PAC learning
6 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
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
Freund and Schapire gave the first classical boosting algorithm for binary hypothesis known as AdaBoost, and this was recently adapted into a quantum boosting algorithm by Arunachalam et al. Their quantum boosting algorithm (which we refer to as Q-AdaBoost) is quadratically faster than the classical version in terms of the VC-dimension of the hypothesis class of the weak learner but polynomially worse in the bias of the weak learner.
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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A central goal in the cognitive sciences is the development of numerical models for mental representations of object concepts.
