5.2k citations · 7.8k across the 38 of their papers we have counts for
21 papers · 1 filter
Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift
Benjamin Eyre, Elliot Creager, David Madras +2
Designing deep neural network classifiers that perform robustly on distributions differing from the available training data is an active area of machine learning research. However,…
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…
Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalization
Alexandre Ramé, Kartik Ahuja, Jianyu Zhang +3
Foundation models are redefining how AI systems are built. Practitioners now follow a standard procedure to build their machine learning solutions: from a pre-trained foundation mo…
Learning useful representations for shifting tasks and distributions
Jianyu Zhang, Léon Bottou
Does the dominant approach to learn representations (as a side effect of optimizing an expected cost for a single training distribution) remain a good approach when we are dealing…
The Effects of Regularization and Data Augmentation are Class Dependent
Randall Balestriero, Leon Bottou, Yann LeCun
Regularization is a fundamental technique to prevent over-fitting and to improve generalization performances by constraining a model's complexity. Current Deep Networks heavily rel…
Rich Feature Construction for the Optimization-Generalization Dilemma
Jianyu Zhang, David Lopez-Paz, Léon Bottou
There often is a dilemma between ease of optimization and robust out-of-distribution (OoD) generalization. For instance, many OoD methods rely on penalty terms whose optimization i…