Research
Publications
- G. Thurin, K. Nadjahi, C. Boyer. Optimal Transport-based Conformal Prediction. ICML 2025
- C. Bonet*, K. Nadjahi*, T. Séjourné*, K. Fatras, N. Courty. Slicing Unbalanced Optimal Transport. TMLR (January 2025)
- J. Zhu, K. Greenewald, K. Nadjahi, H. Sáez de Ocáriz Borde, R. Brüel Gabrielsson, L. Choshen, M. Ghassemi, M. Yurochkin, J. Solomon. Asymmetry in Low-Rank Adapters of Foundation Models. ICML 2024
- K. Nadjahi, K. Greenewald, R. Brüel Gabrielsson, J. Solomon. Slicing Mutual Information Generalization Bounds for Neural Networks. ICML 2024
- A. Rakotomamonjy, K. Nadjahi, L. Ralaivola. Federated Wasserstein Distance. ICLR 2024
- R. Ohana*, K. Nadjahi*, A. Rakotomamonjy, L. Ralaivola. Shedding a PAC-Bayesian Light on Adaptive Sliced-Wasserstein Distances. ICML 2023
- S. Kolouri, K. Nadjahi, U. Şimşekli, S. Shahrampour. Generalized Sliced Probability Metrics. IEEE ICASSP 2022 (Best Paper Award)
- K. Nadjahi, A. Durmus, P. E. Jacob, R. Badeau, U. Şimşekli. Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections. NeurIPS 2021
- K. Nadjahi, A. Durmus, L. Chizat, S. Kolouri, S. Shahrampour, U. Şimşekli. Statistical and Topological Properties of Sliced Probability Divergences. NeurIPS 2020 (Spotlight)
- K. Nadjahi, V. De Bortoli, A. Durmus, R. Badeau, U. Şimşekli. Approximate Bayesian Computation with the Sliced-Wasserstein Distance. IEEE ICASSP 2020 (Best Student Paper Award)
- K. Nadjahi, A. Durmus, U. Şimşekli, R. Badeau. Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance. NeurIPS 2019 (Spotlight)
- S. Kolouri*, K. Nadjahi*, U. Şimşekli, R. Badeau, G. K. Rohde. Generalized Sliced Wasserstein Distances. NeurIPS 2019
- K. Nadjahi, R. Laroche, R. Tachet des Combes. Safe Policy Improvement with Soft Baseline Bootstrapping. ECML-PKDD 2019
PhD thesis