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, phone: 765-496-6757
e-mail: jhonorio at purdue.edu

Through a unifying framework, with the power of continuous relaxations (beyond convexity) and primal-dual certificates, my research group produces novel algorithms for learning and inference in combinatorial problems. Our aim is to generate correct, computationally efficient and statistically efficient algorithms for high dimensional machine learning problems. We have produced breakthroughs not only on classical worst-case NP-hard problems, such as learning and inference in structured prediction, community detection and learning Bayesian networks, but also on areas of recent interest such as fairness, meta learning and federated learning. [vita]

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

Students

Current. Adarsh Barik (CS PhD), Site Bai (CS PhD), Chuyang Ke (CS PhD), Hanbyul Lee (Stat PhD), Wenjie Li (Stat PhD), Deepak Maurya (CS PhD).
Past. Kevin Bello (CS PhD), Asish Ghoshal (CS PhD).
Prospective. Here is a note for students who are considering working with me.

News

09/22. 1 paper accepted at NeurIPS (Hanbyul).
09/22. 1 paper accepted at JMLR (Chuyang).
09/22. visiting National University of Singapore.
05/22. talk at University of Oxford.
05/22. 2 papers accepted at ICML (Adarsh, Wenjie/Adarsh).
04/22. 1 paper accepted at ISIT (Hanbyul/Kevin).
04/22. Siya Goel (highschool student I have mentored) accepted to Stanford.
03/22. talk at National University of Singapore.
02/22. 7 professors from different departments + 5 corporate partners are sponsoring Capstone projects.
01/22. 3 papers accepted at ICASSP (Chuyang, Adarsh:2).
01/22. 2 papers accepted at AISTATS (Chuyang, Kevin/Chuyang).
10/21. talks at Columbia and Hebrew University of Jerusalem.
09/21. 2 papers accepted at NeurIPS (Adarsh, Gregory/Kevin).
09/21. talks at UMass Amherst and University College London.
08/21. NSF DMS:Collaborative grant awarded to do research on deep learning.
06/21. CI fellowship awarded to Kevin to do a postdoc in UChicago & CMU.
06/21. talk at CalTech.
05/21. 2 papers accepted at ICML (Abi, Qian/Yilin).
04/21. 6 papers accepted at ISIT (Kevin/Qiuling, Donald/Adarsh, Jiajun/Chuyang, Zitao, Krishna, Abdulrahman).
04/21. talk at CMU.
03/21. talks at MIT, UCSD and University of Wisconsin-Madison.
02/21. talks at Virginia Tech and Alan Turing Institute.
01/21. 2 papers accepted at AISTATS (Zhanyu, Yuki).
09/20. 1 paper accepted at NeurIPS (Kevin).
01/20. 1 paper accepted at AISTATS (Kevin).
09/19. 3 papers accepted at NeurIPS (Kevin, Adarsh, Abi).
04/19. 1 paper accepted at ICML (Raphael).
09/18. 3 papers accepted at NeurIPS (Kevin:2, Chuyang).
05/18. 1 paper accepted at ICML (Asish).
12/17. 3 papers accepted at AISTATS (Asish:2, Yixi).
09/17. 1 paper accepted at NeurIPS (Asish).
08/17. NSF RI:Small grant awarded to do research on structured prediction.

Selected Publications (see all)

Exact Partitioning of High-order Models with a Novel Convex Tensor Cone Relaxation. JMLR 2022.
Fair Sparse Regression with Clustering: An Invex Relaxation for a Combinatorial Problem. NeurIPS 2021 spotlight.
The Sample Complexity of Meta Sparse Regression. AISTATS 2021.
Fairness Constraints can Help Exact Inference in Structured Prediction. NeurIPS 2020.
Learning Linear Structural Equation Models in Polynomial Time and Sample Complexity. AISTATS 2018.

Teaching

CS 49000-DSC. Data Science Capstone: Spring 2022.
CS 69000-SML. Statistical Machine Learning II: Fall 2021, also offered on Spring 2019 and Spring 2017.
CS 59200-HLT / STAT 59800-HLT. Hands-On Learning Theory: Fall 2021, also offered on Fall 2020, Fall 2019, Fall 2018, Fall 2017, Fall 2016 and Fall 2015.
CS 37300. Data Mining and Machine Learning: Spring 2021, also offered on Fall 2019 and Fall 2018.
CS 57800. Statistical Machine Learning: Fall 2020, also offered on Spring 2020, Spring 2018, Fall 2017 and Fall 2016.
CS 52000. Computational Methods In Optimization: Spring 2016.