25 citations · 62 across the 5 of their papers we have counts for
5 papers
Towards Robust, Locally Linear Deep Networks
Guang-He Lee, David Alvarez-Melis, Tommi S. Jaakkola
Deep networks realize complex mappings that are often understood by their locally linear behavior at or around points of interest. For example, we use the derivative of the mapping…
Learning Generative Models across Incomparable Spaces
Charlotte Bunne, David Alvarez-Melis, Andreas Krause +1
Generative Adversarial Networks have shown remarkable success in learning a distribution that faithfully recovers a reference distribution in its entirety. However, in some cases,…
Structured Optimal Transport
David Alvarez-Melis, Tommi S. Jaakkola, Stefanie Jegelka
Optimal Transport has recently gained interest in machine learning for applications ranging from domain adaptation, sentence similarities to deep learning. Yet, its ability to capt…
Distributional Adversarial Networks
Chengtao Li, David Alvarez-Melis, Keyulu Xu +2
We propose a framework for adversarial training that relies on a sample rather than a single sample point as the fundamental unit of discrimination. Inspired by discrepancy measure…
A causal framework for explaining the predictions of black-box sequence-to-sequence models
David Alvarez-Melis, Tommi S. Jaakkola
We interpret the predictions of any black-box structured input-structured output model around a specific input-output pair. Our method returns an "explanation" consisting of groups…