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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
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
Learning versus Refutation in Noninteractive Local Differential Privacy—0
Is Out-of-Distribution Detection Learnable?—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
VICE: Variational Interpretable Concept EmbeddingsCode1
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
Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks—0
Monotone Learning—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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