100 citations · 193 across the 10 of their papers we have counts for
11 papers
Towards data-driven mass spectrometry in atmospheric science
Hilda Sandström, Matti Rissanen, Juho Rousu +1
Aerosols found in the atmosphere affect the climate and worsen air quality. To mitigate these adverse impacts, aerosol formation and aerosol chemistry in the atmosphere need to be…
Vector-Valued Least-Squares Regression under Output Regularity Assumptions
Luc Brogat-Motte, Alessandro Rudi, Céline Brouard +2
We propose and analyse a reduced-rank method for solving least-squares regression problems with infinite dimensional output. We derive learning bounds for our method, and study und…
Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters
Luc Brogat-Motte, Rémi Flamary, Céline Brouard +2
This paper introduces a novel and generic framework to solve the flagship task of supervised labeled graph prediction by leveraging Optimal Transport tools. We formulate the proble…
Learning primal-dual sparse kernel machines
Riikka Huusari, Sahely Bhadra, Cécile Capponi +2
Traditionally, kernel methods rely on the representer theorem which states that the solution to a learning problem is obtained as a linear combination of the data mapped into the r…
Learning Output Embeddings in Structured Prediction
Luc Brogat-Motte, Alessandro Rudi, Céline Brouard +2
A powerful and flexible approach to structured prediction consists in embedding the structured objects to be predicted into a feature space of possibly infinite dimension by means…
A Solution for Large Scale Nonlinear Regression with High Rank and Degree at Constant Memory Complexity via Latent Tensor Reconstruction
Sandor Szedmak, Anna Cichonska, Heli Julkunen +2
This paper proposes a novel method for learning highly nonlinear, multivariate functions from examples. Our method takes advantage of the property that continuous functions can be…
Bayesian Metabolic Flux Analysis reveals intracellular flux couplings
Markus Heinonen, Maria Osmala, Henrik Mannerström +4
Metabolic flux balance analyses are a standard tool in analysing metabolic reaction rates compatible with measurements, steady-state and the metabolic reaction network stoichiometr…
A Tutorial on Canonical Correlation Methods
Viivi Uurtio, João M. Monteiro, Jaz Kandola +3
Canonical correlation analysis is a family of multivariate statistical methods for the analysis of paired sets of variables. Since its proposition, canonical correlation analysis h…
Multi-view Kernel Completion
Sahely Bhadra, Samuel Kaski, Juho Rousu
In this paper, we introduce the first method that (1) can complete kernel matrices with completely missing rows and columns as opposed to individual missing kernel values, (2) does…
Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo
Markus Heinonen, Henrik Mannerström, Juho Rousu +2
We present a novel approach for fully non-stationary Gaussian process regression (GPR), where all three key parameters -- noise variance, signal variance and lengthscale -- can be…
Multilabel Classification through Random Graph Ensembles
Hongyu Su, Juho Rousu
We present new methods for multilabel classification, relying on ensemble learning on a collection of random output graphs imposed on the multilabel and a kernel-based structured o…