PAC learning

6 papers with code • 0 benchmarks • 0 datasets

Probably Approximately Correct (PAC) learning analyzes machine learning mathematically using probability bounds.

Most implemented papers

Introduction to Machine Learning: Class Notes 67577

carsonzhu/Machine-learning-oriented 23 Apr 2009

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

omerbp/Regression-Equilibrium 4 May 2019

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

pariard/SLIP NeurIPS 2020

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

braqiiit/qrealboost 25 Oct 2021

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

louzounlab/pygon 5 Jan 2022

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 2 May 2022

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