Biography

I am a Postdoc working with Robert Nowak at the University of Wisconsin. Previously, I was a Postdoc at the Paul G. Allen School of Computer Science & Engineering at the University of Washington under Kevin Jamieson. I completed my PhD in the Electrical Engineering and Computer Science Department at the University of Michigan where my advisor was Clayton Scott. Prior to that, I double-majored in mathematics and philosophy at the University of Chicago. My research focuses on pure exploration multi-armed bandits, recommender systems, and nonparametric estimation. I am also interested in applications of machine learning that promote the social good. As a Data Science for Social Good fellow at the University of Chicago in 2015, I helped develop the Legislative Influence Detector.

I’m on the job market this year! Feel free to reach out if you think I’d be a good fit for a position!

Preprints

• Y. Zhu, J. Katz-Samuels, R. Nowak. Near Instance Optimal Model Selection for Pure Exploration Linear Bandits.
• C. Coleman, E. Chou, J. Katz-Samuels, S. Culatana, P. Bailis, A. C. Berg, R. Nowak, R. Sumbaly, M. Zaharia, I. Zeki Yalniz. Similarity Search for Efficient Active Learning and Search of Rare Concepts

Publications

• J. Katz-Samuels, B. Mason, K. Jamieson, R. Nowak. Practical, Provably-Correct Interactive Learning in the Realizable Setting: The Power of True Believers, to appear in NeurIPS 2021.
• J. Katz-Samuels, J. Zhang, L. Jain, K. Jamieson. Improved Algorithms for Agnostic Pool-based Active Classification, ICML 2021.
• R. Camilleri, J. Katz-Samuels, K. Jamieson. High-Dimensional Experimental Design and Kernel Bandits, ICML 2021 (Long Talk).
• A. Wagenmaker, J. Katz-Samuels, K. Jamieson. Experimental Design for Regret Minimization in Linear Bandits, AISTATS 2021.
• J. Katz-Samuels, L. Jain, Z. Karnin, K. Jamieson. An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits, NeurIPS 2020.
• J. Katz-Samuels, and K. Jamieson. The True Sample Complexity of Identifying Good Arm. AISTATS 2020.
• J. Katz-Samuels, and C. Scott. Top Feasible Arm Identification. AISTATS 2019.
• J. Katz-Samuels, G. Blanchard, and C. Scott. Decontamination of Mutual Contamination Models. Journal of Machine Learning Research 2019.
• J. Katz-Samuels, and C. Scott. Feasible Arm Identification. ICML 2018 (Long Talk).
• J. Katz-Samuels, and C. Scott. Nonparametric Preference Completion. AISTATS 2018.
• Burgess, Matthew, Eugenia Giraudy, Julian Katz-Samuels, Joe Walsh, Derek Willis, Lauren Haynes, and Rayid Ghani. "The Legislative Influence Detector: Finding Text Reuse in State Legislation." In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 57-66. ACM, 2016.

Contact

katzsamuels “at” wisc.edu