9 citations · 39 across the 17 of their papers we have counts for
7 papers · 1 filter
The Galerkin method beats Graph-Based Approaches for Spectral Algorithms
Vivien Cabannes, Francis Bach
Historically, the machine learning community has derived spectral decompositions from graph-based approaches. We break with this approach and prove the statistical and computationa…
Active Self-Supervised Learning: A Few Low-Cost Relationships Are All You Need
Vivien Cabannes, Leon Bottou, Yann Lecun +1
Self-Supervised Learning (SSL) has emerged as the solution of choice to learn transferable representations from unlabeled data. However, SSL requires to build samples that are know…
On minimal variations for unsupervised representation learning
Vivien Cabannes, Alberto Bietti, Randall Balestriero
Unsupervised representation learning aims at describing raw data efficiently to solve various downstream tasks. It has been approached with many techniques, such as manifold learni…
From Weakly Supervised Learning to Active Learning
Vivien Cabannes
Applied mathematics and machine computations have raised a lot of hope since the recent success of supervised learning. Many practitioners in industries have been trying to switch…
Active Labeling: Streaming Stochastic Gradients
Vivien Cabannes, Francis Bach, Vianney Perchet +1
The workhorse of machine learning is stochastic gradient descent. To access stochastic gradients, it is common to consider iteratively input/output pairs of a training dataset. Int…
Disambiguation of weak supervision with exponential convergence rates
Vivien Cabannes, Francis Bach, Alessandro Rudi
Machine learning approached through supervised learning requires expensive annotation of data. This motivates weakly supervised learning, where data are annotated with incomplete y…
Structured Prediction with Partial Labelling through the Infimum Loss
Vivien Cabannes, Alessandro Rudi, Francis Bach
Annotating datasets is one of the main costs in nowadays supervised learning. The goal of weak supervision is to enable models to learn using only forms of labelling which are chea…