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PAC learning

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

Papers

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Showing 26–50 of 289 papers

TitleStatusHype
A Near-optimal Algorithm for Learning Margin Halfspaces with Massart Noise—0
Identifying Information from Observations with Uncertainty and Novelty—0
A Closer Look at the Learnability of Out-of-Distribution (OOD) Detection—0
Monotonic Learning in the PAC Framework: A New Perspective—0
Ensuring superior learning outcomes and data security for authorized learner—0
Of Dice and Games: A Theory of Generalized Boosting—0
Implicit High-Order Moment Tensor Estimation and Learning Latent Variable Models—0
Effective Littlestone Dimension—0
Probably Approximately Precision and Recall Learning—0
Learning multivariate Gaussians with imperfect advice—0
Reliable Learning of Halfspaces under Gaussian Marginals—0
Probably approximately correct high-dimensional causal effect estimation given a valid adjustment set—0
Prospective Learning: Learning for a Dynamic FutureCode1
Screw Geometry Meets Bandits: Incremental Acquisition of Demonstrations to Generate Manipulation Plans—0
Enhancing PAC Learning of Half spaces Through Robust Optimization Techniques—0
Measurability in the Fundamental Theorem of Statistical Learning—0
Learning Linear Attention in Polynomial Time—0
Strategic Classification With Externalities—0
Fill In The Gaps: Model Calibration and Generalization with Synthetic Data—0
Agnostic Smoothed Online Learning—0
Efficient PAC Learning of Halfspaces with Constant Malicious Noise Rate—0
Efficient Statistics With Unknown Truncation, Polynomial Time Algorithms, Beyond Gaussians—0
Derandomizing Multi-Distribution Learning—0
Fast decision tree learning solves hard coding-theoretic problems—0
A Practical Theory of Generalization in Selectivity Learning—0
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