Motonobu Kanagawa

I am currently an Assistant Professor in the Data Science department at Eurecom since September 2019. I also hold a Chair at 3IA Côte d’Azur since 2021. Previously I was a research scientist at the Chair for the Methods of Machine Learning in the University of Tuebingen (Oct 2018 - Aug 2019), and at the Probabilistic Numerics Group in the Max Planck Institute for Intelligent Systems (Sep 2017 - Sep 2018), working with Prof. Philipp Hennig. Prior to this, I was a postdoc (Apr 2016 - Aug 2017) and a PhD. student (Apr 2013 - Mar 2016) at Institute of Statistical Mathematics, working with Prof. Kenji Fukumizu.


I am currently working on the following research topics:

- Kernel methods and Gaussian processes in machine learning

These are statistical learning methods that make use of positive definite kernels. See our recent review paper on the connections and equivalence between the two approaches.

- Machine learning for computer simulation

Machine learning methods, in particular those based on kernels and Gaussian processes, are very useful in enhancing the power of computer simulation. They enable automatic parameter tuning and model selection, as well as quantification of various kinds of uncertainties.

- Computer simulations for real world problems

Current projects include simulations for evacuation planning and emulations for tsunami early warning.


Contact Information

Eurecom, Data Science Department, Office 429

Campus SophiaTech, 450 Route des Chappes, 06410 Biot, France

Email: motonobu.kanagawa [CHAN] eurecom.fr (Please replace [CHAN] by @)


News

  • Our new paper ``Improved Random Features for Dot Product Kernels'' is now available on arXiv (Jan 2022).

  • We have written a new paper ``Intergenerational Risk Sharing in a Collective Defined-Contribution Pension System: A Simulation Study with Bayesian Optimization'' (June 2021).

  • Our paper "Counterfactual Mean Embeddings" has been accepted by JMLR! (June 2021)

  • A new preprint ``Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes'' is now available on arXiv (June 2021).

  • I have been awarded a new chair position at the 3IA Cote d'Azur (a French national research institute on AI) (June 2021).

  • We have significantly updated our preprint "Counterfactual Mean Embeddings", now containing several new results (Mar 2020).

  • We are organizing Workshop on Functional Inference and Machine Intelligence at EURECOM on 17 - 19 Feb 2020.

  • Our paper "Simulator Calibration under Covariate Shift with Kernels" has been accepted for publication at AISTATS 2020 (Jan 2020).

  • A new preprint "Simulator Calibration under Covariate Shift with Kernels" has now been available (Oct 2019).

  • Our paper ""Model-based Kernel Sum Rule: Kernel Bayesian Inference with Probabilistic Models" has been accepted for publication in Machine Learning (Oct 2019).

  • Our paper "Convergence Guarantees for Adaptive Bayesian Quadrature Methods" by Kanagawa and Hennig has been accepted for publication in NeurIPS 2019 (Sep 2019).

  • I have moved to Eurecom and started working as an Assistant Professor! (Sep 2019)

  • Our paper "On the positivity and magnitudes of Bayesian quadrature weights" by Karvonen, Kanagawa and Särkkä has been accepted for publication in Statistics and Computing (Aug 2018).

  • A new preprint "Convergence Guarantees for Adaptive Bayesian Quadrature Methods" by M. Kanagawa and P. Hennig (27 May 2019)

  • Our paper "Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings" by Kanagawa, Sriperumbudur and Fukumizu has just been accepted for publication in Foundations of Computational Mathematics (30 Oct 2018)

Academic Services

  • Editorial Board Member of Journal of Machine Learning Research (JMLR)

  • Reviewing service:

    • TPAMI, Machine Learning, Statistics and Computing, JMLR, etc.

    • AISTATS(2016,2019, 2020), ICML (2015,2017,2018,2019,2020), NeuIPS (2015,2016,2017,2018,2019,2020)


Antibes, September 2019

Tuebingen, April 2019