49 citations · 85 across the 10 of their papers we have counts for
14 papers
Emergent Communication: Generalization and Overfitting in Lewis Games
Mathieu Rita, Corentin Tallec, Paul Michel +4
Lewis signaling games are a class of simple communication games for simulating the emergence of language. In these games, two agents must agree on a communication protocol in order…
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…
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…
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…