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PAC learning

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

Papers

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Showing 251–289 of 289 papers

TitleStatusHype
Screw Geometry Meets Bandits: Incremental Acquisition of Demonstrations to Generate Manipulation Plans—0
Semi-verified PAC Learning from the Crowd—0
Sequential Mode Estimation with Oracle Queries—0
Simple and Fast Algorithms for Interactive Machine Learning with Random Counter-examples—0
Simplifying Adversarially Robust PAC Learning with Tolerance—0
Simultaneous Private Learning of Multiple Concepts—0
Small Covers for Near-Zero Sets of Polynomials and Learning Latent Variable Models—0
SQ Lower Bounds for Learning Single Neurons with Massart Noise—0
Stability is Stable: Connections between Replicability, Privacy, and Adaptive Generalization—0
Statistically Near-Optimal Hypothesis Selection—0
Strategic Classification With Externalities—0
Superconstant Inapproximability of Decision Tree Learning—0
Super Non-singular Decompositions of Polynomials and their Application to Robustly Learning Low-degree PTFs—0
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
Planted Dense Subgraphs in Dense Random Graphs Can Be Recovered using Graph-based Machine LearningCode0
Quantum Boosting using Domain-Partitioning HypothesesCode0
Regression EquilibriumCode0
Introduction to Machine Learning: Class Notes 67577Code0
Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean EstimationCode0
Towards a theory of model distillationCode0
SLIP: Learning to Predict in Unknown Dynamical Systems with Long-Term MemoryCode0
Understanding Boolean Function Learnability on Deep Neural Networks: PAC Learning Meets Neurosymbolic ModelsCode0
Optimistic Rates for Learning from Label ProportionsCode0
SAT-Based PAC Learning of Description Logic ConceptsCode0
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