# 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

A central goal in the cognitive sciences is the development of numerical models for mental representations of object concepts.