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PAC learning

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

Papers

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Showing 251–275 of 289 papers

TitleStatusHype
Superpolynomial Lower Bounds for Decision Tree Learning and Testing—0
Supervising the Transfer of Reasoning Patterns in VQA—0
Symbolic Abstractions From Data: A PAC Learning Approach—0
The Optimal Sample Complexity of PAC Learning—0
The Power of Comparisons for Actively Learning Linear Classifiers—0
The Price is (Probably) Right: Learning Market Equilibria from Samples—0
The Sample Complexity of Multi-Distribution Learning for VC Classes—0
The sample complexity of multi-distribution learning—0
The VC-Dimension of Similarity Hypotheses Spaces—0
The working principles of model-based GAs fall within the PAC framework: A mathematical theory of problem decomposition—0
Learning General Halfspaces with General Massart Noise under the Gaussian Distribution—0
Tight Bounds for Collaborative PAC Learning via Multiplicative Weights—0
Tight Bounds on Low-degree Spectral Concentration of Submodular and XOS functions—0
Tight Lower Bounds for Locally Differentially Private Selection—0
Towards a combinatorial characterization of bounded memory learning—0
Towards a Combinatorial Characterization of Bounded-Memory Learning—0
Towards a theory of out-of-distribution learning—0
Towards Efficient Contrastive PAC Learning—0
Towards Understanding Multi-Round Large Language Model Reasoning: Approximability, Learnability and Generalizability—0
Tree Learning: Optimal Algorithms and Sample Complexity—0
Unified Algorithms for RL with Decision-Estimation Coefficients: PAC, Reward-Free, Preference-Based Learning, and Beyond—0
User-Level Differential Privacy With Few Examples Per User—0
-fractional Core Stability in Hedonic Games—0
VC Dimension and Distribution-Free Sample-Based Testing—0
Wasserstein Soft Label Propagation on Hypergraphs: Algorithm and Generalization Error Bounds—0
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