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PAC learning

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

Papers

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Showing 1–50 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
Towards Understanding Multi-Round Large Language Model Reasoning: Approximability, Learnability and Generalizability—0
A Linear Theory of Multi-Winner Voting—0
PAC Learning with Improvements—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
The working principles of model-based GAs fall within the PAC framework: A mathematical theory of problem decomposition—0
A Near-optimal Algorithm for Learning Margin Halfspaces with Massart Noise—0
Identifying Information from Observations with Uncertainty and Novelty—0
Learning Noisy Halfspaces with a Margin: Massart is No Harder than Random—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
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
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