Cover Image

On the Computability of Robust PAC Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Gourdeau, Pascale, Lechner, Tosca, Urner, Ruth
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

On the Computability of Multiclass PAC Learning
by: Gourdeau, Pascale, et al.
Published: (2025)

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

Calibration through the Lens of Interpretability
by: Torabian, Alireza, et al.
Published: (2024)

On the Learnability of Distribution Classes with Adaptive Adversaries
by: Lechner, Tosca, et al.
Published: (2025)

Discriminative Feature Feedback with General Teacher Classes
by: Oz, Omri Bar, et al.
Published: (2025)

Distribution Learnability and Robustness
by: Ben-David, Shai, et al.
Published: (2024)

Score Design for Multi-Criteria Incentivization
by: Kabra, Anmol, et al.
Published: (2024)

Enhancing PAC Learning of Half spaces Through Robust Optimization Techniques
by: Tavangari, Shirmohammad, et al.
Published: (2024)

Distributionally Robust PAC-Bayesian Control
by: Herceg, Domagoj, et al.
Published: (2026)

Efficient Optimal PAC Learning
by: Høgsgaard, Mikael Møller
Published: (2025)

Symmetries in PAC-Bayesian Learning
by: Beck, Armin, et al.
Published: (2025)

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

Error Exponent in Agnostic PAC Learning
by: Hendel, Adi, et al.
Published: (2024)

Learning via Surrogate PAC-Bayes
by: Picard-Weibel, Antoine, et al.
Published: (2024)

A Characterization of Semi-Supervised Adversarially-Robust PAC Learnability
by: Attias, Idan, et al.
Published: (2022)

Causal Machine Learning for Moderation Effects
by: Bearth, Nora, et al.
Published: (2024)

PAC-Bayesian Adversarially Robust Generalization Bounds for Graph Neural Network
by: Sun, Tan, et al.
Published: (2024)

On Agnostic PAC Learning in the Small Error Regime
by: Asilis, Julian, et al.
Published: (2025)

PAC-Bayesian Soft Actor-Critic Learning
by: Tasdighi, Bahareh, et al.
Published: (2023)

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

Uniform Computability of PAC Learning
by: Brattka, Vasco, et al.
Published: (2026)

PAC Apprenticeship Learning with Bayesian Active Inverse Reinforcement Learning
by: Bajgar, Ondrej, et al.
Published: (2025)

More Flexible PAC-Bayesian Meta-Learning by Learning Learning Algorithms
by: Zakerinia, Hossein, et al.
Published: (2024)

Tight PAC-Bayesian Risk Certificates for Contrastive Learning
by: Van Elst, Anna, et al.
Published: (2024)

Monotonic Learning in the PAC Framework: A New Perspective
by: Li, Ming, et al.
Published: (2025)

Towards Efficient Contrastive PAC Learning
by: Shen, Jie
Published: (2025)

PAC Guarantees for Reinforcement Learning: Sample Complexity, Coverage, and Structure
by: Steier, Joshua
Published: (2026)

Efficient and Noise-Tolerant PAC Learning of Multiclass Linear Classifiers
by: Adhikari, Rita, et al.
Published: (2026)

PAC-Bayesian Reward-Certified Outcome Weighted Learning
by: Ishikawa, Yuya, et al.
Published: (2026)

Deep Exploration with PAC-Bayes
by: Tasdighi, Bahareh, et al.
Published: (2024)

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

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

Is Transductive Learning Equivalent to PAC Learning?
by: Dughmi, Shaddin, et al.
Published: (2024)

PAC-Bayes Bounds for Gibbs Posteriors via Singular Learning Theory
by: Wang, Chenyang, et al.
Published: (2026)

A PAC-Bayesian View of Generalisation for Physics-Informed Machine Learning
by: Nguyen, Thien V., et al.
Published: (2026)

Randomized Pairwise Learning with Adaptive Sampling: A PAC-Bayes Analysis
by: Zhou, Sijia, et al.
Published: (2025)

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

Do PAC-Learners Learn the Marginal Distribution?
by: Hopkins, Max, et al.
Published: (2023)

Is Efficient PAC Learning Possible with an Oracle That Responds 'Yes' or 'No'?
by: Daskalakis, Constantinos, et al.
Published: (2024)

PAC-Bayesian Reinforcement Learning Trains Generalizable Policies
by: Zitouni, Abdelkrim, et al.
Published: (2025)