Louis Filstroff

Louis Filstroff

Enseignant-chercheur

Publications

Google scholar profile

Submitted papers

  1. L. Filstroff, I. Sundin, P. Mikkola, A. Tiulpin, J. Kylmäoja, and S. Kaski
    “Targeted Active Learning for Bayesian Decision-Making”
    Submitted, May 2021.
    arXiv

  2. D. Huang, L. Filstroff, P. Mikkola, R. Zhang, and S. Kaski
    “Bayesian Optimization Augmented with Actively Elicited Expert Knowledge”
    Submitted, Feb. 2022.
    arXiv
    (There is also a workshop version of this work, see below)

International journal papers

  1. 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

  2. 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

  1. 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

  2. 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.
    pdf

  3. 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

  4. 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

  5. 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

  6. J. Martinelli, A. Bharti, A. Tiihonen, S. T. John, L. Filstroff, S. J. Sloman, P. Rinke, and S. Kaski
    “Learning Relevant Contextual Variables Within Bayesian Optimization”
    Conference on Uncertainty in Artificial Intelligence (UAI), 2024.
    pdf, code coming soon
    (There is also a workshop version of this work, see below)

Workshop papers

  1. 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

  2. D. Huang, L. Filstroff, P. Mikkola, R. Zheng, M. Todorović, and S. Kaski
    “Augmenting Bayesian Optimization with Preference-based Expert Feedback”
    The Many Facets of Preference Learning ICML 2023 Workshop
    pdf

  3. J. Martinelli, A. Bharti, A. Tiihonen, L. Filstroff, S. T. John, S. J. Sloman, P. Rinke, and S. Kaski
    “Learning relevant contextual variables within Bayesian optimization”
    Adaptive Experimental Design and Active Learning in the Real World NeurIPS 2023 Workshop
    pdf

Theses

  1. 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