David Alvarez-Melis | DeepAI

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∙ 06/12/2023

Generating Synthetic Datasets by Interpolating along Generalized Geodesics

Data for pretraining machine learning models often consists of collectio...

0 Jiaojiao Fan, et al. ∙

∙ 11/26/2022

Transfer RL via the Undo Maps Formalism

Transferring knowledge across domains is one of the most fundamental pro...

0 Abhi Gupta, et al. ∙

∙ 10/24/2022

Budget-Constrained Bounds for Mini-Batch Estimation of Optimal Transport

Optimal Transport (OT) is a fundamental tool for comparing probability d...

0 David Alvarez-Melis, et al. ∙

∙ 10/06/2022

InfoOT: Information Maximizing Optimal Transport

Optimal transport aligns samples across distributions by minimizing the ...

5 Ching-Yao Chuang, et al. ∙

∙ 09/30/2022

Neural Unbalanced Optimal Transport via Cycle-Consistent Semi-Couplings

Comparing unpaired samples of a distribution or population taken at diff...

5 Frederike Lübeck, et al. ∙

∙ 08/04/2022

Interpretable Distribution Shift Detection using Optimal Transport

We propose a method to identify and characterize distribution shifts in ...

29 Neha Hulkund, et al. ∙

∙ 05/19/2022

Why GANs are overkill for NLP

This work offers a novel theoretical perspective on why, despite numerou...

13 David Alvarez-Melis, et al. ∙

∙ 04/18/2022

Hierarchical Optimal Transport for Comparing Histopathology Datasets

Scarcity of labeled histopathology data limits the applicability of deep...

16 Anna Yeaton, et al. ∙

∙ 06/01/2021

Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks

Gradient flows are a powerful tool for optimizing functionals in general...

20 David Alvarez-Melis, et al. ∙

∙ 04/27/2021

A Human-Centered Interpretability Framework Based on Weight of Evidence

In this paper, we take a human-centered approach to interpretable machin...

16 David Alvarez-Melis, et al. ∙

∙ 10/24/2020

Gradient Flows in Dataset Space

The current practice in machine learning is traditionally model-centric,...

29 David Alvarez-Melis, et al. ∙

∙ 02/07/2020

Geometric Dataset Distances via Optimal Transport

The notion of task similarity is at the core of various machine learning...

0 David Alvarez-Melis, et al. ∙

∙ 11/06/2019

Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces

This paper focuses on the problem of unsupervised alignment of hierarchi...

0 David Alvarez-Melis, et al. ∙

∙ 10/31/2019

Probabilistic Bias Mitigation in Word Embeddings

It has been shown that word embeddings derived from large corpora tend t...

0 Hailey James-Sorenson, et al. ∙

∙ 10/29/2019

Weight of Evidence as a Basis for Human-Oriented Explanations

Interpretability is an elusive but highly sought-after characteristic of...

53 David Alvarez-Melis, et al. ∙

∙ 07/07/2019

Towards Robust, Locally Linear Deep Networks

Deep networks realize complex mappings that are often understood by thei...

2 Guang-He Lee, et al. ∙

∙ 05/14/2019

Learning Generative Models across Incomparable Spaces

Generative Adversarial Networks have shown remarkable success in learnin...

1 Charlotte Bunne, et al. ∙

∙ 02/26/2019

Functional Transparency for Structured Data: a Game-Theoretic Approach

We provide a new approach to training neural models to exhibit transpare...

0 Guang-He Lee, et al. ∙

∙ 08/31/2018

Gromov-Wasserstein Alignment of Word Embedding Spaces

Cross-lingual or cross-domain correspondences play key roles in tasks ra...

0 David Alvarez-Melis, et al. ∙

∙ 06/30/2018

Game-Theoretic Interpretability for Temporal Modeling

Interpretability has arisen as a key desideratum of machine learning mod...

0 Guang-He Lee, et al. ∙

∙ 06/25/2018

Towards Optimal Transport with Global Invariances

Many problems in machine learning involve calculating correspondences be...

0 David Alvarez-Melis, et al. ∙

∙ 06/21/2018

On the Robustness of Interpretability Methods

We argue that robustness of explanations---i.e., that similar inputs sho...

0 David Alvarez-Melis, et al. ∙

∙ 06/20/2018

Towards Robust Interpretability with Self-Explaining Neural Networks

Most recent work on interpretability of complex machine learning models ...

0 David Alvarez-Melis, et al. ∙

∙ 12/17/2017

Structured Optimal Transport

Optimal Transport has recently gained interest in machine learning for a...

0 David Alvarez-Melis, et al. ∙

∙ 09/18/2015

Word, graph and manifold embedding from Markov processes

Continuous vector representations of words and objects appear to carry s...

0 Tatsunori B. Hashimoto, et al. ∙

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