Distance Estimators with Sublogarithmic Number of Queries. - Michal Moshkovitz (Electron. Colloquium Comput. Complex., 2010)

Control your information for better predictions. - Michal Moshkovitz; Naftali Tishby (ISIT, 2014)

Principled Option Learning in Markov Decision Processes. - Roy Fox; Michal Moshkovitz; Naftali Tishby (CoRR, 2016)

Mixing Complexity and its Applications to Neural Networks. - Michal Moshkovitz; Naftali Tishby (CoRR, 2017)

A General Memory-Bounded Learning Algorithm. - Michal Moshkovitz; Naftali Tishby (CoRR, 2017)

Mixing Implies Lower Bounds for Space Bounded Learning. - Michal Moshkovitz; Dana Moshkovitz (Electron. Colloquium Comput. Complex., 2017)

Mixing Implies Strong Lower Bounds for Space Bounded Learning. - Michal Moshkovitz; Dana Moshkovitz (Electron. Colloquium Comput. Complex., 2017)

Entropy Samplers and Strong Generic Lower Bounds For Space Bounded Learning. - Dana Moshkovitz; Michal Moshkovitz (ITCS, 2018)

Novel Uncertainty Framework for Deep Learning Ensembles. - Tal Kachman; Michal Moshkovitz; Michal Rosen-Zvi (CoRR, 2019)

Unexpected Effects of Online K-means Clustering. - Michal Moshkovitz (CoRR, 2019)

Explainable k-Means and k-Medians Clustering. - Michal Moshkovitz; Sanjoy Dasgupta; Cyrus Rashtchian; Nave Frost (ICML, 2020)

Towards a Combinatorial Characterization of Bounded-Memory Learning. - Alon Gonen; Shachar Lovett; Michal Moshkovitz (NeurIPS, 2020)

ExKMC: Expanding Explainable k-Means Clustering. - Nave Frost; Michal Moshkovitz; Cyrus Rashtchian (CoRR, 2020)

No-substitution k-means Clustering with Adversarial Order. - Robi Bhattacharjee; Michal Moshkovitz (ALT, 2021)

Unexpected Effects of Online no-Substitution k-means Clustering. - Michal Moshkovitz (ALT, 2021)

Bounded Memory Active Learning through Enriched Queries. - Max Hopkins; Daniel Kane 001; Shachar Lovett; Michal Moshkovitz (COLT, 2021)

Connecting Interpretability and Robustness in Decision Trees through Separation. - Michal Moshkovitz; Yao-Yuan Yang; Kamalika Chaudhuri (ICML, 2021)

A Constant Approximation Algorithm for Sequential Random-Order No-Substitution k-Median Clustering. - Tom Hess; Michal Moshkovitz; Sivan Sabato (NeurIPS, 2021)

A Constant Approximation Algorithm for Sequential No-Substitution k-Median Clustering under a Random Arrival Order. - Tom Hess; Michal Moshkovitz; Sivan Sabato (CoRR, 2021)

Framework for Evaluating Faithfulness of Local Explanations. - Sanjoy Dasgupta; Nave Frost; Michal Moshkovitz (ICML, 2022)

Finding Safe Zones of policies Markov Decision Processes. - Lee Cohen 001; Yishay Mansour; Michal Moshkovitz (CoRR, 2022)

There is no Accuracy-Interpretability Tradeoff in Reinforcement Learning for Mazes. - Yishay Mansour; Michal Moshkovitz; Cynthia Rudin (CoRR, 2022)

A Learning-Theoretic Framework for Certified Auditing of Machine Learning Models. - Chhavi Yadav; Michal Moshkovitz; Kamalika Chaudhuri (CoRR, 2022)

Online k-means Clustering on Arbitrary Data Streams. - Robi Bhattacharjee; Jacob Imola; Michal Moshkovitz; Sanjoy Dasgupta (ALT, 2023)

Finding Safe Zones of Markov Decision Processes Policies. - Lee Cohen 001; Yishay Mansour; Michal Moshkovitz (NeurIPS, 2023)

Principal-Agent Reward Shaping in MDPs. - Omer Ben-Porat; Yishay Mansour; Michal Moshkovitz; Boaz Taitler (AAAI, 2024)

Partially Interpretable Models with Guarantees on Coverage and Accuracy. - Nave Frost; Zachary C. Lipton; Yishay Mansour; Michal Moshkovitz (ALT, 2024)

An Axiomatic Approach to Model-Agnostic Concept Explanations. - Zhili Feng; Michal Moshkovitz; Dotan Di Castro; J. Zico Kolter (CoRR, 2024)

Gradient-Free Neural Network Training on the Edge. - Dotan Di Castro; Omkar Joglekar; Shir Kozlovsky; Vladimir Tchuiev; Michal Moshkovitz (CoRR, 2024)

Beyond Data Scarcity: A Frequency-Driven Framework for Zero-Shot Forecasting. - Liran Nochumsohn; Michal Moshkovitz; Orly Avner; Dotan Di Castro; Omri Azencot (CoRR, 2024)

XAudit : A Learning-Theoretic Look at Auditing with Explanations. - Chhavi Yadav; Michal Moshkovitz; Kamalika Chaudhuri (Trans. Mach. Learn. Res., 2024)