Kilian Fatras

Orcid: 0000-0003-4458-7029

According to our database1, Kilian Fatras authored at least 21 papers between 2019 and 2023.

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

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Other 

Links

On csauthors.net:

Bibliography

2023
SE(3)-Stochastic Flow Matching for Protein Backbone Generation.
CoRR, 2023

Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted Trees.
CoRR, 2023

Simulation-free Schrödinger bridges via score and flow matching.
CoRR, 2023

No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths.
CoRR, 2023

Unbalanced Optimal Transport meets Sliced-Wasserstein.
CoRR, 2023

Diffusion models with location-scale noise.
CoRR, 2023

PopulAtion Parameter Averaging (PAPA).
CoRR, 2023

Conditional Flow Matching: Simulation-Free Dynamic Optimal Transport.
CoRR, 2023

2022
Generating Natural Adversarial Remote Sensing Images.
IEEE Trans. Geosci. Remote. Sens., 2022

Wasserstein Adversarial Regularization for Learning With Label Noise.
IEEE Trans. Pattern Anal. Mach. Intell., 2022

A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods.
CoRR, 2022

On making optimal transport robust to all outliers.
CoRR, 2022

Optimal Transport meets Noisy Label Robust Loss and MixUp Regularization for Domain Adaptation.
Proceedings of the Conference on Lifelong Learning Agents, 2022

2021
Deep learning and optimal transport: learning from one another. (Apprentissage profond et transport optimal: apprendre l'un de l'autre).
PhD thesis, 2021

POT: Python Optimal Transport.
J. Mach. Learn. Res., 2021

Minibatch optimal transport distances; analysis and applications.
CoRR, 2021

Unbalanced minibatch Optimal Transport; applications to Domain Adaptation.
Proceedings of the 38th International Conference on Machine Learning, 2021

2020
Generating Natural Adversarial Hyperspectral examples with a modified Wasserstein GAN.
CoRR, 2020

Learning with minibatch Wasserstein : asymptotic and gradient properties.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Pushing the right boundaries matters! Wasserstein Adversarial Training for Label Noise.
CoRR, 2019

Proximal Splitting Meets Variance Reduction.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019


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