828 citations · 5.6k across the 171 of their papers we have counts for
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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…
Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network
Wengong Jin, Connor W. Coley, Regina Barzilay +1
The prediction of organic reaction outcomes is a fundamental problem in computational chemistry. Since a reaction may involve hundreds of atoms, fully exploring the space of possib…
Grounding Language for Transfer in Deep Reinforcement Learning
Karthik Narasimhan, Regina Barzilay, Tommi Jaakkola
In this paper, we explore the utilization of natural language to drive transfer for reinforcement learning (RL). Despite the wide-spread application of deep RL techniques, learning…
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…
Style Transfer from Non-Parallel Text by Cross-Alignment
Tianxiao Shen, Tao Lei, Regina Barzilay +1
This paper focuses on style transfer on the basis of non-parallel text. This is an instance of a broad family of problems including machine translation, decipherment, and sentiment…
Deriving Neural Architectures from Sequence and Graph Kernels
Tao Lei, Wengong Jin, Regina Barzilay +1
The design of neural architectures for structured objects is typically guided by experimental insights rather than a formal process. In this work, we appeal to kernels over combina…