Publications
New
An Axiomatic Approach to Model-Agnostic Concept Explanations (with Z. Feng, D. Di Castro, and Z. Kolter)
There is no Accuracy-Interpretability Tradeoff in Reinforcement Learning for Mazes (with Y. Mansour and C. Rudin)
A Learning-Theoretic Framework for Certified Auditing of Machine Learning Models (with C. Yadav and K. Chaudhuri)
Conferences
Partially Interpretable Models with Guarantees on Coverage and Accuracy (with N. Frost, Z. Lipton, and Y. Mansour), ALT 2024
Principal-Agent Reward Shaping in MDPs (with Omer Ben-Porat, Yishay Mansour, and Boaz Taitler), AAAI 2024
Finding Safe Zones of Markov Decision Processes Policies (with L. Cohen and Y. Mansour), NeurIPS 2023
Trustworthy and Socially Responsible Machine Learning Workshop, NeurIPS 2022
Machine Learning for Autonomous Driving Workshop, NeurIPS 2022
Online k-means Clustering on Arbitrary Data Streams (with R. Bhattacharjee, J. Imola, and S. Dasgupta), ALT 2023
Framework for Evaluating Faithfulness of Local Explanations (with S. Dasgupta and N. Frost), ICML 2022
A Constant Approximation Algorithm for Sequential Random-Order No-Substitution k-Median Clustering (with T. Hess and S. Sabato), NeurIPS 2021
Connecting Interpretability and Robustness in Decision Trees through Separation (with Y. Yang and K. Chaudhuri), ICML 2021
Bounded Memory Active Learning through Enriched Queries (with M. Hopkins, D. Kane, and S. Lovett), COLT 2021
Extended abstract accepted as a contributed talk to WIML 2020 (5% of accepted abstracts)
No-substitution k-means Clustering with Adversarial Order (with R. Bhattacharjee), ALT 2021
Towards a Combinatorial Characterization of Bounded Memory Learning (with A. Gonen and S. Lovett), NeurIPS 2020
Explainable k-Means and k-Medians Clustering (with S. Dasgupta, N. Frost, and C. Rashtchian), ICML 2020
Entropy Samplers and Strong Generic Lower Bounds For Space Bounded Learning (with D. Moshkovitz), ITCS 2018
Mixing Implies Lower Bounds for Space Bounded Learning (with D. Moshkovitz), COLT 2017
Control your Information for Better Predictions (with N. Tishby), ISIT 2014
Workshops
Explainable k-Means Clustering: Theory and Practice (with S. Dasgupta, N. Frost, and C. Rashtchian), XXAI Workshop, ICML 2020
Novel Uncertainty Framework for Deep Learning Ensembles (with T. Kachman and M. Rosen-Zvi), Bayesian Deep Learning Workshop, NeurIPS 2018
Mixing Complexity and its Applications to Neural Networks (with N. Tishby), WIML2017
Principled Option Learning in Markov Decision Processes (with R. Fox and N. Tishby), EWRL 2016
Information-Based Exploration for Reinforcement Learning (with N. Tishby), WIML 2016
Manuscripts
ExKMC: Expanding Explainable k-Means Clustering (with N. Frost, and C. Rashtchian), 2020
A General Memory-Bounded Learning Algorithm (with N. Tishby), 2017
Distance Estimators with Sublogarithmic Number of Queries, ECCC TR10-119, 2010