Juho Rousu — accepted AI-conference papers

2024

MassSpecGym: A benchmark for the discovery and identification of molecules

Roman Bushuiev, Anton Bushuiev, Niek F. de Jonge, Adamo Young, Fleming Kretschmer, Raman Samusevich +24

NeurIPS 2024spotlight

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure…

Cited by 4 Source ↗PDF ↗code ↗Scholar ↗

2022

Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters

Luc Brogat-Motte, Rémi Flamary, Celine Brouard, Juho Rousu, Florence D’Alché-Buc

ICML 2022spotlight

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 problem as regression with the Fused Gromov-Wasserstein (FGW) loss and propose a predictive model relying on a FGW barycenter…

Cited by 37 Source ↗PDF ↗code ↗Scholar ↗

2019

Large-Scale Sparse Kernel Canonical Correlation Analysis

Viivi Uurtio, Sahely Bhadra, Juho Rousu

ICML 2019oral

This paper presents gradKCCA, a large-scale sparse non-linear canonical correlation method. Like Kernel Canonical Correlation Analysis (KCCA), our method finds non-linear relations through kernel functions, but it does not rely on a kernel matrix, a known bottleneck for scaling up kernel methods. gr…

Cited by 50 Source ↗PDF ↗Scholar ↗

2016

Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo

Markus Heinonen, Henrik Mannerström, Juho Rousu, Samuel Kaski, Harri Lähdesmäki

AISTATS 2016poster

We present a novel approach for non-stationary Gaussian process regression (GPR), where the three key parameters – noise variance, signal variance and lengthscale – can be simultaneously input-dependent. We develop gradient-based inference methods to learn the unknown function and the non-stationary…

Cited by 154 Source ↗PDF ↗code ↗Scholar ↗