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PAC learning

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

Papers

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Showing 151–200 of 289 papers

TitleStatusHype
Monotone Learning—0
Monotonic Learning in the PAC Framework: A New Perspective—0
More data speeds up training time in learning halfspaces over sparse vectors—0
Multiclass Boosting: Simple and Intuitive Weak Learning Criteria—0
Multiclass versus Binary Differentially Private PAC Learning—0
Multi-group Agnostic PAC Learnability—0
Multi-label Learning for Large Text Corpora using Latent Variable Model with Provable Gurantees—0
Multi-step learning and underlying structure in statistical models—0
Numerical and statistical analysis of NeuralODE with Runge-Kutta time integration—0
Of Dice and Games: A Theory of Generalized Boosting—0
On Agnostic PAC Learning in the Small Error Regime—0
On Agnostic PAC Learning using L_2-polynomial Regression and Fourier-based Algorithms—0
On computable learning of continuous features—0
On Fundamental Limits of Robust Learning—0
On Generalization Bounds of a Family of Recurrent Neural Networks—0
On Learnability with Computable Learners—0
On Learning and Enforcing Latent Assessment Models using Binary Feedback from Human Auditors Regarding Black-Box Classifiers—0
Online Learning and Disambiguations of Partial Concept Classes—0
Online Learning of k-CNF Boolean Functions—0
On PAC Learning Halfspaces in Non-interactive Local Privacy Model with Public Unlabeled Data—0
On the Complexity of Learning from Label Proportions—0
On the complexity of PAC learning in Hilbert spaces—0
On the Computability of Multiclass PAC Learning—0
On the Computability of Robust PAC Learning—0
On the Computational Landscape of Replicable Learning—0
On the Hardness of PAC-learning Stabilizer States with Noise—0
On the Learnability of Out-of-distribution Detection—0
On the Power of Differentiable Learning versus PAC and SQ Learning—0
On the Power of Interactive Proofs for Learning—0
On the Power of Learning from k-Wise Queries—0
On the Role of Entanglement and Statistics in Learning—0
On the Role of Noise in the Sample Complexity of Learning Recurrent Neural Networks: Exponential Gaps for Long Sequences—0
On the Sample Complexity of Adversarial Multi-Source PAC Learning—0
On the Sample Complexity of Learning Sum-Product Networks—0
Sample Efficient Algorithms for Learning Quantum Channels in PAC Model and the Approximate State Discrimination Problem—0
Optimal Bounds on Approximation of Submodular and XOS Functions by Juntas—0
Optimal Learners for Realizable Regression: PAC Learning and Online Learning—0
Optimal lower bounds for Quantum Learning via Information Theory—0
Order-Revealing Encryption and the Hardness of Private Learning—0
Overview of AdaBoost : Reconciling its views to better understand its dynamics—0
PAC Generalization via Invariant Representations—0
PAC learning and stabilizing Hedonic Games: towards a unifying approach—0
PAC Learning-Based Verification and Model Synthesis—0
PAC-Learning for Strategic Classification—0
PAC-learning gains of Turing machines over circuits and neural networks—0
PAC Learning Guarantees Under Covariate Shift—0
PAC-learning in the presence of adversaries—0
PAC-learning in the presence of evasion adversaries—0
PAC Learning is just Bipartite Matching (Sort of)—0
PAC-learning is Undecidable—0
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