Michal Klein

According to our database, Michal Klein authored at least 19 papers between 2022 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

The Data-Quality Illusion: Rethinking Classifier-Based Quality Filtering for LLM Pretraining.

Thiziri Nait Saada, Louis Béthune, Michal Klein, David Grangier, Marco Cuturi, Pierre Ablin

CoRR, October, 2025

Flow Matching with Semidiscrete Couplings.

Alireza Mousavi Hosseini, Stephen Y. Zhang, Michal Klein, Marco Cuturi

CoRR, September, 2025

The Geometries of Truth Are Orthogonal Across Tasks.

Waïss Azizian, Michael Kirchhof, Eugène Ndiaye, Louis Béthune, Michal Klein, Pierre Ablin, Marco Cuturi

CoRR, June, 2025

On Fitting Flow Models with Large Sinkhorn Couplings.

Michal Klein, Alireza Mousavi Hosseini, Stephen Y. Zhang, Marco Cuturi

CoRR, June, 2025

End-to-end Learning of Sparse Interventions on Activations to Steer Generation.

Pau Rodríguez, Michal Klein, Eleonora Gualdoni, Arno Blaas, Luca Zappella, Marco Cuturi, Xavier Suau

CoRR, March, 2025

Multivariate Conformal Prediction using Optimal Transport.

Michal Klein, Louis Béthune, Eugène Ndiaye, Marco Cuturi

CoRR, February, 2025

Controlling Language and Diffusion Models by Transporting Activations.

Pau Rodríguez, Arno Blaas, Michal Klein, Luca Zappella, Nicholas Apostoloff, Marco Cuturi, Xavier Suau

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Simple ReFlow: Improved Techniques for Fast Flow Models.

Beomsu Kim, Yu-Guan Hsieh, Michal Klein, Marco Cuturi, Jong Chul Ye, Bahjat Kawar, James Thornton

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Multimodal Autoregressive Pre-training of Large Vision Encoders.

Enrico Fini, Mustafa Shukor, Xiujun Li, Philipp Dufter, Michal Klein, David Haldimann, Sai Aitharaju, Victor G. Turrisi da Costa, Louis Béthune, Zhe Gan, Alexander Toshev, Marcin Eichner, Moin Nabi, Yinfei Yang, Joshua M. Susskind, Alaaeldin El-Nouby

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025

2024

Learning Elastic Costs to Shape Monge Displacements.

Michal Klein, Aram-Alexandre Pooladian, Pierre Ablin, Eugène Ndiaye, Jonathan Niles-Weed, Marco Cuturi

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Progressive Entropic Optimal Transport Solvers.

Parnian Kassraie, Aram-Alexandre Pooladian, Michal Klein, James Thornton, Jonathan Niles-Weed, Marco Cuturi

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Contrasting Multiple Representations with the Multi-Marginal Matching Gap.

Zoe Piran, Michal Klein, James Thornton, Marco Cuturi

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Careful with that Scalpel: Improving Gradient Surgery with an EMA.

Yu-Guan Hsieh, James Thornton, Eugène Ndiaye, Michal Klein, Marco Cuturi, Pierre Ablin

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Scalable Pre-training of Large Autoregressive Image Models.

Alaaeldin El-Nouby, Michal Klein, Shuangfei Zhai, Miguel Ángel Bautista, Vaishaal Shankar, Alexander T. Toshev, Joshua M. Susskind, Armand Joulin

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Learning Costs for Structured Monge Displacements.

Michal Klein, Aram-Alexandre Pooladian, Pierre Ablin, Eugène Ndiaye, Jonathan Niles-Weed, Marco Cuturi

CoRR, 2023

Unbalanced Low-rank Optimal Transport Solvers.

Meyer Scetbon, Michal Klein, Giovanni Palla, Marco Cuturi

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps.

Marco Cuturi, Michal Klein, Pierre Ablin

Proceedings of the International Conference on Machine Learning, 2023

Predicting Ordinary Differential Equations with Transformers.

Sören Becker, Michal Klein, Alexander Neitz, Giambattista Parascandolo, Niki Kilbertus

Proceedings of the International Conference on Machine Learning, 2023

2022

Discovering ordinary differential equations that govern time-series.

Sören Becker, Michal Klein, Alexander Neitz, Giambattista Parascandolo, Niki Kilbertus

CoRR, 2022