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PAC learning

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

Papers

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Showing 101–150 of 289 papers

TitleStatusHype
On the Role of Noise in the Sample Complexity of Learning Recurrent Neural Networks: Exponential Gaps for Long Sequences—0
Policy Synthesis and Reinforcement Learning for Discounted LTL—0
SAT-Based PAC Learning of Description Logic ConceptsCode0
A Parameterized Theory of PAC Learning—0
Probably Approximately Correct Federated Learning—0
Online Learning and Disambiguations of Partial Concept Classes—0
Lifting uniform learners via distributional decomposition—0
Stability is Stable: Connections between Replicability, Privacy, and Adaptive Generalization—0
Agnostic PAC Learning of k-juntas Using L2-Polynomial Regression—0
On the complexity of PAC learning in Hilbert spaces—0
Do PAC-Learners Learn the Marginal Distribution?—0
Tree Learning: Optimal Algorithms and Sample Complexity—0
Find a witness or shatter: the landscape of computable PAC learning—0
PAC learning and stabilizing Hedonic Games: towards a unifying approach—0
Optimal lower bounds for Quantum Learning via Information Theory—0
A Strongly Polynomial Algorithm for Approximate Forster Transforms and its Application to Halfspace Learning—0
Bagging is an Optimal PAC Learner—0
PAC Verification of Statistical Algorithms—0
Comparative Learning: A Sample Complexity Theory for Two Hypothesis Classes—0
On Proper Learnability between Average- and Worst-case Robustness—0
A Characterization of List Learnability—0
Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean EstimationCode0
Is Out-of-Distribution Detection Learnable?—0
Learning versus Refutation in Noninteractive Local Differential Privacy—0
SQ Lower Bounds for Learning Single Neurons with Massart Noise—0
Superpolynomial Lower Bounds for Decision Tree Learning and Testing—0
Unified Algorithms for RL with Decision-Estimation Coefficients: PAC, Reward-Free, Preference-Based Learning, and Beyond—0
Analyzing Robustness of Angluin's L* Algorithm in Presence of Noise—0
On PAC Learning Halfspaces in Non-interactive Local Privacy Model with Public Unlabeled Data—0
Fine-Grained Distribution-Dependent Learning Curves—0
Cryptographic Hardness of Learning Halfspaces with Massart Noise—0
Generalization Bounds for Data-Driven Numerical Linear Algebra—0
PAC Generalization via Invariant Representations—0
Bézier Flow: a Surface-wise Gradient Descent Method for Multi-objective Optimization—0
Sample Complexity Bounds for Robustly Learning Decision Lists against Evasion Attacks—0
Clifford Circuits can be Properly PAC Learned if and only if RP=NP—0
Active-learning-based non-intrusive Model Order Reduction—0
Metric Entropy Duality and the Sample Complexity of Outcome Indistinguishability—0
A Characterization of Multiclass Learnability—0
Adversarially Robust Learning with Tolerance—0
On Learning and Enforcing Latent Assessment Models using Binary Feedback from Human Auditors Regarding Black-Box Classifiers—0
A Characterization of Semi-Supervised Adversarially-Robust PAC Learnability—0
Monotone Learning—0
Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks—0
Planted Dense Subgraphs in Dense Random Graphs Can Be Recovered using Graph-based Machine LearningCode0
Efficiently Learning One Hidden Layer ReLU Networks From Queries—0
Exponential Separation between Two Learning Models and Adversarial Robustness—0
On computable learning of continuous features—0
PAC-Learning Uniform Ergodic Communicative Networks—0
Realizable Learning is All You Need—0
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