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PAC learning

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

Papers

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Showing 201–250 of 289 papers

TitleStatusHype
PAC Learning Linear Thresholds from Label Proportions—0
PAC-Learning Uniform Ergodic Communicative Networks—0
PAC Learning, VC Dimension, and the Arithmetic Hierarchy—0
PAC Learning with Improvements—0
PAC learning with nasty noise—0
PAC learning with stable and private predictions—0
PAC Verification of Statistical Algorithms—0
Policy Synthesis and Reinforcement Learning for Discounted LTL—0
Predicting with Distributions—0
Predictive PAC Learning and Process Decompositions—0
Privacy-preserving Prediction—0
Private Hypothesis Selection—0
Private learning implies quantum stability—0
Private PAC learning implies finite Littlestone dimension—0
Private PAC Learning May be Harder than Online Learning—0
On Proper Learnability between Average- and Worst-case Robustness—0
Probably Approximately Correct Constrained Learning—0
Probably approximately correct high-dimensional causal effect estimation given a valid adjustment set—0
Probably Approximately Precision and Recall Learning—0
Proper Learning, Helly Number, and an Optimal SVM Bound—0
Proper vs Improper Quantum PAC learning—0
Provable learning of quantum states with graphical models—0
Quantum hardness of learning shallow classical circuits—0
Quantum statistical query learning—0
Query-driven PAC-Learning for Reasoning—0
Ramsey Theorems for Trees and a General 'Private Learning Implies Online Learning' Theorem—0
Realizable Learning is All You Need—0
Reducing Adversarially Robust Learning to Non-Robust PAC Learning—0
Reliable Learning of Halfspaces under Gaussian Marginals—0
Representation, Approximation and Learning of Submodular Functions Using Low-rank Decision Trees—0
Revisiting Agnostic PAC Learning—0
Robust learning under clean-label attack—0
Sample Complexity Bounds for Robustly Learning Decision Lists against Evasion Attacks—0
Sample Complexity Bounds on Differentially Private Learning via Communication Complexity—0
Sample-Efficient Learning of Mixtures—0
Sample-efficient proper PAC learning with approximate differential privacy—0
Sample-Optimal PAC Learning of Halfspaces with Malicious Noise—0
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
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