20 citations · 75 across the 17 of their papers we have counts for
18 papers
Principal-Agent Reward Shaping in MDPs
Omer Ben-Porat, Yishay Mansour, Michal Moshkovitz +1
Principal-agent problems arise when one party acts on behalf of another, leading to conflicts of interest. The economic literature has extensively studied principal-agent problems,…
There is no Accuracy-Interpretability Tradeoff in Reinforcement Learning for Mazes
Yishay Mansour, Michal Moshkovitz, Cynthia Rudin
Interpretability is an essential building block for trustworthiness in reinforcement learning systems. However, interpretability might come at the cost of deteriorated performance,…
XAudit : A Theoretical Look at Auditing with Explanations
Chhavi Yadav, Michal Moshkovitz, Kamalika Chaudhuri
Responsible use of machine learning requires models to be audited for undesirable properties. While a body of work has proposed using explanations for auditing, how to do so and wh…
Finding Safe Zones of policies Markov Decision Processes
Lee Cohen, Yishay Mansour, Michal Moshkovitz
Given a policy of a Markov Decision Process, we define a SafeZone as a subset of states, such that most of the policy's trajectories are confined to this subset. The quality of a S…
Framework for Evaluating Faithfulness of Local Explanations
Sanjoy Dasgupta, Nave Frost, Michal Moshkovitz
We study the faithfulness of an explanation system to the underlying prediction model. We show that this can be captured by two properties, consistency and sufficiency, and introdu…
Connecting Interpretability and Robustness in Decision Trees through Separation
Michal Moshkovitz, Yao-Yuan Yang, Kamalika Chaudhuri
Recent research has recognized interpretability and robustness as essential properties of trustworthy classification. Curiously, a connection between robustness and interpretabilit…
Bounded Memory Active Learning through Enriched Queries
Max Hopkins, Daniel Kane, Shachar Lovett +1
The explosive growth of easily-accessible unlabeled data has lead to growing interest in active learning, a paradigm in which data-hungry learning algorithms adaptively select info…
Online -means Clustering on Arbitrary Data Streams
Robi Bhattacharjee, Jacob Imola, Michal Moshkovitz +1
We consider online -means clustering where each new point is assigned to the nearest cluster center, after which the algorithm may update its centers. The loss incurred is the s…
A Constant Approximation Algorithm for Sequential Random-Order No-Substitution k-Median Clustering
Tom Hess, Michal Moshkovitz, Sivan Sabato
We study k-median clustering under the sequential no-substitution setting. In this setting, a data stream is sequentially observed, and some of the points are selected by the algor…
No-substitution k-means Clustering with Adversarial Order
Robi Bhattacharjee, Michal Moshkovitz
We investigate -means clustering in the online no-substitution setting when the input arrives in \emph{arbitrary} order. In this setting, points arrive one after another, and th…
ExKMC: Expanding Explainable -Means Clustering
Nave Frost, Michal Moshkovitz, Cyrus Rashtchian
Despite the popularity of explainable AI, there is limited work on effective methods for unsupervised learning. We study algorithms for -means clustering, focusing on a trade-of…
Towards a combinatorial characterization of bounded memory learning
Alon Gonen, Shachar Lovett, Michal Moshkovitz
Combinatorial dimensions play an important role in the theory of machine learning. For example, VC dimension characterizes PAC learning, SQ dimension characterizes weak learning wi…
Explainable -Means and -Medians Clustering
Sanjoy Dasgupta, Nave Frost, Michal Moshkovitz +1
Clustering is a popular form of unsupervised learning for geometric data. Unfortunately, many clustering algorithms lead to cluster assignments that are hard to explain, partially…
Unexpected Effects of Online no-Substitution k-means Clustering
Michal Moshkovitz
Offline k-means clustering was studied extensively, and algorithms with a constant approximation are available. However, online clustering is still uncharted. New factors come into…
Novel Uncertainty Framework for Deep Learning Ensembles
Tal Kachman, Michal Moshkovitz, Michal Rosen-Zvi
Deep neural networks have become the default choice for many of the machine learning tasks such as classification and regression. Dropout, a method commonly used to improve the con…
A General Memory-Bounded Learning Algorithm
Michal Moshkovitz, Naftali Tishby
Designing bounded-memory algorithms is becoming increasingly important nowadays. Previous works studying bounded-memory algorithms focused on proving impossibility results, while t…
Mixing Complexity and its Applications to Neural Networks
Michal Moshkovitz, Naftali Tishby
We suggest analyzing neural networks through the prism of space constraints. We observe that most training algorithms applied in practice use bounded memory, which enables us to us…
Principled Option Learning in Markov Decision Processes
Roy Fox, Michal Moshkovitz, Naftali Tishby
It is well known that options can make planning more efficient, among their many benefits. Thus far, algorithms for autonomously discovering a set of useful options were heuristic.…