43 citations · 48 across the 10 of their papers we have counts for
11 papers
On Union-Closedness of Language Generation
Steve Hanneke, Amin Karbasi, Anay Mehrotra +1
We investigate language generation in the limit - a model by Kleinberg and Mullainathan [NeurIPS 2024] and extended by Li, Raman, and Tewari [COLT 2025]. While Kleinberg and Mullai…
Tradeoffs between Mistakes and ERM Oracle Calls in Online and Transductive Online Learning
Idan Attias, Steve Hanneke, Arvind Ramaswami
We study online and transductive online learning when the learner interacts with the concept class only via Empirical Risk Minimization (ERM) or weak consistency oracles on arbitra…
Sample Compression Scheme Reductions
Idan Attias, Steve Hanneke, Arvind Ramaswami
We present novel reductions from sample compression schemes in multiclass classification, regression, and adversarially robust learning settings to binary sample compression scheme…
Optimal Learners for Realizable Regression: PAC Learning and Online Learning
Idan Attias, Steve Hanneke, Alkis Kalavasis +2
In this work, we aim to characterize the statistical complexity of realizable regression both in the PAC learning setting and the online learning setting. Previous work had establi…
Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization
Omar Montasser, Steve Hanneke, Nathan Srebro
We present a minimax optimal learner for the problem of learning predictors robust to adversarial examples at test-time. Interestingly, we find that this requires new algorithmic i…
Adversarially Robust PAC Learnability of Real-Valued Functions
Idan Attias, Steve Hanneke
We study robustness to test-time adversarial attacks in the regression setting with losses and arbitrary perturbation sets. We address the question of which function class…
A Characterization of Semi-Supervised Adversarially-Robust PAC Learnability
Idan Attias, Steve Hanneke, Yishay Mansour
We study the problem of learning an adversarially robust predictor to test time attacks in the semi-supervised PAC model. We address the question of how many labeled and unlabeled…
Transductive Robust Learning Guarantees
Omar Montasser, Steve Hanneke, Nathan Srebro
We study the problem of adversarially robust learning in the transductive setting. For classes of bounded VC dimension, we propose a simple transductive learner that…
Adversarially Robust Learning with Unknown Perturbation Sets
Omar Montasser, Steve Hanneke, Nathan Srebro
We study the problem of learning predictors that are robust to adversarial examples with respect to an unknown perturbation set, relying instead on interaction with an adversarial…
Reducing Adversarially Robust Learning to Non-Robust PAC Learning
Omar Montasser, Steve Hanneke, Nathan Srebro
We study the problem of reducing adversarially robust learning to standard PAC learning, i.e. the complexity of learning adversarially robust predictors using access to only a blac…
VC Classes are Adversarially Robustly Learnable, but Only Improperly
Omar Montasser, Steve Hanneke, Nathan Srebro
We study the question of learning an adversarially robust predictor. We show that any hypothesis class with finite VC dimension is robustly PAC learnable with an impr…