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Tag: pac

  • PAC-Bayesian Generalization Guarantees for Fairness on Stochastic and Deterministic Classifiers

    PAC-Bayesian Generalization Guarantees for Fairness on Stochastic and Deterministic Classifiers arXiv:2602.11722v1 Announce Type: new Abstract: Classical PAC generalization bounds on the prediction risk of a classifier are insufficient to provide theoretical guarantees on fairness when the goal is to learn models balancing predictive risk and fairness constraints. We propose a PAC-Bayesian framework for deriving generalization…

  • Towards A Unified PAC-Bayesian Framework for Norm-based Generalization Bounds

    Towards A Unified PAC-Bayesian Framework for Norm-based Generalization Bounds arXiv:2601.08100v1 Announce Type: new Abstract: Understanding the generalization behavior of deep neural networks remains a fundamental challenge in modern statistical learning theory. Among existing approaches, PAC-Bayesian norm-based bounds have demonstrated particular promise due to their data-dependent nature and their ability to capture algorithmic and geometric properties…

  • A note on the impossibility of conditional PAC-efficient reasoning in large language models

    A note on the impossibility of conditional PAC-efficient reasoning in large language models arXiv:2512.03057v1 Announce Type: new Abstract: We prove an impossibility result for conditional Probably Approximately Correct (PAC)-efficient reasoning in large language models. While recent work has established marginal PAC efficiency guarantees for composite models that switch between expensive expert models and cheaper fast…

  • Random Walk Learning and the Pac-Man Attack

    Random Walk Learning and the Pac-Man Attack arXiv:2508.05663v1 Announce Type: new Abstract: Random walk (RW)-based algorithms have long been popular in distributed systems due to low overheads and scalability, with recent growing applications in decentralized learning. However, their reliance on local interactions makes them inherently vulnerable to malicious behavior. In this work, we investigate an…

  • PAC Off-Policy Prediction of Contextual Bandits

    PAC Off-Policy Prediction of Contextual Bandits arXiv:2507.16236v1 Announce Type: new Abstract: This paper investigates off-policy evaluation in contextual bandits, aiming to quantify the performance of a target policy using data collected under a different and potentially unknown behavior policy. Recently, methods based on conformal prediction have been developed to construct reliable prediction intervals that guarantee…