kernel-methods

A collection of important graph embedding, classification and representation learning papers with implementations.

Learning kernels to maximize the power of MMD tests

Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020)

Large-scale, multi-GPU capable, kernel solver

A package for Multiple Kernel Learning in Python

ML4Chem: Machine Learning for Chemistry and Materials

[IEEE TCYB 2022] Unsupervised Change Detection in Multitemporal VHR Images Based on Deep Kernel PCA Convolutional Mapping Network

Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326

NeurIPS 2017 best paper. An interpretable linear-time kernel goodness-of-fit test.

Multivariate Local Polynomial Regression and Radial Basis Function Regression

Toolkit for training quantum kernels in machine learning applications

Foundational library for Kernel methods in pattern analysis and machine learning

Contains the code (and working vm setup) for our KDD MLG 2016 paper titled: "subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs"

"GRAIL: Efficient Time-Series Representation Learning"

Koopman Kernels for Learning Dynamical Systems from Trajectory Data

Neo LS-SVM is a modern Least-Squares Support Vector Machine implementation

The code for Principal Component Analysis (PCA), dual PCA, Kernel PCA, Supervised PCA (SPCA), dual SPCA, and Kernel SPCA

Neural Tangent Kernel (NTK) module for the scikit-learn library

This repository contains the TensorFlow implemtation of subgraph2vec (KDD MLG 2016) paper

Causal Modeling with Stationary Diffusions, AISTATS 2024