David Alvarez-Melis

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MIT ∙ Microsoft

Gradient Flows in Dataset Space

The current practice in machine learning is traditionally model-centric,...
10/24/2020 ∙ by David Alvarez-Melis, et al. ∙ 29 ∙ share

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Geometric Dataset Distances via Optimal Transport

The notion of task similarity is at the core of various machine learning...
02/07/2020 ∙ by David Alvarez-Melis, et al. ∙ 0 ∙ share

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Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces

This paper focuses on the problem of unsupervised alignment of hierarchi...
11/06/2019 ∙ by David Alvarez-Melis, et al. ∙ 0 ∙ share

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Probabilistic Bias Mitigation in Word Embeddings

It has been shown that word embeddings derived from large corpora tend t...
10/31/2019 ∙ by Hailey James-Sorenson, et al. ∙ 0 ∙ share

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Weight of Evidence as a Basis for Human-Oriented Explanations

Interpretability is an elusive but highly sought-after characteristic of...
10/29/2019 ∙ by David Alvarez-Melis, et al. ∙ 53 ∙ share

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Towards Robust, Locally Linear Deep Networks

Deep networks realize complex mappings that are often understood by thei...
07/07/2019 ∙ by Guang-He Lee, et al. ∙ 2 ∙ share

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Learning Generative Models across Incomparable Spaces

Generative Adversarial Networks have shown remarkable success in learnin...
05/14/2019 ∙ by Charlotte Bunne, et al. ∙ 1 ∙ share

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Functional Transparency for Structured Data: a Game-Theoretic Approach

We provide a new approach to training neural models to exhibit transpare...
02/26/2019 ∙ by Guang-He Lee, et al. ∙ 0 ∙ share

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Gromov-Wasserstein Alignment of Word Embedding Spaces

Cross-lingual or cross-domain correspondences play key roles in tasks ra...
08/31/2018 ∙ by David Alvarez-Melis, et al. ∙ 0 ∙ share

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Game-Theoretic Interpretability for Temporal Modeling

Interpretability has arisen as a key desideratum of machine learning mod...
06/30/2018 ∙ by Guang-He Lee, et al. ∙ 0 ∙ share

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Towards Optimal Transport with Global Invariances

Many problems in machine learning involve calculating correspondences be...
06/25/2018 ∙ by David Alvarez-Melis, et al. ∙ 0 ∙ share

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On the Robustness of Interpretability Methods

We argue that robustness of explanations---i.e., that similar inputs sho...
06/21/2018 ∙ by David Alvarez-Melis, et al. ∙ 0 ∙ share

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Towards Robust Interpretability with Self-Explaining Neural Networks

Most recent work on interpretability of complex machine learning models ...
06/20/2018 ∙ by David Alvarez-Melis, et al. ∙ 0 ∙ share

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Structured Optimal Transport

Optimal Transport has recently gained interest in machine learning for a...
12/17/2017 ∙ by David Alvarez-Melis, et al. ∙ 0 ∙ share

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Word, graph and manifold embedding from Markov processes

Continuous vector representations of words and objects appear to carry s...
09/18/2015 ∙ by Tatsunori B. Hashimoto, et al. ∙ 0 ∙ share

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David Alvarez-Melis

Featured Co-authors

Andreas Krause

Stefanie Jegelka

Hal Daumé III

Youssef Mroueh

Tommi S. Jaakkola

Hanna Wallach

Jennifer Wortman Vaughan

Wengong Jin

Tatsunori B. Hashimoto

Nicolo Fusi

Guang-He Lee

Gradient Flows in Dataset Space

Geometric Dataset Distances via Optimal Transport

Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces

Probabilistic Bias Mitigation in Word Embeddings

Weight of Evidence as a Basis for Human-Oriented Explanations

Towards Robust, Locally Linear Deep Networks

Learning Generative Models across Incomparable Spaces

Functional Transparency for Structured Data: a Game-Theoretic Approach

Gromov-Wasserstein Alignment of Word Embedding Spaces

Game-Theoretic Interpretability for Temporal Modeling

Towards Optimal Transport with Global Invariances

On the Robustness of Interpretability Methods

Towards Robust Interpretability with Self-Explaining Neural Networks

Structured Optimal Transport

Word, graph and manifold embedding from Markov processes