42 citations · 45 across the 8 of their papers we have counts for
11 papers
Optimal Mistake Bounds for Transductive Online Learning
Zachary Chase, Steve Hanneke, Shay Moran +1
We resolve a 30-year-old open problem concerning the power of unlabeled data in online learning by tightly quantifying the gap between transductive and standard online learning. In…
Protocols for Verifying Smooth Strategies in Bandits and Games
Miranda Christ, Daniel Reichman, Jonathan Shafer
We study protocols for verifying approximate optimality of strategies in multi-armed bandits and normal-form games. As the number of actions available to each player is often large…
Oblivious Defense in ML Models: Backdoor Removal without Detection
Shafi Goldwasser, Jonathan Shafer, Neekon Vafa +1
As society grows more reliant on machine learning, ensuring the security of machine learning systems against sophisticated attacks becomes a pressing concern. A recent result of Go…
Which Algorithms Have Tight Generalization Bounds?
Michael Gastpar, Ido Nachum, Jonathan Shafer +1
We study which machine learning algorithms have tight generalization bounds. First, we present conditions that preclude the existence of tight generalization bounds. Specifically,…
A Trichotomy for Transductive Online Learning
Steve Hanneke, Shay Moran, Jonathan Shafer
We present new upper and lower bounds on the number of learner mistakes in the `transductive' online learning setting of Ben-David, Kushilevitz and Mansour (1997). This setting is…
The Bayesian Stability Zoo
Shay Moran, Hilla Schefler, Jonathan Shafer
We show that many definitions of stability found in the learning theory literature are equivalent to one another. We distinguish between two families of definitions of stability: d…
Fantastic Generalization Measures are Nowhere to be Found
Michael Gastpar, Ido Nachum, Jonathan Shafer +1
We study the notion of a generalization bound being uniformly tight, meaning that the difference between the bound and the population loss is small for all learning algorithms and…
PAC Verification of Statistical Algorithms
Saachi Mutreja, Jonathan Shafer
Goldwasser et al. (2021) recently proposed the setting of PAC verification, where a hypothesis (machine learning model) that purportedly satisfies the agnostic PAC learning objecti…
Fine-Grained Distribution-Dependent Learning Curves
Olivier Bousquet, Steve Hanneke, Shay Moran +2
Learning curves plot the expected error of a learning algorithm as a function of the number of labeled samples it receives from a target distribution. They are widely used as a mea…
A Direct Sum Result for the Information Complexity of Learning
Ido Nachum, Jonathan Shafer, Amir Yehudayoff
How many bits of information are required to PAC learn a class of hypotheses of VC dimension ? The mathematical setting we follow is that of Bassily et al. (2018), where the val…
Learners that Use Little Information
Raef Bassily, Shay Moran, Ido Nachum +2
We study learning algorithms that are restricted to using a small amount of information from their input sample. We introduce a category of learning algorithms we term -bit info…