Cover Image

A Characterization of Semi-Supervised Adversarially-Robust PAC Learnability

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Attias, Idan, Hanneke, Steve, Mansour, Yishay
Format:	Preprint
Published:	2022
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Adversarially Robust PAC Learnability of Real-Valued Functions
by: Attias, Idan, et al.
Published: (2022)

Optimal Learners for Realizable Regression: PAC Learning and Online Learning
by: Attias, Idan, et al.
Published: (2023)

On Characterizing Learnability for Adversarial Noisy Bandits
by: Hanneke, Steve, et al.
Published: (2026)

Sample Compression Scheme Reductions
by: Attias, Idan, et al.
Published: (2024)

Regret-Oracle Complexity Tradeoffs in Agnostic Online Learning
by: Attias, Idan, et al.
Published: (2026)

Tradeoffs between Mistakes and ERM Oracle Calls in Online and Transductive Online Learning
by: Attias, Idan, et al.
Published: (2025)

Agnostic Sample Compression Schemes for Regression
by: Attias, Idan, et al.
Published: (2018)

A Complete Characterization of Learnability for Stochastic Noisy Bandits
by: Hanneke, Steve, et al.
Published: (2024)

The Horizon Threshold in Cooperative Multi-Agent Reward-Free Exploration
by: Barnea, Idan, et al.
Published: (2026)

Collaborating in Multi-Armed Bandits with Strategic Agents
by: Barnea, Idan, et al.
Published: (2026)

Individual Regret in Cooperative Stochastic Multi-Armed Bandits
by: Barnea, Idan, et al.
Published: (2024)

A Theory of Optimistically Universal Online Learnability for General Concept Classes
by: Hanneke, Steve, et al.
Published: (2025)

PAC Learning with Improvements
by: Attias, Idan, et al.
Published: (2025)

Sample Complexity of Autoregressive Reasoning: Chain-of-Thought vs. End-to-End
by: Hanneke, Steve, et al.
Published: (2026)

Learnability Gaps of Strategic Classification
by: Cohen, Lee, et al.
Published: (2024)

Fast Rates for Bandit PAC Multiclass Classification
by: Erez, Liad, et al.
Published: (2024)

Online Learning in MDPs with Partially Adversarial Transitions and Losses
by: Schlisselberg, Ofir, et al.
Published: (2026)

Private List Learnability vs. Online List Learnability
by: Hanneke, Steve, et al.
Published: (2025)

Revisiting Agnostic PAC Learning
by: Hanneke, Steve, et al.
Published: (2024)

Sample Complexity of Agnostic Multiclass Classification: Natarajan Dimension Strikes Back
by: Cohen, Alon, et al.
Published: (2025)

What is Learnable in Valiant's Theory of the Learnable?
by: Hanneke, Steve, et al.
Published: (2026)

Scale-Sensitive Shattering: Learnability and Evaluability at Optimal Scale
by: Aiyer, Shashaank, et al.
Published: (2026)

Optimal Prediction Using Expert Advice and Randomized Littlestone Dimension
by: Filmus, Yuval, et al.
Published: (2023)

Bandit-Feedback Online Multiclass Classification: Variants and Tradeoffs
by: Filmus, Yuval, et al.
Published: (2024)

Near-optimal Regret Using Policy Optimization in Online MDPs with Aggregate Bandit Feedback
by: Lancewicki, Tal, et al.
Published: (2025)

Optimal Regret for Policy Optimization in Contextual Bandits
by: Levy, Orin, et al.
Published: (2026)

Rising Rested MAB with Linear Drift
by: Amichay, Omer, et al.
Published: (2025)

Non-stochastic Bandits With Evolving Observations
by: Bar-On, Yogev, et al.
Published: (2024)

Regret Bounds for Adversarial Contextual Bandits with General Function Approximation and Delayed Feedback
by: Levy, Orin, et al.
Published: (2025)

PAC Learnability in the Presence of Performativity
by: Kirev, Ivan, et al.
Published: (2025)

PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting
by: Hanneke, Steve, et al.
Published: (2026)

How to Boost Any Loss Function
by: Nock, Richard, et al.
Published: (2024)

Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations around Unknown Marginals
by: Liu, Ziyi, et al.
Published: (2024)

Capacity-Constrained Online Learning with Delays: Scheduling Frameworks and Regret Trade-offs
by: Ryabchenko, Alexander, et al.
Published: (2025)

Sequential Probability Assignment with Contexts: Minimax Regret, Contextual Shtarkov Sums, and Contextual Normalized Maximum Likelihood
by: Liu, Ziyi, et al.
Published: (2024)

A Combinatorial Characterization of Supervised Online Learnability
by: Raman, Vinod, et al.
Published: (2023)

Simplifying Adversarially Robust PAC Learning with Tolerance
by: Ashtiani, Hassan, et al.
Published: (2025)

Batch Ensemble for Variance Dependent Regret in Stochastic Bandits
by: Cassel, Asaf, et al.
Published: (2024)

Theoretical Foundations and Effective Algorithms for Policy-Aware Simulator Learning
by: Dann, Christoph, et al.
Published: (2026)

Learning-Augmented Algorithms for Boolean Satisfiability
by: Attias, Idan, et al.
Published: (2025)