32 citations · 110 across the 9 of their papers we have counts for
12 papers
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…
Approximate Bayesian Computation with the Sliced-Wasserstein Distance
Kimia Nadjahi, Valentin De Bortoli, Alain Durmus +2
Approximate Bayesian Computation (ABC) is a popular method for approximate inference in generative models with intractable but easy-to-sample likelihood. It constructs an approxima…
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…
Generalized Sliced Wasserstein Distances
Soheil Kolouri, Kimia Nadjahi, Umut Simsekli +2
The Wasserstein distance and its variations, e.g., the sliced-Wasserstein (SW) distance, have recently drawn attention from the machine learning community. The SW distance, specifi…
Model-based STFT phase recovery for audio source separation
Paul Magron, Roland Badeau, Bertrand David
For audio source separation applications, it is common to estimate the magnitude of the short-time Fourier transform (STFT) of each source. In order to further synthesizing time-do…
Lévy NMF for robust nonnegative source separation
Paul Magron, Roland Badeau, Antoine Liutkus
Source separation, which consists in decomposing data into meaningful structured components, is an active research topic in many areas, such as music and image signal processing, a…