PAC learning
10 papers with code • 0 benchmarks • 0 datasets
Probably Approximately Correct (PAC) learning analyzes machine learning mathematically using probability bounds.
Benchmarks
These leaderboards are used to track progress in PAC learning
Most implemented papers
Understanding Boolean Function Learnability on Deep Neural Networks: PAC Learning Meets Neurosymbolic Models
Computational learning theory states that many classes of boolean formulas are learnable in polynomial time.
Introduction to Machine Learning: Class Notes 67577
Introduction to Machine learning covering Statistical Inference (Bayes, EM, ML/MaxEnt duality), algebraic and spectral methods (PCA, LDA, CCA, Clustering), and PAC learning (the Formal model, VC dimension, Double Sampling theorem).
Regression Equilibrium
Despite their centrality in the competition between online companies who offer prediction-based products, the \textit{strategic} use of prediction algorithms remains unexplored.
SLIP: Learning to Predict in Unknown Dynamical Systems with Long-Term Memory
Our theoretical and experimental results shed light on the conditions required for efficient probably approximately correct (PAC) learning of the Kalman filter from partially observed data.
Quantum Boosting using Domain-Partitioning Hypotheses
Izdebski et al. posed an open question on whether we can boost quantum weak learners that output non-binary hypothesis.
Planted Dense Subgraphs in Dense Random Graphs Can Be Recovered using Graph-based Machine Learning
We show that PYGON can recover cliques of sizes $\Theta\left(\sqrt{n}\right)$, where $n$ is the size of the background graph, comparable with the state of the art.
VICE: Variational Interpretable Concept Embeddings
lukasmut/vice
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This paper introduces Variational Interpretable Concept Embeddings (VICE), an approximate Bayesian method for embedding object concepts in a vector space using data collected from humans in a triplet odd-one-out task.
Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean Estimation
We establish a simple connection between robust and differentially-private algorithms: private mechanisms which perform well with very high probability are automatically robust in the sense that they retain accuracy even if a constant fraction of the samples they receive are adversarially corrupted.
SAT-Based PAC Learning of Description Logic Concepts
We propose bounded fitting as a scheme for learning description logic concepts in the presence of ontologies.
Towards a theory of model distillation
eboix/theory-of-model-distillation
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Distillation is the task of replacing a complicated machine learning model with a simpler model that approximates the original [BCNM06, HVD15].
