21 citations · 58 across the 17 of their papers we have counts for
9 papers · 1 filter
Convergence Rates for Distribution Matching with Sliced Optimal Transport
Gauthier Thurin, Claire Boyer, Kimia Nadjahi
We study the slice-matching scheme, an efficient iterative method for distribution matching based on sliced optimal transport. We investigate convergence to the target distribution…
Tighter CMI-Based Generalization Bounds via Stochastic Projection and Quantization
Milad Sefidgaran, Kimia Nadjahi, Abdellatif Zaidi
In this paper, we leverage stochastic projection and lossy compression to establish new conditional mutual information (CMI) bounds on the generalization error of statistical learn…
Optimal Transport-based Conformal Prediction
Gauthier Thurin, Kimia Nadjahi, Claire Boyer
Conformal Prediction (CP) is a principled framework for quantifying uncertainty in blackbox learning models, by constructing prediction sets with finite-sample coverage guarantees.…
Slicing Mutual Information Generalization Bounds for Neural Networks
Kimia Nadjahi, Kristjan Greenewald, Rickard Brüel Gabrielsson +1
The ability of machine learning (ML) algorithms to generalize well to unseen data has been studied through the lens of information theory, by bounding the generalization error with…
Shedding a PAC-Bayesian Light on Adaptive Sliced-Wasserstein Distances
Ruben Ohana, Kimia Nadjahi, Alain Rakotomamonjy +1
The Sliced-Wasserstein distance (SW) is a computationally efficient and theoretically grounded alternative to the Wasserstein distance. Yet, the literature on its statistical prope…
Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections
Kimia Nadjahi, Alain Durmus, Pierre E. Jacob +2
The Sliced-Wasserstein distance (SW) is being increasingly used in machine learning applications as an alternative to the Wasserstein distance and offers significant computational…
Statistical and Topological Properties of Sliced Probability Divergences
Kimia Nadjahi, Alain Durmus, Lénaïc Chizat +3
The idea of slicing divergences has been proven to be successful when comparing two probability measures in various machine learning applications including generative modeling, and…
Generalized Sliced Distances for Probability Distributions
Soheil Kolouri, Kimia Nadjahi, Umut Simsekli +1
Probability metrics have become an indispensable part of modern statistics and machine learning, and they play a quintessential role in various applications, including statistical…
Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance
Kimia Nadjahi, Alain Durmus, Umut Şimşekli +1
Minimum expected distance estimation (MEDE) algorithms have been widely used for probabilistic models with intractable likelihood functions and they have become increasingly popula…