Publications
Submitted papers
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L. Filstroff, I. Sundin, P. Mikkola, A. Tiulpin, J. Kylmäoja, and S. Kaski
“Targeted Active Learning for Bayesian Decision-Making”
Submitted, May 2021.
arXiv -
D. Huang, L. Filstroff, P. Mikkola, R. Zhang, and S. Kaski
“Bayesian Optimization Augmented with Actively Elicited Expert Knowledge”
Submitted, Feb. 2022.
arXiv
International journal papers
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A. Lumbreras, L. Filstroff, and C. Févotte
“Bayesian Mean-parameterized Nonnegative Binary Matrix Factorization”
Data Mining and Knowledge Discovery, vol. 34, no. 6, 2020.
pdf, code, doi -
L. Filstroff, O. Gouvert, C. Févotte, and O. Cappé
“A Comparative Study of Gamma Markov Chains for Temporal Non-Negative Matrix Factorization”
IEEE Transactions on Signal Processing, vol. 69, 2021.
pdf, code, doi
International conference papers
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L. Filstroff, A. Lumbreras, and C. Févotte
“Closed-form Marginal Likelihood in Gamma-Poisson matrix factorization”
International Conference on Machine Learning (ICML), 2018.
pdf, code -
T. Taburet, L. Filstroff, P. Bas, and W. Sawaya
“An Empirical Study of Steganography and Steganalysis of Color Images in the JPEG Domain”
International Workshop on Digital Forensics and Watermarking (IWDW), 2018.
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R. Xia, V.Y.F. Tan, L. Filstroff, and C. Févotte
“A Ranking Model Motivated by Nonnegative Matrix Factorization with Applications to Tennis Tournaments”
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2019.
pdf, code -
A. Bharti, L. Filstroff, and S. Kaski
“Approximate Bayesian Computation with Domain Expert in the Loop”
International Conference on Machine Learning (ICML), 2022.
pdf, code -
P. Mikkola, J. Martinelli, L. Filstroff, and S. Kaski
“Multi-Fidelity Bayesian Optimization with Unreliable Information Sources”
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023.
pdf, code
Workshop papers
- A. Tiihonen, L. Filstroff, P. Mikkola, E. Lehto, S. Kaski, M. Todorović, and P. Rinke
“More trustworthy Bayesian optimization of materials properties by adding humans into the loop”
AI for Accelerated Materials Design NeurIPS 2022 Workshop
pdf
Theses
- L. Filstroff
“Contributions to probabilistic non-negative matrix factorization - Maximum marginal likelihood estimation and Markovian temporal models”
Ph.D. Thesis, Institut National Polytechnique de Toulouse, 2019.
pdf