7 papers
Functional Bilevel Optimization for Predictive Fairness
Ieva Petrulionyte, Julien Mairal, Michael Arbel
When sensitive attributes are continuous and high-dimensional demographic score vectors, posteriors over attributes, age or income profiles enforcing full statistical indep…
Non-Stationary Functional Bilevel Optimization
Jason Bohne, Ieva Petrulionyte, Michael Arbel +2
Functional bilevel optimization (FBO) provides a powerful framework for hierarchical learning in function spaces, yet current methods are limited to static offline settings and per…
MAP Estimation with Denoisers: Convergence Rates and Guarantees
Scott Pesme, Giacomo Meanti, Michael Arbel +1
Denoiser models have become powerful tools for inverse problems, enabling the use of pretrained networks to approximate the score of a smoothed prior distribution. These models are…
Learning Theory for Kernel Bilevel Optimization
Fares El Khoury, Edouard Pauwels, Samuel Vaiter +1
Bilevel optimization has emerged as a technique for addressing a wide range of machine learning problems that involve an outer objective implicitly determined by the minimizer of a…
LUDVIG: Learning-Free Uplifting of 2D Visual Features to Gaussian Splatting Scenes
Juliette Marrie, Romain Menegaux, Michael Arbel +2
We address the problem of extending the capabilities of vision foundation models such as DINO, SAM, and CLIP, to 3D tasks. Specifically, we introduce a novel method to uplift 2D im…
Unsupervised Imaging Inverse Problems with Diffusion Distribution Matching
Giacomo Meanti, Thomas Ryckeboer, Michael Arbel +1
This work addresses image restoration tasks through the lens of inverse problems using unpaired datasets. In contrast to traditional approaches -- which typically assume full knowl…