Zobrazeno 1 - 10
of 196
pro vyhledávání: '"Moran, Shay"'
This work continues to investigate the link between differentially private (DP) and online learning. Alon, Livni, Malliaris, and Moran (2019) showed that for binary concept classes, DP learnability of a given class implies that it has a finite Little
Externí odkaz:
http://arxiv.org/abs/2407.07765
Credit attribution is crucial across various fields. In academic research, proper citation acknowledges prior work and establishes original contributions. Similarly, in generative models, such as those trained on existing artworks or music, it is imp
Externí odkaz:
http://arxiv.org/abs/2406.15916
We study multiclass PAC learning with bandit feedback, where inputs are classified into one of $K$ possible labels and feedback is limited to whether or not the predicted labels are correct. Our main contribution is in designing a novel learning algo
Externí odkaz:
http://arxiv.org/abs/2406.12406
Autor:
Bressan, Marco, Cesa-Bianchi, Nicolò, Esposito, Emmanuel, Mansour, Yishay, Moran, Shay, Thiessen, Maximilian
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 models that are *interpretable* by humans. In this work we study such quest
Externí odkaz:
http://arxiv.org/abs/2406.10529
This work studies embedding of arbitrary VC classes in well-behaved VC classes, focusing particularly on extremal classes. Our main result expresses an impossibility: such embeddings necessarily require a significant increase in dimension. In particu
Externí odkaz:
http://arxiv.org/abs/2405.17120
Autor:
Cohen, Edith, Kaplan, Haim, Mansour, Yishay, Moran, Shay, Nissim, Kobbi, Stemmer, Uri, Tsfadia, Eliad
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 definition is much more nuanced than it appears, to the extent that a single
Externí odkaz:
http://arxiv.org/abs/2405.15753
We revisit the classical problem of multiclass classification with bandit feedback (Kakade, Shalev-Shwartz and Tewari, 2008), where each input classifies to one of $K$ possible labels and feedback is restricted to whether the predicted label is corre
Externí odkaz:
http://arxiv.org/abs/2405.10027
List learning is a variant of supervised classification where the learner outputs multiple plausible labels for each instance rather than just one. We investigate classical principles related to generalization within the context of list learning. Our
Externí odkaz:
http://arxiv.org/abs/2403.10889
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 enhancement in performance when the predictions are accurate, while also ensur
Externí odkaz:
http://arxiv.org/abs/2403.07413
In contrast with standard classification tasks, strategic classification involves agents strategically modifying their features in an effort to receive favorable predictions. For instance, given a classifier determining loan approval based on credit
Externí odkaz:
http://arxiv.org/abs/2402.19303