I received my Ph.D. in the electrical and computer engineering at the University of Michigan and my B.A. in mathematics and philosophy at the University of Chicago. I focus on designing practical machine learning algorithms that adaptively collect data to accelerate learning. My recent research interests include active learning, multi-armed bandits, black-box optimization, and out-of-distribution detection using deep neural networks. I am also very interested in machine learning applications that promote the social good. As a fellow at Data Science for Social Good (University of Chicago), I helped develop the Legislative Influence Detector.
- Training OOD Detectors in their Natural Habitats. J. Katz-Samuels, J. Nakhleh, R. Nowak, Y. Li. ICML 2022.
- GALAXY: Graph-based Active Learning at the Extreme. J. Zhang, J. Katz-Samuels, R. Nowak. ICML 2022.
- Near Instance Optimal Model Selection for Pure Exploration Linear Bandits. Y. Zhu, J. Katz-Samuels, R. Nowak. AISTATS 2022.
- Similarity Search for Efficient Active Learning and Search of Rare Concepts. C. Coleman, E. Chou, J. Katz-Samuels, S. Culatana, P. Bailis, A. C. Berg, R. Nowak, R. Sumbaly, M. Zaharia, I. Zeki Yalniz. AAAI 2022.
- Practical, Provably-Correct Interactive Learning in the Realizable Setting: The Power of True Believers. J. Katz-Samuels, B. Mason, K. Jamieson, R. Nowak. NeurIPS 2021.
- Improved Algorithms for Agnostic Pool-based Active Classification. J. Katz-Samuels, J. Zhang, L. Jain, K. Jamieson. ICML 2021.
- High-Dimensional Experimental Design and Kernel Bandits. R. Camilleri, J. Katz-Samuels, K. Jamieson. ICML 2021 (Long Talk).
- Experimental Design for Regret Minimization in Linear Bandits. A. Wagenmaker, J. Katz-Samuels, K. Jamieson. AISTATS 2021.
- An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits. J. Katz-Samuels, L. Jain, Z. Karnin, K. Jamieson. NeurIPS 2020.
- The True Sample Complexity of Identifying Good Arm. J. Katz-Samuels, K. Jamieson. AISTATS 2020.
- Top Feasible Arm Identification. J. Katz-Samuels, C. Scott. AISTATS 2019.
- Decontamination of Mutual Contamination Models. J. Katz-Samuels, G. Blanchard, C. Scott. Journal of Machine Learning Research 2019.
- Feasible Arm Identification. J. Katz-Samuels, C. Scott. ICML 2018 (Long Talk).
- Nonparametric Preference Completion. J. Katz-Samuels, C. Scott. AISTATS 2018.
- The Legislative Influence Detector: Finding Text Reuse in State Legislation. M. Burgess, E. Giraudy, J. Katz-Samuels, J. Walsh, D. Willis, L. Haynes, R. Ghani. KDD 2016.
katzsamuels “at” wisc.edu