Riikka Huusari

Data scientist at Readpeak (Finland)

My research as post-doctoral researcher at Aalto University involves (scalable) structured prediction approaches for drug combination response prediction. My previous research has included feature selection and interpretability, and especially during my thesis at Aix-Marseille University also kernel methods, considering both the traditional scalar-valued kernels and the more general operator-valued kernels.

Most up-to-date list can be found from Google scholar

Preprints:

• Riikka Huusari, Tianduanyi Wang, Sandor Szedmak, Diogo Dias, Tero Aittokallio and Juho Rousu: Scaling up drug combination surface predictions, December 2024. (link to paper, Python code)

• [In press] Riikka Huusari, Tianduanyi Wang, Sandor Szedmak, Tero Aittokallio and Juho Rousu: Predicting drug combination response surfaces, April 2024. (link to paper, Python code)

• Riikka Huusari, Sahely Bhadra, Cécile Capponi, Hachem Kadri and Juho Rousu: Learning primal-dual sparse kernel machines, August 2021. (link to paper, Python code)

Book chapters and journal papers:

• Sandor Szedmak, Riikka Huusari, Tat Hong Duong Le and Juho Rousu: Scalable variable selection for two-view learning tasks with projection operators in Machine Learning, Volume 113, 2024. (link to paper)

• Riikka Huusari, Cécile Capponi, Paul Villoutreix and Hachem Kadri: Cross-View Kernel Transfer in Pattern Recognition, Volume 129, May 2022. (link to paper, BibTeX, Python code)

• Riikka Huusari and Hachem Kadri: Entangled Kernels - Beyond Separability in Journal of Machine Learning Research, volume 22, p.1-40, Springer, January 2021 (PDF, BibTeX, Python code)

• Riikka Huusari, Hachem Kadri and Cécile Capponi: General Framework for Multi-view Metric Learning in Linking and Mining Heterogeneous and Multi-view Data, chapter 11, p.265-294, Springer, January 2019

Publications in international conferences:

• Hachem Kadri, Stéphane Ayache, Riikka Huusari, Alain Rakotomamonjy and Liva Ralaivola: Partial Trace Regression and Low-Rank Kraus Decomposition in 37th International Conference on Machine Learning (ICML), Vienna, Austria, July 2020. (PDF, Python code)

• Riikka Huusari and Hachem Kadri: Entangled Kernels in 28th International Joint Conference on Artificial Intelligence (IJCAI), Macao, China, August 2019. (PDF, BibTeX, Python code, poster, presentation)

• Riikka Huusari, Hachem Kadri and Cécile Capponi: Multi-View Metric Learning in Vector-Valued Kernel Spaces in The 21st International Conference on Artificial Intelligence and Statistics (AISTATS), Lanzarote, Spain, April 2018. (PDF, BibTeX, Python code, Poster)

Publications in French conferences:

• Riikka Huusari, Cécile Capponi, Hachem Kadri and Paul Villoutreix: Apprentissage multi-vues pour la complétion transmodale de matrices de noyaux in Conférence sur l'Apprentissage automatique (CAp), Toulouse, France, July 2019

• Riikka Huusari and Hachem Kadri: Intrication et noyaux à valeurs opérateurs in Conférence sur l'Apprentissage automatique (CAp), Rouen, France, June 2018

• Riikka Huusari, Hachem Kadri and Cécile Capponi: Support Vector Machine Framework for Multi-View Metric Learning. in 50^e Journées de Statistique de la Société Française de Statistique, Paris, France, May-June 2018

• Riikka Huusari, Hachem Kadri and Cécile Capponi: Noyaux à valeurs opérateurs et apprentissage de métriques multi-vues. in Conférence sur l'Apprentissage automatique (CAp), Grenoble, France, June 2017

Workshop publications:

• Riikka Huusari, Cécile Capponi, Hachem Kadri and Paul Villoutreix: Cross-view kernel transfer in Data and Machine Learning Advances with Multiple Views (DAMVL) Workshop of of ECMLPKDD 2019, Würzburg, Germany, September 2019

Thesis:

Kernel learning for structured data: A study on learning operator- and scalar-valued kernels for multi-view and multi-task learning problems November 2019. (link)