# PAC learning

9 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

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.