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Journal Articles
- 2024
[j1]Chhavi Yadav, Michal Moshkovitz, Kamalika Chaudhuri:
XAudit : A Learning-Theoretic Look at Auditing with Explanations. Trans. Mach. Learn. Res. 2024 (2024)
Conference and Workshop Papers
- 2024
- [c15]Omer Ben-Porat, Yishay Mansour, Michal Moshkovitz, Boaz Taitler:
Principal-Agent Reward Shaping in MDPs. AAAI 2024: 9502-9510 - [c14]Nave Frost, Zachary C. Lipton, Yishay Mansour, Michal Moshkovitz:
Partially Interpretable Models with Guarantees on Coverage and Accuracy. ALT 2024: 590-613 - 2023
- [c13]Robi Bhattacharjee, Jacob Imola, Michal Moshkovitz, Sanjoy Dasgupta:
Online k-means Clustering on Arbitrary Data Streams. ALT 2023: 204-236 - [c12]Lee Cohen, Yishay Mansour, Michal Moshkovitz:
Finding Safe Zones of Markov Decision Processes Policies. NeurIPS 2023 - 2022
- [c11]Sanjoy Dasgupta, Nave Frost, Michal Moshkovitz:
Framework for Evaluating Faithfulness of Local Explanations. ICML 2022: 4794-4815 - 2021
- [c10]Robi Bhattacharjee, Michal Moshkovitz:
No-substitution k-means Clustering with Adversarial Order. ALT 2021: 345-366 - [c9]Michal Moshkovitz:
Unexpected Effects of Online no-Substitution k-means Clustering. ALT 2021: 892-930 - [c8]Max Hopkins, Daniel Kane, Shachar Lovett, Michal Moshkovitz:
Bounded Memory Active Learning through Enriched Queries. COLT 2021: 2358-2387 - [c7]Michal Moshkovitz, Yao-Yuan Yang, Kamalika Chaudhuri:
Connecting Interpretability and Robustness in Decision Trees through Separation. ICML 2021: 7839-7849 - [c6]Tom Hess, Michal Moshkovitz, Sivan Sabato:
A Constant Approximation Algorithm for Sequential Random-Order No-Substitution k-Median Clustering. NeurIPS 2021: 3298-3308 - 2020
- [c5]Michal Moshkovitz, Sanjoy Dasgupta, Cyrus Rashtchian, Nave Frost:
Explainable k-Means and k-Medians Clustering. ICML 2020: 7055-7065 - [c4]Alon Gonen, Shachar Lovett, Michal Moshkovitz:
Towards a Combinatorial Characterization of Bounded-Memory Learning. NeurIPS 2020 - 2018
- [c3]Dana Moshkovitz, Michal Moshkovitz:
Entropy Samplers and Strong Generic Lower Bounds For Space Bounded Learning. ITCS 2018: 28:1-28:20 - 2017
- [c2]Dana Moshkovitz, Michal Moshkovitz:
Mixing Implies Lower Bounds for Space Bounded Learning. COLT 2017: 1516-1566 - 2014
- [c1]Michal Moshkovitz, Naftali Tishby:
Control your information for better predictions. ISIT 2014: 916-920
Informal and Other Publications
- 2024
- [i22]Omer Ben-Porat, Yishay Mansour, Michal Moshkovitz, Boaz Taitler:
Principal-Agent Reward Shaping in MDPs. CoRR abs/2401.00298 (2024) - [i21]Zhili Feng, Michal Moshkovitz, Dotan Di Castro, J. Zico Kolter:
An Axiomatic Approach to Model-Agnostic Concept Explanations. CoRR abs/2401.06890 (2024) - [i20]Dotan Di Castro, Omkar Joglekar, Shir Kozlovsky, Vladimir Tchuiev, Michal Moshkovitz:
Gradient-Free Neural Network Training on the Edge. CoRR abs/2410.09734 (2024) - 2022
- [i19]Sanjoy Dasgupta, Nave Frost, Michal Moshkovitz:
Framework for Evaluating Faithfulness of Local Explanations. CoRR abs/2202.00734 (2022) - [i18]Lee Cohen, Yishay Mansour, Michal Moshkovitz:
Finding Safe Zones of policies Markov Decision Processes. CoRR abs/2202.11593 (2022) - [i17]Yishay Mansour, Michal Moshkovitz, Cynthia Rudin:
There is no Accuracy-Interpretability Tradeoff in Reinforcement Learning for Mazes. CoRR abs/2206.04266 (2022) - [i16]Chhavi Yadav, Michal Moshkovitz, Kamalika Chaudhuri:
A Learning-Theoretic Framework for Certified Auditing of Machine Learning Models. CoRR abs/2206.04740 (2022) - 2021
- [i15]Tom Hess, Michal Moshkovitz, Sivan Sabato:
A Constant Approximation Algorithm for Sequential No-Substitution k-Median Clustering under a Random Arrival Order. CoRR abs/2102.04050 (2021) - [i14]Max Hopkins, Daniel Kane, Shachar Lovett, Michal Moshkovitz:
Bounded Memory Active Learning through Enriched Queries. CoRR abs/2102.05047 (2021) - [i13]Michal Moshkovitz, Yao-Yuan Yang, Kamalika Chaudhuri:
Connecting Interpretability and Robustness in Decision Trees through Separation. CoRR abs/2102.07048 (2021) - 2020
- [i12]Alon Gonen, Shachar Lovett, Michal Moshkovitz:
Towards a combinatorial characterization of bounded memory learning. CoRR abs/2002.03123 (2020) - [i11]Sanjoy Dasgupta, Nave Frost, Michal Moshkovitz, Cyrus Rashtchian:
Explainable k-Means and k-Medians Clustering. CoRR abs/2002.12538 (2020) - [i10]Nave Frost, Michal Moshkovitz, Cyrus Rashtchian:
ExKMC: Expanding Explainable k-Means Clustering. CoRR abs/2006.02399 (2020) - [i9]Robi Bhattacharjee, Michal Moshkovitz:
No-substitution k-means Clustering with Adversarial Order. CoRR abs/2012.14512 (2020) - 2019
- [i8]Tal Kachman, Michal Moshkovitz, Michal Rosen-Zvi:
Novel Uncertainty Framework for Deep Learning Ensembles. CoRR abs/1904.04917 (2019) - [i7]Michal Moshkovitz:
Unexpected Effects of Online K-means Clustering. CoRR abs/1908.06818 (2019) - 2017
- [i6]Michal Moshkovitz, Naftali Tishby:
Mixing Complexity and its Applications to Neural Networks. CoRR abs/1703.00729 (2017) - [i5]Michal Moshkovitz, Naftali Tishby:
A General Memory-Bounded Learning Algorithm. CoRR abs/1712.03524 (2017) - [i4]Michal Moshkovitz, Dana Moshkovitz:
Mixing Implies Lower Bounds for Space Bounded Learning. Electron. Colloquium Comput. Complex. TR17 (2017) - [i3]Michal Moshkovitz, Dana Moshkovitz:
Mixing Implies Strong Lower Bounds for Space Bounded Learning. Electron. Colloquium Comput. Complex. TR17 (2017) - 2016
- [i2]Roy Fox, Michal Moshkovitz, Naftali Tishby:
Principled Option Learning in Markov Decision Processes. CoRR abs/1609.05524 (2016) - 2010
- [i1]Michal Moshkovitz:
Distance Estimators with Sublogarithmic Number of Queries. Electron. Colloquium Comput. Complex. TR10 (2010)
Coauthor Index
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