7.4k citations · 22.9k across the 86 of their papers we have counts for
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A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks
Renjie Liao, Raquel Urtasun, Richard Zemel
In this paper, we derive generalization bounds for the two primary classes of graph neural networks (GNNs), namely graph convolutional networks (GCNs) and message passing GNNs (MPG…
Fairness and Robustness in Invariant Learning: A Case Study in Toxicity Classification
Robert Adragna, Elliot Creager, David Madras +1
Robustness is of central importance in machine learning and has given rise to the fields of domain generalization and invariant learning, which are concerned with improving perform…
Probing Few-Shot Generalization with Attributes
Mengye Ren, Eleni Triantafillou, Kuan-Chieh Wang +5
Despite impressive progress in deep learning, generalizing far beyond the training distribution is an important open challenge. In this work, we consider few-shot classification, a…
Theoretical bounds on estimation error for meta-learning
James Lucas, Mengye Ren, Irene Kameni +2
Machine learning models have traditionally been developed under the assumption that the training and test distributions match exactly. However, recent success in few-shot learning…
Environment Inference for Invariant Learning
Elliot Creager, Jörn-Henrik Jacobsen, Richard Zemel
Learning models that gracefully handle distribution shifts is central to research on domain generalization, robust optimization, and fairness. A promising formulation is domain-inv…
SketchEmbedNet: Learning Novel Concepts by Imitating Drawings
Alexander Wang, Mengye Ren, Richard S. Zemel
Sketch drawings capture the salient information of visual concepts. Previous work has shown that neural networks are capable of producing sketches of natural objects drawn from a s…