Louis Filstroff

Orcid: 0000-0002-7386-4745

According to our database1, Louis Filstroff authored at least 13 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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Article 
PhD thesis 
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Links

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Bibliography

2024
Targeted Active Learning for Bayesian Decision-Making.
Trans. Mach. Learn. Res., 2024

2023
Cost-aware learning of relevant contextual variables within Bayesian optimization.
CoRR, 2023

Multi-Fidelity Bayesian Optimization with Unreliable Information Sources.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Bayesian Optimization Augmented with Actively Elicited Expert Knowledge.
CoRR, 2022

Approximate Bayesian Computation with Domain Expert in the Loop.
Proceedings of the International Conference on Machine Learning, 2022

2021
A Comparative Study of Gamma Markov Chains for Temporal Non-Negative Matrix Factorization.
IEEE Trans. Signal Process., 2021

2020
Bayesian mean-parameterized nonnegative binary matrix factorization.
Data Min. Knowl. Discov., 2020

A Comparative Study of Temporal Non-Negative Matrix Factorization with Gamma Markov Chains.
CoRR, 2020

2019
Contributions to probabilistic non-negative matrix factorization - Maximum marginal likelihood estimation and Markovian temporal models. (Contributions à la factorisation en matrices non-négatives probabiliste - Estimation par maximum de vraisemblance marginale et modèles markoviens temporels).
PhD thesis, 2019

A Ranking Model Motivated by Nonnegative Matrix Factorization with Applications to Tennis Tournaments.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

2018
Closed-form marginal likelihood in Gamma-Poisson factorization.
CoRR, 2018

An Empirical Study of Steganography and Steganalysis of Color Images in the JPEG Domain.
Proceedings of the Digital Forensics and Watermarking - 17th International Workshop, 2018

Closed-form Marginal Likelihood in Gamma-Poisson Matrix Factorization.
Proceedings of the 35th International Conference on Machine Learning, 2018


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