2k citations · 6.6k across the 78 of their papers we have counts for
8 papers · 1 filter
Learning Reward Functions for Robotic Manipulation by Observing Humans
Minttu Alakuijala, Gabriel Dulac-Arnold, Julien Mairal +2
Observing a human demonstrator manipulate objects provides a rich, scalable and inexpensive source of data for learning robotic policies. However, transferring skills from human vi…
Entropic Descent Archetypal Analysis for Blind Hyperspectral Unmixing
Alexandre Zouaoui, Gedeon Muhawenayo, Behnood Rasti +2
In this paper, we introduce a new algorithm based on archetypal analysis for blind hyperspectral unmixing, assuming linear mixing of endmembers. Archetypal analysis is a natural fo…
High Dynamic Range and Super-Resolution from Raw Image Bursts
Bruno Lecouat, Thomas Eboli, Jean Ponce +1
Photographs captured by smartphones and mid-range cameras have limited spatial resolution and dynamic range, with noisy response in underexposed regions and color artefacts in satu…
Non-Convex Bilevel Games with Critical Point Selection Maps
Michael Arbel, Julien Mairal
Bilevel optimization problems involve two nested objectives, where an upper-level objective depends on a solution to a lower-level problem. When the latter is non-convex, multiple…
Self Supervised Learning for Few Shot Hyperspectral Image Classification
Nassim Ait Ali Braham, Lichao Mou, Jocelyn Chanussot +2
Deep learning has proven to be a very effective approach for Hyperspectral Image (HSI) classification. However, deep neural networks require large annotated datasets to generalize…
The Spectral Bias of Polynomial Neural Networks
Moulik Choraria, Leello Tadesse Dadi, Grigorios Chrysos +2
Polynomial neural networks (PNNs) have been recently shown to be particularly effective at image generation and face recognition, where high-frequency information is critical. Prev…
Efficient Kernel UCB for Contextual Bandits
Houssam Zenati, Alberto Bietti, Eustache Diemert +3
In this paper, we tackle the computational efficiency of kernelized UCB algorithms in contextual bandits. While standard methods require a O(CT^3) complexity where T is the horizon…
On the Benefits of Large Learning Rates for Kernel Methods
Gaspard Beugnot, Julien Mairal, Alessandro Rudi
This paper studies an intriguing phenomenon related to the good generalization performance of estimators obtained by using large learning rates within gradient descent algorithms.…