27 Hi! PARIS Papers Accepted at ICML 2025
From July 13 to 19, Hi! PARIS researchers will take part in the International Conference on Machine Learning (ICML 2025), one of the world’s leading conferences in artificial intelligence, held this year in Vancouver, Canada.
A total of 27 research papers from Hi! PARIS affiliated teams have been accepted following ICML’s highly selective peer-review process, recognizing the excellence and innovation of our researchers across partner institutions.
This strong presence reflects Hi! PARIS’s scientific leadership and deep engagement in frontier AI research. The accepted papers span a wide array of domains, including machine learning theory, optimization, generative models, privacy, and multi-agent systems, and demonstrate the center’s interdisciplinary strength.
By contributing to ICML 2025’s core sessions and workshops, Hi! PARIS reaffirms its commitment to advancing AI research for science, business, and society at the highest international level.
Congratulations to our researchers!
Here is a list of papers accepted at ICML 2025 that include at least one author affiliated with Hi! PARIS:
| Title | Hi! PARIS Authors | All Authors |
|---|---|---|
| Branches: Efficiently Seeking Optimal Sparse Decision Trees via AO* | Albert Bifet, Jesse Read | Ayman Chaouki, Jesse Read, Albert Bifet |
| To Each Metric Its Decoding | Thomas Bonald, Matthieu Labeau | Roman Plaud, Alexandre Perez-Lebel, Matthieu Labeau, Antoine Saillenfest, Thomas Bonald |
| Scaling Laws for Forgetting | Marco Cuturi | Louis Béthune, David Grangier, Dan Busbridge, Eleonora Gualdoni, Marco Cuturi, Pierre Ablin |
| Shielded Diffusion | Marco Cuturi | Michael Kirchhof, James Thornton, Louis Béthune, Pierre Ablin, Eugene Ndiaye, Marco Cuturi |
| Misspecification in Simulation-based Inference | Marco Cuturi | Antoine Wehenkel, Juan L. Gamella, Ozan Sener, Jens Behrmann, Guillermo Sapiro, Jörn Jacobsen, Marco Cuturi |
| TabICL | Gael Varoquaux | Jingang QU, David Holzmüller, Gael Varoquaux, Marine Le Morvan |
| Byzantine Robust Gossip | Aymeric Dieuleveut | Renaud Gaucher, Aymeric Dieuleveut, Hadrien Hendrikx |
| Scaffold with Stochastic Gradients | Aymeric Dieuleveut, Alain Oliviero Durmus, Eric Moulines | Paul Mangold, Alain Oliviero Durmus, Aymeric Dieuleveut, Eric Moulines |
| Compressed and Distributed Least-Squares | Aymeric Dieuleveut | Constantin Philippenko, Aymeric Dieuleveut |
| Discrete Markov Probabilistic Models | Alain Oliviero Durmus | Le Tuyet Nhi PHAM, Dario Shariatian, Antonio Ocello, Giovanni Conforti, Alain Oliviero Durmus |
| Prediction-Aware Learning | Alain Oliviero Durmus, Eric Moulines | Aymeric Capitaine, Etienne Boursier, Eric Moulines, Michael Jordan, Alain Oliviero Durmus |
| Mixture-Based Framework for Diffusion Models | Alain Oliviero Durmus, Eric Moulines | Yazid Janati el idrissi, Badr MOUFAD, Mehdi Qassime, Alain Oliviero Durmus, Eric Moulines, Jimmy Olsson |
| Differential Privacy for MCMC | Alain Oliviero Durmus | Andrea Bertazzi, Tim Johnston, Gareth Roberts, Alain Oliviero Durmus |
| Asymmetric Actor-Critic Algorithms | Damien Ernst | Gaspard Lambrechts, Damien Ernst, Aditya Mahajan |
| Score-Based Generative Models in W2 | Marta Gentiloni Silveri | Marta Gentiloni Silveri, Antonio Ocello |
| Sliced-Wasserstein Distance Analysis | Anna Korba | Christophe Vauthier, Anna Korba, Quentin Mérigot |
| Density Ratio Estimation | Anna Korba | Hanlin Yu, Arto Klami, Aapo Hyvarinen, Anna Korba, Lemir Omar Chehab |
| Wasserstein Gradient Flows | Anna Korba | Clément Bonet, Christophe Vauthier, Anna Korba |
| Learning of Continuous Markov Semigroups | Karim Lounici | Vladimir Kostic, Karim Lounici, Hélène Halconruy, Timothée Devergne, Pietro Novelli, Massimiliano Pontil |
| GNN with GMM Augmentation | Johannes Lutzeyer, Michalis Vazirgiannis | Yassine Abbahaddou, Fragkiskos Malliaros, Johannes Lutzeyer, Amine Aboussalah, Michalis Vazirgiannis |
| TRPO in Mean Field Games | Eric Moulines | Antonio Ocello, Daniil Tiapkin, Lorenzo Mancini, Mathieu Lauriere, Eric Moulines |
| Conditional Coverage with Conformity Scores | Eric Moulines | Vincent Plassier, Alexander Fishkov, Victor Dheur, Mohsen Guizani, Souhaib Ben Taieb, Maxim Panov, Eric Moulines |
| Efficient On-Device Learning | Van-tam Nguyen, Enzo Tartaglione | Le-Trung Nguyen, Aël Quélennec, Van-Tam Nguyen, Enzo Tartaglione |
| Pareto-Optimality in One-Max-Search | Vianney Perchet | Ziyad Benomar, Lorenzo Croissant, Vianney Perchet, Spyros Angelopoulos |
| Last Iterate Convergence for Uncoupled Learning | Vianney Perchet | Côme Fiegel, Pierre Menard, Tadashi Kozuno, Michal Valko, Vianney Perchet |
| Quantifying Treatment Effects | Erwan Scornet | Ahmed Boughdiri, Julie Josse, Erwan Scornet |
| Prediction via Shapley Value Regression | Michalis Vazirgiannis | Amr Alkhatib, Roman Bresson, Henrik Boström, Michalis Vazirgiannis |