2 citations · 3 across the 16 of their papers we have counts for
10 papers · 1 filter
Of Dice and Games: A Theory of Generalized Boosting
Marco Bressan, Nataly Brukhim, Nicolò Cesa-Bianchi +4
Cost-sensitive loss functions are crucial in many real-world prediction problems, where different types of errors are penalized differently; for example, in medical diagnosis, a fa…
On Differentially Private Linear Algebra
Haim Kaplan, Yishay Mansour, Shay Moran +2
We introduce efficient differentially private (DP) algorithms for several linear algebraic tasks, including solving linear equalities over arbitrary fields, linear inequalities ove…
Probably Approximately Precision and Recall Learning
Lee Cohen, Yishay Mansour, Shay Moran +1
Precision and Recall are fundamental metrics in machine learning tasks where both accurate predictions and comprehensive coverage are essential, such as in multi-label learning, la…
The Real Price of Bandit Information in Multiclass Classification
Liad Erez, Alon Cohen, Tomer Koren +2
We revisit the classical problem of multiclass classification with bandit feedback (Kakade, Shalev-Shwartz and Tewari, 2008), where each input classifies to one of possible lab…
Fast Rates for Bandit PAC Multiclass Classification
Liad Erez, Alon Cohen, Tomer Koren +2
We study multiclass PAC learning with bandit feedback, where inputs are classified into one of possible labels and feedback is limited to whether or not the predicted labels ar…
A Theory of Interpretable Approximations
Marco Bressan, Nicolò Cesa-Bianchi, Emmanuel Esposito +3
Can a deep neural network be approximated by a small decision tree based on simple features? This question and its variants are behind the growing demand for machine learning model…
Credit Attribution and Stable Compression
Roi Livni, Shay Moran, Kobbi Nissim +1
Credit attribution is crucial across various fields. In academic research, proper citation acknowledges prior work and establishes original contributions. Similarly, in generative…
Data Reconstruction: When You See It and When You Don't
Edith Cohen, Haim Kaplan, Yishay Mansour +4
We revisit the fundamental question of formally defining what constitutes a reconstruction attack. While often clear from the context, our exploration reveals that a precise defini…
Learning-Augmented Algorithms with Explicit Predictors
Marek Elias, Haim Kaplan, Yishay Mansour +1
Recent advances in algorithmic design show how to utilize predictions obtained by machine learning models from past and present data. These approaches have demonstrated an enhancem…
Learnability Gaps of Strategic Classification
Lee Cohen, Yishay Mansour, Shay Moran +1
In contrast with standard classification tasks, strategic classification involves agents strategically modifying their features in an effort to receive favorable predictions. For i…