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PAC learning
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Probably Approximately Correct (PAC) learning analyzes machine learning mathematically using probability bounds.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
A Distributional-Lifting Theorem for PAC Learning
Conservative classifiers do consistently well with improving agents: characterizing statistical and online learning
Attribute-Efficient PAC Learning of Sparse Halfspaces with Constant Malicious Noise Rate
Algorithms and SQ Lower Bounds for Robustly Learning Real-valued Multi-index Models
A packing lemma for VCN${}_k$-dimension and learning high-dimensional data
From learnable objects to learnable random objects
Lean Formalization of Generalization Error Bound by Rademacher Complexity
Numerical and statistical analysis of NeuralODE with Runge-Kutta time integration
A Linear Theory of Multi-Winner Voting
Towards Understanding Multi-Round Large Language Model Reasoning: Approximability, Learnability and Generalizability
PAC Learning with Improvements
Contrastive Learning with Nasty Noise
Towards Efficient Contrastive PAC Learning
Bandit Multiclass List Classification
On Agnostic PAC Learning in the Small Error Regime
Simplifying Adversarially Robust PAC Learning with Tolerance
On the Computability of Multiclass PAC Learning
Data-Driven Neural Certificate Synthesis
Efficient Optimal PAC Learning
PAC Learning is just Bipartite Matching (Sort of)
Distribution-Specific Agnostic Conditional Classification With Halfspaces
Ehrenfeucht-Haussler Rank and Chain of Thought
The working principles of model-based GAs fall within the PAC framework: A mathematical theory of problem decomposition
Identifying Information from Observations with Uncertainty and Novelty
A Near-optimal Algorithm for Learning Margin Halfspaces with Massart Noise
Learning Noisy Halfspaces with a Margin: Massart is No Harder than Random
A Closer Look at the Learnability of Out-of-Distribution (OOD) Detection
Monotonic Learning in the PAC Framework: A New Perspective
Ensuring superior learning outcomes and data security for authorized learner
Of Dice and Games: A Theory of Generalized Boosting
Implicit High-Order Moment Tensor Estimation and Learning Latent Variable Models
Effective Littlestone Dimension
Probably Approximately Precision and Recall Learning
Learning multivariate Gaussians with imperfect advice
Reliable Learning of Halfspaces under Gaussian Marginals
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