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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
Quantum Boosting using Domain-Partitioning HypothesesCode0
Active Learning for Contextual Search with Binary Feedbacks—0
Towards a theory of out-of-distribution learning—0
Learning the hypotheses space from data through a U-curve algorithm—0
Learning General Halfspaces with General Massart Noise under the Gaussian Distribution—0
Is Nash Equilibrium Approximator Learnable?—0
Statistically Near-Optimal Hypothesis Selection—0
On the Power of Differentiable Learning versus PAC and SQ Learning—0
Multiclass versus Binary Differentially Private PAC Learning—0
A Theory of PAC Learnability of Partial Concept Classes—0
Forster Decomposition and Learning Halfspaces with Noise—0
Semi-verified PAC Learning from the Crowd—0
Supervising the Transfer of Reasoning Patterns in VQA—0
Multi-group Agnostic PAC Learnability—0
Incentive-aware PAC learning—0
Learning and Certification under Instance-targeted Poisoning—0
Broadly Applicable Targeted Data Sample Omission Attacks—0
Symbolic Abstractions From Data: A PAC Learning Approach—0
PAC-learning gains of Turing machines over circuits and neural networks—0
Robust learning under clean-label attack—0
Private learning implies quantum stability—0
Fairness-Aware PAC Learning from Corrupted Data—0
Sample-Optimal PAC Learning of Halfspaces with Malicious Noise—0
On Agnostic PAC Learning using L_2-polynomial Regression and Fourier-based Algorithms—0
Adversarial Robustness: What fools you makes you stronger—0
On the Hardness of PAC-learning Stabilizer States with Noise—0
Adversarial Laws of Large Numbers and Optimal Regret in Online Classification—0
From Local Pseudorandom Generators to Hardness of Learning—0
The Price is (Probably) Right: Learning Market Equilibria from Samples—0
Communication-Aware Collaborative Learning—0
Near-Optimal Statistical Query Hardness of Learning Halfspaces with Massart Noise—0
Small Covers for Near-Zero Sets of Polynomials and Learning Latent Variable Models—0
VC Dimension and Distribution-Free Sample-Based Testing—0
Sample-efficient proper PAC learning with approximate differential privacy—0
PAC-Learning for Strategic Classification—0
Towards a Combinatorial Characterization of Bounded-Memory Learning—0
Efficient PAC Learning from the Crowd with Pairwise Comparisons—0
Reducing Adversarially Robust Learning to Non-Robust PAC Learning—0
Learning, compression, and leakage: Minimising classification error via meta-universal compression principles—0
SLIP: Learning to Predict in Unknown Dynamical Systems with Long-Term MemoryCode0
A Polynomial Time Algorithm for Learning Halfspaces with Tsybakov Noise—0
Understanding Boolean Function Learnability on Deep Neural Networks: PAC Learning Meets Neurosymbolic ModelsCode0
Learning from Mixtures of Private and Public Populations—0
A Computational Separation between Private Learning and Online Learning—0
Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks—0
An Optimal Elimination Algorithm for Learning a Best Arm—0
List Learning with Attribute Noise—0
Learning Halfspaces with Tsybakov Noise—0
Faster PAC Learning and Smaller Coresets via Smoothed Analysis—0
Probably Approximately Correct Constrained Learning—0
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