Kernel Mean Embeddings

Kernel mean embeddings (KMEs) provide a powerful way to represent probability distributions as points in a high-dimensional space, enabling comparisons and analysis of distributions using kernel methods. Current research focuses on improving KME's scalability and expressiveness through techniques like neural network integration and Nyström approximation, as well as extending their application to diverse fields such as optimal control, fairness in machine learning, and atomistic simulations. This approach offers advantages in handling complex data, reducing computational burdens, and improving model interpretability, leading to advancements in various scientific domains and practical applications.

Papers

October 30, 2024

An Overview of Causal Inference using Kernel Embeddings
Dino Sejdinovic
Causal Inference Causal Relationship Causal Effect Estimation Kernel Hilbert Space Kernel Mean Embeddings

October 11, 2024

Deterministic Fokker-Planck Transport -- With Applications to Sampling, Variational Inference, Kernel Mean Embeddings & Sequential Monte Carlo
Ilja Klebanov
Variational Inference Open Sampling Kernel Density Sequential Monte Carlo Fokker Planck Equation Probability Flow ODE Kernel Mean Embeddings Particle Flow

July 23, 2024

Data-Driven Optimal Feedback Laws via Kernel Mean Embeddings
Petar Bevanda, Nicolas Hoischen, Stefan Sosnowski, Sandra Hirche, Boris Houska
Data Driven Optimal Control Kernel Hilbert Space Kernel Mean Embeddings Hamilton Jacobi Bellman Stochastic Diffusion

June 10, 2024

On the Consistency of Kernel Methods with Dependent Observations
Pierre-François Massiani, Sebastian Trimpe, Friedrich Solowjow
Strong Consistency Kernel Method Statistical Learning Linear Convergence Dependent Data Kernel Mean Embeddings

March 16, 2024

Neural-Kernel Conditional Mean Embeddings
Eiki Shimizu, Kenji Fukumizu, Dino Sejdinovic
Distributional Reinforcement Learning Hyper Parameter Kernel Based Conditional Density Kernel Mean Embeddings Mean Embeddings

June 11, 2023

Toward Fair Facial Expression Recognition with Improved Distribution Alignment
Mojtaba Kolahdouzi, Ali Etemad
Face Recognition Expression Recognition Adversarial Loss Distribution Alignment Sensitive Attribute Kernel Mean Embeddings

June 2, 2023

Transfer learning for atomistic simulations using GNNs and kernel mean embeddings
John Falk, Luigi Bonati, Pietro Novelli, Michele Parrinello, Massimiliano Pontil
Graph Neural Network Formality Transfer Interatomic Potential Atomistic Simulation Chemical Data Potential Energy Surface Kernel Mean Embeddings

May 10, 2023

Supervised learning with probabilistic morphisms and kernel mean embeddings
Hông Vân Lê
Supervised Learning Learning Algorithm Parameterized Model Statistical Learning Theory Kernel Mean Embeddings

February 12, 2023

Recursive Estimation of Conditional Kernel Mean Embeddings
Ambrus Tamás, Balázs Csanád Csáji
Kernel Hilbert Space Hilbert Space Kernel Mean Embeddings Recursive Bayesian

January 29, 2023

Kernelized Cumulants: Beyond Kernel Mean Embeddings
Patric Bonnier, Harald Oberhauser, Zoltán Szabó
Kernel Hilbert Space Hilbert Schmidt Independence Criterion Kernel Mean Embeddings Order Cumulants Kernel Trick Kernel Sum

January 9, 2023

Physics-Informed Kernel Embeddings: Integrating Prior System Knowledge with Data-Driven Control
Adam J. Thorpe, Cyrus Neary, Franck Djeumou, Meeko M. K. Oishi, Ufuk Topcu
Data Driven Kernel Mean Embeddings Nonholonomic System

October 13, 2022

Variance-Aware Estimation of Kernel Mean Embedding
Geoffrey Wolfer, Pierre Alquier
Robust Estimation Kernel Extension Kernel Mean Embeddings Variance Estimation

July 28, 2022

Self-supervised learning with rotation-invariant kernels
Léon Zheng, Gilles Puy, Elisa Riccietti, Patrick Pérez, Rémi Gribonval
Self Supervised Regularization Loss Kernel Mean Embeddings Dot Product Kernel Invariant Kernel

February 11, 2022

Conditional Contrastive Learning with Kernel
Yao-Hung Hubert Tsai, Tianqin Li, Martin Q. Ma, Han Zhao, Kun Zhang, Louis-Philippe Morency, Ruslan Salakhutdinov
Contrastive Learning Kernel Extension Contrastive Objective Kernel Mean Embeddings Conditional Contrastive Learning

February 8, 2022

February 1, 2022

Deep Layer-wise Networks Have Closed-Form Weights
Chieh Wu, Aria Masoomi, Arthur Gretton, Jennifer Dy
Back Propagation Layer Wise Kernel Mean Embeddings Novel Kernel

January 31, 2022

Nystr\"om Kernel Mean Embeddings
Antoine Chatalic, Nicolas Schreuder, Alessandro Rudi, Lorenzo Rosasco
Jina Embeddings Maximum Mean Discrepancy Efficient Approximation Kernel Mean Embeddings Nystr\"om Approximation

January 18, 2022

Transparent Single-Cell Set Classification with Kernel Mean Embeddings
Siyuan Shan, Vishal Baskaran, Haidong Yi, Jolene Ranek, Natalie Stanley, Junier Oliva
Cell Representation Cell Type Cell Population Kernel Mean Embeddings Human Interpretability