no code implementations • 23 May 2023 • Julien Martinelli, Ayush Bharti, Armi Tiihonen, S. T. John, Louis Filstroff, Sabina J. Sloman, Patrick Rinke, Samuel Kaski
Contextual Bayesian Optimization (CBO) efficiently optimizes black-box functions with respect to design variables, while simultaneously integrating contextual information regarding the environment, such as experimental conditions.
1 code implementation • 25 Oct 2022 • Petrus Mikkola, Julien Martinelli, Louis Filstroff, Samuel Kaski
Over the past decade, many algorithms have been proposed to integrate cheaper, lower-fidelity approximations of the objective function into the optimization process, with the goal of converging towards the global optimum at a reduced cost.
no code implementations • 18 Aug 2022 • Daolang Huang, Louis Filstroff, Petrus Mikkola, Runkai Zheng, Samuel Kaski
We design a multi-task learning architecture for this task, with the goal of jointly eliciting the expert knowledge and minimizing the objective function.
1 code implementation • 28 Jan 2022 • Ayush Bharti, Louis Filstroff, Samuel Kaski
Approximate Bayesian computation (ABC) is a popular likelihood-free inference method for models with intractable likelihood functions.
no code implementations • 8 Jun 2021 • Louis Filstroff, Iiris Sundin, Petrus Mikkola, Aleksei Tiulpin, Juuso Kylmäoja, Samuel Kaski
Active learning is usually applied to acquire labels of informative data points in supervised learning, to maximize accuracy in a sample-efficient way.
1 code implementation • 23 Jun 2020 • Louis Filstroff, Olivier Gouvert, Cédric Févotte, Olivier Cappé
Non-negative matrix factorization (NMF) has become a well-established class of methods for the analysis of non-negative data.
no code implementations • 15 Mar 2019 • Rui Xia, Vincent Y. F. Tan, Louis Filstroff, Cédric Févotte
We propose a novel ranking model that combines the Bradley-Terry-Luce probability model with a nonnegative matrix factorization framework to model and uncover the presence of latent variables that influence the performance of top tennis players.
1 code implementation • 17 Dec 2018 • Alberto Lumbreras, Louis Filstroff, Cédric Févotte
In some cases, the analysis of binary matrices can be tackled with nonnegative matrix factorization (NMF), where the observed data matrix is approximated by the product of two smaller nonnegative matrices.
no code implementations • ICML 2018 • Louis Filstroff, Alberto Lumbreras, Cédric Févotte
We present novel understandings of the Gamma-Poisson (GaP) model, a probabilistic matrix factorization model for count data.