49 citations · 85 across the 12 of their papers we have counts for
7 papers · 1 filter
Benchmarking_Fast_Domain_Adaptation_for_Unsupervised_Speech_Units
Robin San Roman, Manel Khentout, Tu Anh Nguyen +3
Representation learning has attracted great atten- tion and managed to reach good performances as a pretraining method for downstream tasks or as a first step towards unsu- pervise…
Curriculum Learning with Adam: The Devil Is in the Wrong Details
Lucas Weber, Jaap Jumelet, Paul Michel +2
Curriculum learning (CL) posits that machine learning models -- similar to humans -- may learn more efficiently from data that match their current learning progress. However, CL me…
Distributionally Robust Models with Parametric Likelihood Ratios
Paul Michel, Tatsunori Hashimoto, Graham Neubig
As machine learning models are deployed ever more broadly, it becomes increasingly important that they are not only able to perform well on their training distribution, but also yi…
Balancing Average and Worst-case Accuracy in Multitask Learning
Paul Michel, Sebastian Ruder, Dani Yogatama
When training and evaluating machine learning models on a large number of tasks, it is important to not only look at average task accuracy -- which may be biased by easy or redunda…
Examining and Combating Spurious Features under Distribution Shift
Chunting Zhou, Xuezhe Ma, Paul Michel +1
A central goal of machine learning is to learn robust representations that capture the causal relationship between inputs features and output labels. However, minimizing empirical…
Modeling the Second Player in Distributionally Robust Optimization
Paul Michel, Tatsunori Hashimoto, Graham Neubig
Distributionally robust optimization (DRO) provides a framework for training machine learning models that are able to perform well on a collection of related data distributions (th…