9 papers
Directly Optimizing Mean Demographic Parity for Nonlinear Regression
Ieva Petrulionyte, Julien Mairal, Michael Arbel
We focus on regression settings where the fairness goal is to equalize average predictions across values of a sensitive attribute, a criterion known as mean demographic parity. Dir…
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…
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…
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…
Functional Bilevel Optimization for Machine Learning
Ieva Petrulionyte, Julien Mairal, Michael Arbel
In this paper, we introduce a new functional point of view on bilevel optimization problems for machine learning, where the inner objective is minimized over a function space. Thes…
MLXP: A Framework for Conducting Replicable Experiments in Python
Michael Arbel, Alexandre Zouaoui
Replicability in machine learning (ML) research is increasingly concerning due to the utilization of complex non-deterministic algorithms and the dependence on numerous hyper-param…
On Good Practices for Task-Specific Distillation of Large Pretrained Visual Models
Juliette Marrie, Michael Arbel, Julien Mairal +1
Large pretrained visual models exhibit remarkable generalization across diverse recognition tasks. Yet, real-world applications often demand compact models tailored to specific pro…