343 citations · 494 across the 18 of their papers we have counts for
5 papers · 1 filter
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…
Learning Neural Models for Natural Language Processing in the Face of Distributional Shift
Paul Michel
The dominating NLP paradigm of training a strong neural predictor to perform one task on a specific dataset has led to state-of-the-art performance in a variety of applications (eg…
Should We Be Pre-training? An Argument for End-task Aware Training as an Alternative
Lucio M. Dery, Paul Michel, Ameet Talwalkar +1
In most settings of practical concern, machine learning practitioners know in advance what end-task they wish to boost with auxiliary tasks. However, widely used methods for levera…
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…