69 citations · 1.1k across the 120 of their papers we have counts for
6 papers · 2 filters
ELFI: Engine for Likelihood-Free Inference
Jarno Lintusaari, Henri Vuollekoski, Antti Kangasrääsiö +7
Engine for Likelihood-Free Inference (ELFI) is a Python software library for performing likelihood-free inference (LFI). ELFI provides a convenient syntax for arranging components…
Non-Stationary Spectral Kernels
Sami Remes, Markus Heinonen, Samuel Kaski
We propose non-stationary spectral kernels for Gaussian process regression. We propose to model the spectral density of a non-stationary kernel function as a mixture of input-depen…
Differentially Private Bayesian Learning on Distributed Data
Mikko Heikkilä, Eemil Lagerspetz, Samuel Kaski +3
Many applications of machine learning, for example in health care, would benefit from methods that can guarantee privacy of data subjects. Differential privacy (DP) has become esta…
Distributed Bayesian Matrix Factorization with Limited Communication
Xiangju Qin, Paul Blomstedt, Eemeli Leppäaho +2
Bayesian matrix factorization (BMF) is a powerful tool for producing low-rank representations of matrices and for predicting missing values and providing confidence intervals. Scal…
Interpreting Outliers: Localized Logistic Regression for Density Ratio Estimation
Makoto Yamada, Song Liu, Samuel Kaski
We propose an inlier-based outlier detection method capable of both identifying the outliers and explaining why they are outliers, by identifying the outlier-specific features. Spe…
A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings
Sami Remes, Markus Heinonen, Samuel Kaski
We introduce a novel kernel that models input-dependent couplings across multiple latent processes. The pairwise joint kernel measures covariance along inputs and across different…