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Stanford AI Lab Papers and Talks at EMNLP 2020

All the great work from the Stanford AI Lab accepted at EMNLP 2020, all in one place.

Learning to Influence Multi-Agent Interaction

We introduce a framework for multi-agent interaction that represents the low-level policies of non-stationary agents with high-level latent strategies.

Bootleg: Chasing the Tail with Self-Supervised Named Entity Disambiguation

Laurel Orr, Megan Leszczynski, Simran Arora, Neel Guha, Xiao Ling, Sen Wu, and Chris Ré

Identifying rare people, places, and things (entities) in text is a critical problem in AI, especially as the majority of entities are rare. We address this problem in our self-supervised system Bootleg. Bootleg learns to reason over entity types and relations for improved rare entity performance.

Measuring Bias in NLP (with Confidence!)

Kawin Ethayarajh

When estimating bias in NLP, there is usually uncertainty. We propose capturing this uncertainty using confidence intervals, finding that the datasets currently used to measure bias are too small to conclusively identify it – except in the most egregious cases.

Learning to Fix Programs from Error Messages

Michihiro Yasunaga

We study how to use machine learning to repair programs from error messages, and introduce a promising approach that leverages program-feedback graphs and self-supervised learning.

Adapting on the Fly to Test Time Distribution Shift

Marvin Zhang

We meta-learn models that can use unlabeled test data to adapt to group distribution shift.

The Coming Wave of ML Systems

Chris Ré, Piero Molino, Dan Fu, Karan Goel, Fiodar Kazhamakia, and Matei Zaharia

Introducing the Stanford MLSys Seminar Series on the frontier of machine learning systems. How does ML change the modern programming stack, and what does it mean for how people will build and deploy applications in the future?

GTI: Learning to Generalize Across Long-Horizon Tasks from Human Demonstrations

Ajay Mandlekar, Danfei Xu

We developed Generalization Through Imitation (GTI) - an algorithm for learning visuomotor control from human demonstrations and generalizing to new long-horizon tasks by leveraging latent compositional structures.

How to Fill in the Blanks with Language Models

Mina Lee

We present a simple way to extend the capabilities of language models to fill in the blanks. Our framework constructs training examples by masking random spans to generate pairs of inputs and targets and fine-tunes language models on the concatenation of each pair.

Safety Validation of Black-Box Autonomous Systems

Anthony L. Corso

Validation of safety-critical autonomous systems is necessary before deployment and is made possible by black-box validation strategies. In this blog post, we provide a model for black-box safety validation and survey the literature to uncover the most effective validation techniques.