414 citations · 723 across the 50 of their papers we have counts for
6 papers · 1 filter
Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces
David Alvarez-Melis, Youssef Mroueh, Tommi S. Jaakkola
This paper focuses on the problem of unsupervised alignment of hierarchical data such as ontologies or lexical databases. This is a problem that appears across areas, from natural…
Weight of Evidence as a Basis for Human-Oriented Explanations
David Alvarez-Melis, Hal Daumé, Jennifer Wortman Vaughan +1
Interpretability is an elusive but highly sought-after characteristic of modern machine learning methods. Recent work has focused on interpretability via , w…
Probabilistic Bias Mitigation in Word Embeddings
Hailey Joren, David Alvarez-Melis
It has been shown that word embeddings derived from large corpora tend to incorporate biases present in their training data. Various methods for mitigating these biases have been p…
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,…
Functional Transparency for Structured Data: a Game-Theoretic Approach
Guang-He Lee, Wengong Jin, David Alvarez-Melis +1
We provide a new approach to training neural models to exhibit transparency in a well-defined, functional manner. Our approach naturally operates over structured data and tailors t…