Learning Theory for Distribution Regression

8 November 2014

Papers citing "Learning Theory for Distribution Regression"

10 / 10 papers shown

Title
Kernel Quantile Embeddings and Associated Probability Metrics Masha Naslidnyk Siu Lun Chau F. Briol Krikamol Muandet 59 0 0 26 May 2025
Integral Imprecise Probability Metrics Siu Lun Chau Michele Caprio Krikamol Muandet 61 0 0 22 May 2025
Regularized least squares learning with heavy-tailed noise is minimax optimal Mattes Mollenhauer Nicole Mücke Dimitri Meunier Arthur Gretton 47 0 0 20 May 2025
Bayesian Density-Density Regression with Application to Cell-Cell Communications Khai Nguyen Yang Ni Peter Mueller 47 0 0 17 Apr 2025
Improved learning theory for kernel distribution regression with two-stage sampling François Bachoc Louis Bethune Alberto González Sanz Jean-Michel Loubes 115 2 0 28 Jan 2025
Demystifying amortized causal discovery with transformers Francesco Montagna Max Cairney-Leeming Dhanya Sridhar Francesco Locatello CML 94 1 0 27 May 2024
k-NN Regression Adapts to Local Intrinsic Dimension Samory Kpotufe 252 128 0 19 Oct 2011
Kernels for Vector-Valued Functions: a Review Mauricio A. Alvarez Lorenzo Rosasco Neil D. Lawrence GP 154 918 0 30 Jun 2011
Hilbert space embeddings and metrics on probability measures Bharath K. Sriperumbudur Arthur Gretton Kenji Fukumizu Bernhard Schölkopf Gert R. G. Lanckriet 158 741 0 30 Jul 2009
Multi-Instance Learning by Treating Instances As Non-I.I.D. Samples Zhi Zhou Yu-Yin Sun Yu-Feng Li 110 476 0 12 Jul 2008