2 citations · 3 across the 16 of their papers we have counts for
16 papers · 1 filter
The Sample Complexity of Multiclass and Sparse Contextual Bandits
Liad Erez, Fan Chen, Alon Cohen +4
We study contextual bandits in the stochastic i.i.d.\ setting, where a learner observes contexts drawn from an unknown distribution, selects actions from a finite set , and aims…
A Theoretical Framework for Statistical Evaluability of Generative Models
Shashaank Aiyer, Yishay Mansour, Shay Moran +1
Statistical evaluation aims to estimate the generalization performance of a model using held-out i.i.d. test data sampled from the ground-truth distribution. In supervised learning…
Learning from Equivalence Queries, Revisited
Mark Braverman, Roi Livni, Yishay Mansour +2
Modern machine learning systems, such as generative models and recommendation systems, often evolve through a cycle of deployment, user interaction, and periodic model updates. Thi…
Learning Conditional Averages
Marco Bressan, Nataly Brukhim, Nicolo Cesa-Bianchi +4
We introduce the problem of learning conditional averages in the PAC framework. The learner receives a sample labeled by an unknown target concept from a known concept class, as in…
Sample Complexity of Agnostic Multiclass Classification: Natarajan Dimension Strikes Back
Alon Cohen, Liad Erez, Steve Hanneke +4
The fundamental theorem of statistical learning states that binary PAC learning is governed by a single parameter -- the Vapnik-Chervonenkis (VC) dimension -- which determines both…
The Hidden Cost of Approximation in Online Mirror Descent
Ofir Schlisselberg, Uri Sherman, Tomer Koren +1
Online mirror descent (OMD) is a fundamental algorithmic paradigm that underlies many algorithms in optimization, machine learning and sequential decision-making. The OMD iterates…
Fast Inference via Hierarchical Speculative Decoding
Clara Mohri, Haim Kaplan, Tal Schuster +2
Transformer language models generate text autoregressively, making inference latency proportional to the number of tokens generated. Speculative decoding reduces this latency witho…
Convergence and Sample Complexity of First-Order Methods for Agnostic Reinforcement Learning
Uri Sherman, Tomer Koren, Yishay Mansour
We study reinforcement learning (RL) in the agnostic policy learning setting, where the goal is to find a policy whose performance is competitive with the best policy in a given cl…
Convergence of Policy Mirror Descent Beyond Compatible Function Approximation
Uri Sherman, Tomer Koren, Yishay Mansour
Modern policy optimization methods roughly follow the policy mirror descent (PMD) algorithmic template, for which there are by now numerous theoretical convergence results. However…
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…
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…
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…