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PAC learning

Probably Approximately Correct (PAC) learning analyzes machine learning mathematically using probability bounds.

Papers

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Showing 1–25 of 289 papers

TitleStatusHype
Lean Formalization of Generalization Error Bound by Rademacher ComplexityCode1
Prospective Learning: Learning for a Dynamic FutureCode1
VICE: Variational Interpretable Concept EmbeddingsCode1
Computational-Statistical Tradeoffs from NP-hardness—0
A Distributional-Lifting Theorem for PAC Learning—0
Conservative classifiers do consistently well with improving agents: characterizing statistical and online learning—0
Algorithms and SQ Lower Bounds for Robustly Learning Real-valued Multi-index Models—0
Attribute-Efficient PAC Learning of Sparse Halfspaces with Constant Malicious Noise Rate—0
A packing lemma for VCN_k-dimension and learning high-dimensional data—0
From learnable objects to learnable random objects—0
Numerical and statistical analysis of NeuralODE with Runge-Kutta time integration—0
PAC Learning with Improvements—0
A Linear Theory of Multi-Winner Voting—0
Towards Understanding Multi-Round Large Language Model Reasoning: Approximability, Learnability and Generalizability—0
Contrastive Learning with Nasty Noise—0
Towards Efficient Contrastive PAC Learning—0
On Agnostic PAC Learning in the Small Error Regime—0
Bandit Multiclass List Classification—0
Simplifying Adversarially Robust PAC Learning with Tolerance—0
On the Computability of Multiclass PAC Learning—0
Data-Driven Neural Certificate Synthesis—0
Efficient Optimal PAC Learning—0
PAC Learning is just Bipartite Matching (Sort of)—0
Distribution-Specific Agnostic Conditional Classification With Halfspaces—0
Ehrenfeucht-Haussler Rank and Chain of Thought—0
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