,p Multi-Task Learning of Gaussian Graphical Models. (Preprint)
Honorio J., Jaakkola T., Samaras D.
(In preparation.) [code]

2018

Learning Latent Variable Structured Prediction Models with Gaussian Perturbations.
Bello K., Honorio J.
Neural Information Processing Systems. Montreal/Canada.

Learning Causal Bayes Networks Using Interventional Path Queries in Polynomial Time and Sample Complexity.
Bello K., Honorio J.
Neural Information Processing Systems. Montreal/Canada.

Information-Theoretic Limits for Community Detection in Network Models.
Ke C., Honorio J.
Neural Information Processing Systems. Montreal/Canada.

Statistically and Computationally Efficient Variance Estimator for Kernel Ridge Regression.
Liu M., Honorio J., Cheng G.
IEEE Allerton Conference on Communication, Control and Computing. Illinois.

Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time.
Ghoshal A., Honorio J.
International Conference on Machine Learning. Stockholm/Sweden.

Learning Linear Structural Equation Models in Polynomial Time and Sample Complexity.
Ghoshal A., Honorio J.
Artificial Intelligence and Statistics. Canary Islands/Spain.

Learning Sparse Polymatrix Games in Polynomial Time and Sample Complexity.
Ghoshal A., Honorio J.
Artificial Intelligence and Statistics. Canary Islands/Spain.

On the Statistical Efficiency of Compositional Nonparametric Prediction.
Xu Y., Honorio J., Wang X.
Artificial Intelligence and Statistics. Canary Islands/Spain.

2017

Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity.
Ghoshal A., Honorio J.
Neural Information Processing Systems. California.

On the Sample Complexity of Learning Graphical Games.
Honorio J.
IEEE Allerton Conference on Communication, Control and Computing. Illinois.

Information Theoretic Limits for Linear Prediction with Graph-Structured Sparsity.
Barik A., Honorio J., Tawarmalani M.
IEEE International Symposium on Information Theory. Aachen/Germany.

Learning Graphical Games from Behavioral Data: Sufficient and Necessary Conditions.
Ghoshal A., Honorio J.
Artificial Intelligence and Statistics. Florida.

Information-Theoretic Limits of Bayesian Network Structure Learning.
Ghoshal A., Honorio J.
Artificial Intelligence and Statistics. Florida.

2016

From Behavior to Sparse Graphical Games: Efficient Recovery of Equilibria.
Ghoshal A., Honorio J.
IEEE Allerton Conference on Communication, Control and Computing. Illinois.

Structured Prediction: From Gaussian Perturbations to Linear-Time Principled Algorithms.
Honorio J., Jaakkola T.
Uncertainty in Artificial Intelligence. New York.

Information-Theoretic Lower Bounds for Recovery of Diffusion Network Structures.
Park K., Honorio J.
IEEE International Symposium on Information Theory. Barcelona/Spain.

Variable Selection in Gaussian Markov Random Fields.
Honorio J., Samaras D., Rish I., Cecchi G.
Invited book chapter in Log-Linear Models, Extensions and Applications.
Edited by Aravkin A., Deng L., Heigold G., Jebara T., Kanevski D., Wright S. (to be published on December, 2016)

2015

Learning the Structure and Parameters of Large-Population Graphical Games from Behavioral Data.
Honorio J., Ortiz L.
Journal of Machine Learning Research, 16(Jun): pp. 1157-1210 [code]

Predictive Sparse Modeling of fMRI Data for Improved Classification, Regression, and Visualization Using the k-Support Norm.
Belilovsky E., Gkirtzou K., Misyrlis M., Konova A., Honorio J., Alia-Klein N., Goldstein R., Samaras D., Blaschko M.
Computerized Medical Imaging and Graphics, 46(1): pp. 40-46

Integration of PCA with a Novel Machine Learning Method for Reparameterization and Assisted History Matching Geologically Complex Reservoirs.
Honorio J., Chen C., Gao G., Du K., Jaakkola T.
Society of Petroleum Engineers: 91th Annual Technical Conference and Exhibition. Houston.

2014

A Unified Framework for Consistency of Regularized Loss Minimizers.
Honorio J., Jaakkola T.
International Conference on Machine Learning. Beijing/China.

Tight Bounds for the Expected Risk of Linear Classifiers and PAC-Bayes Finite-Sample Guarantees.
Honorio J., Jaakkola T.
Artificial Intelligence and Statistics. Reykjavik/Iceland.

Classification on Brain Functional Magnetic Resonance Imaging: Dimensionality, Sample Size, Subject Variability and Noise.
Honorio J.
Invited book chapter in Frontiers of Medical Imaging.
Edited by Chen C., World Scientific Publishing Company.

Improving Interpretability of Graphical Models in fMRI Analysis via Variable-Selection.
Honorio J., Samaras D., Rish I., Cecchi G.
Organization for Human Brain Mapping, Anual Meeting. Hamburg/Germany.

Predicting Cross-task Behavioral Variables from fMRI Data Using the k-Support Norm. (Best paper award)
Misyrlis M., Konova A., Blaschko M., Honorio J., Alia-Klein N., Goldstein R., Samaras D.
Medical Image Computing and Computer-Assisted Intervention. Workshop on Sparsity Techniques in Medical Imaging. Boston.

Methylphenidate Enhances Executive Function and Optimizes Prefrontal Function in Both Health and Cocaine Addiction.
Moeller S., Honorio J., Tomasi D., Parvaz M., Woicik P., Volkow N., Goldstein R.
Cerebral Cortex, 24(3): pp. 643-653

Integration of Principal Component Analysis and Streamline Information for the History Matching of Channelized Reservoirs.
Chen C., Gao G., Honorio J., Gelderblom P., Jimenez E., Jaakkola T.
Society of Petroleum Engineers: 90th Annual Technical Conference and Exhibition. Amsterdam/The Netherlands.

2013

Inverse Covariance Estimation for High-Dimensional Data in Linear Time and Space: Spectral Methods for Riccati and Sparse Models.
Honorio J., Jaakkola T.
Uncertainty in Artificial Intelligence. Washington. [code]

Two-Sided Exponential Concentration Bounds for Bayes Error Rate and Shannon Entropy.
Honorio J., Jaakkola T.
International Conference on Machine Learning. Atlanta.

fMRI Analysis of Cocaine Addiction Using k-Support Sparsity.
Gkirtzou K., Honorio J., Samaras D., Goldstein R., Blaschko M.
IEEE International Symposium on Biomedical Imaging. California. [code]

fMRI Analysis with Sparse Weisfeiler-Lehman Graph Statistics.
Gkirtzou K., Honorio J., Samaras D., Goldstein R., Blaschko M.
Medical Image Computing and Computer-Assisted Intervention, Workshop on Machine Learning in Medical Imaging. Nagoya/Japan. [code]

2012

Convergence Rates of Biased Stochastic Optimization for Learning Sparse Ising Models.
Honorio J.
International Conference on Machine Learning. Edinburgh/Scotland. [code]

Variable Selection for Gaussian Graphical Models.
Honorio J., Samaras D., Rish I., Cecchi G.
Artificial Intelligence and Statistics. Canary Islands/Spain. [code]

Can a Single Brain Region Predict a Disorder?
Honorio J., Tomasi D., Goldstein R., Leung H.C., Samaras D.
IEEE Transactions on Medical Imaging, 31(11): pp. 2062-2072 [code]

Two-person Interaction Detection Using Body-Pose Features and Multiple Instance Learning.
Yun K., Honorio J., Chattopadhyay D., Berg T., Samaras D.
IEEE Computer Vision and Pattern Recognition, Workshop on Human Activity Understanding from 3D Data. Rhode Island. [data]

Dopaminergic Involvement During Mental Fatigue in Health and Cocaine Addiction.
Moeller S., Tomasi D., Honorio J., Volkow N., Goldstein R.
Translational Psychiatry, 2: e176

Enhanced Midbrain Response at 6-month Follow-up in Cocaine Addiction, Association with Reduced Drug-related Choice.
Moeller S., Tomasi D., Woicik P., Maloney T., Alia-Klein N., Honorio J., Telang F., Wang G., Wang R., Sinha R., Carise D., Astone-Twerell J., Bolger J., Volkow N., Goldstein R.
Addiction Biology, 17(6): pp. 1013-25

2011

Lipschitz Parametrization of Probabilistic Graphical Models.
Honorio J.
Uncertainty in Artificial Intelligence. Barcelona/Spain.

Digital Analysis and Visualization of Swimming Motion.
Kirmizibayrak C., Honorio J., Jiang X., Mark R., Hahn J.
The International Journal of Virtual Reality, 10(3): pp. 9-16

Digital Analysis and Visualization of Swimming Motion.
Kirmizibayrak C., Honorio J., Jiang X., Mark R., Hahn J.
Conference on Computer Animation and Social Agents, Simulation of Sports Motion Workshop. Chengdu/China.

Dopaminergic contribution to endogenous motivation during cognitive control breakdown.
Moeller S., Tomasi D., Honorio J., Volkow N., Goldstein R.
Society for Neuroscience. Washington DC.

2010

Multi-Task Learning of Gaussian Graphical Models.
Honorio J., Samaras D.
International Conference on Machine Learning. Haifa/Israel. [code]

Simple Fully Automated Group Classification on Brain fMRI.
Honorio J., Samaras D., Tomasi D., Goldstein R.
IEEE International Symposium on Biomedical Imaging. Rotterdam/The Netherlands. [code]

Disrupted Functional Connectivity with Dopaminergic Midbrain in Cocaine Abusers.
Tomasi D., Volkow N., Wang R., Honorio J., Maloney T., Alia-Klein N., Woicik P., Telang F., Goldstein R.
Public Library of Science, PLoS ONE, 5(5): e10815

Oral Methylphenidate Normalizes Cingulate Activity in Cocaine Addiction During a Salient Cognitive Task.
Goldstein R., Woicik P., Maloney T., Tomasi D., Alia-Klein N., Shan J., Honorio J., Samaras D., Ruiliang W., Telang F., Wang G., Volkow N.
Proceedings of the National Academy of Sciences, 107(38): pp. 16667-72

2009

Sparse and Locally Constant Gaussian Graphical Models.
Honorio J., Ortiz L., Samaras D., Paragios N., Goldstein R.
Neural Information Processing Systems. Vancouver/Canada. [code]

Learning Brain fMRI Structure Through Sparseness and Local Constancy.
Honorio J., Ortiz L., Samaras D., Goldstein R.
Neural Information Processing Systems, Workshop on Connectivity Inference in NeuroImaging. Vancouver/Canada.

A Functional Geometry of fMRI BOLD Signal Interactions.
Langs G., Samaras D., Paragios N., Honorio J., Golland P., Alia-Klein N., Tomasi D., Volkow N., Goldstein R.
Neural Information Processing Systems, Workshop on Connectivity Inference in NeuroImaging. Vancouver/Canada.

Dopaminergic Response to Drug Words in Cocaine Addiction.
Goldstein R., Tomasi D., Alia-Klein N., Honorio J., Maloney T., Woicik P., Wang R., Telang F., Volkow N.
Journal of Neuroscience, 29(18): pp. 6001-6

Anterior Cingulate Cortex Hypoactivations to an Emotionally Salient Task in Cocaine Addiction.
Goldstein R., Alia-Klein N., Tomasi D., Honorio J., Maloney T., Woicik P., Wang R., Telang F., Volkow N.
Proceedings of the National Academy of Sciences, 106(23): pp. 9453-8

2008

Task-Specific Functional Brain Geometry from Model Maps.
Langs G., Samaras D., Paragios N., Honorio J., Alia-Klein N., Tomasi D., Volkow N., Goldstein R.
Medical Image Computing and Computer-Assisted Intervention. New York.