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 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. Our results include algorithms for learning and inference in structured prediction, community detection, learning Bayesian networks and graphical games. [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, Kevin Bello, Chuyang Ke, Hanbyul Lee, Wenjie Li, Zhanyu Wang, Xiaochen Yang.
Past. Asish Ghoshal.
Prospective. Here is a note for students who are considering working with me.

News

5/8/21. 2 papers accepted at ICML (Abi, Qian/Yilin).
4/30/21. 6 papers accepted at ISIT (Kevin/Qiuling, Donald/Adarsh, Jiajun/Chuyang, Zitao, Krishna, Abdulrahman).
1/22/21. 2 papers accepted at AISTATS (Zhanyu, Yuki).
9/25/20. 1 paper accepted at NeurIPS (Kevin).
1/6/20. 1 paper accepted at AISTATS (Kevin).
9/3/19. 3 papers accepted at NeurIPS (Kevin, Adarsh, Abi).
4/21/19. 1 paper accepted at ICML (Raphael).
9/5/18. 3 papers accepted at NeurIPS (Kevin:2, Chuyang).
5/11/18. 1 paper accepted at ICML (Asish).

Selected Publications (see all by year / by topic)

Fair Sparse Regression with Clustering: An Invex Relaxation for a Combinatorial Problem. (Under submission.)
Exact Partitioning of High-order Models with a Novel Convex Tensor Cone Relaxation. (Under submission.)
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 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 59000-HLT / STAT 59800-HLT. Hands-On Learning Theory: Fall 2020, also offered on Fall 2019, Fall 2018, Fall 2017, Fall 2016 and Fall 2015.
CS 69000-SML. Statistical Machine Learning II: Spring 2019, also offered on Spring 2017.
CS 52000. Computational Methods In Optimization: Spring 2016.