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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...
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11/26/2022
Transfer RL via the Undo Maps Formalism
Transferring knowledge across domains is one of the most fundamental pro...
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10/24/2022
Budget-Constrained Bounds for Mini-Batch Estimation of Optimal Transport
Optimal Transport (OT) is a fundamental tool for comparing probability d...
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10/06/2022
InfoOT: Information Maximizing Optimal Transport
Optimal transport aligns samples across distributions by minimizing the ...
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09/30/2022
Neural Unbalanced Optimal Transport via Cycle-Consistent Semi-Couplings
Comparing unpaired samples of a distribution or population taken at diff...
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08/04/2022
Interpretable Distribution Shift Detection using Optimal Transport
We propose a method to identify and characterize distribution shifts in ...
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05/19/2022
Why GANs are overkill for NLP
This work offers a novel theoretical perspective on why, despite numerou...
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04/18/2022
Hierarchical Optimal Transport for Comparing Histopathology Datasets
Scarcity of labeled histopathology data limits the applicability of deep...
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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...
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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...
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10/24/2020
Gradient Flows in Dataset Space
The current practice in machine learning is traditionally model-centric,...
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02/07/2020
Geometric Dataset Distances via Optimal Transport
The notion of task similarity is at the core of various machine learning...
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11/06/2019
Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces
This paper focuses on the problem of unsupervised alignment of hierarchi...
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10/31/2019
Probabilistic Bias Mitigation in Word Embeddings
It has been shown that word embeddings derived from large corpora tend t...
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10/29/2019
Weight of Evidence as a Basis for Human-Oriented Explanations
Interpretability is an elusive but highly sought-after characteristic of...
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07/07/2019
Towards Robust, Locally Linear Deep Networks
Deep networks realize complex mappings that are often understood by thei...
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05/14/2019
Learning Generative Models across Incomparable Spaces
Generative Adversarial Networks have shown remarkable success in learnin...
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02/26/2019
Functional Transparency for Structured Data: a Game-Theoretic Approach
We provide a new approach to training neural models to exhibit transpare...
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08/31/2018
Gromov-Wasserstein Alignment of Word Embedding Spaces
Cross-lingual or cross-domain correspondences play key roles in tasks ra...
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06/30/2018
Game-Theoretic Interpretability for Temporal Modeling
Interpretability has arisen as a key desideratum of machine learning mod...
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06/25/2018
Towards Optimal Transport with Global Invariances
Many problems in machine learning involve calculating correspondences be...
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06/21/2018
On the Robustness of Interpretability Methods
We argue that robustness of explanations---i.e., that similar inputs sho...
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06/20/2018
Towards Robust Interpretability with Self-Explaining Neural Networks
Most recent work on interpretability of complex machine learning models ...
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12/17/2017
Structured Optimal Transport
Optimal Transport has recently gained interest in machine learning for a...
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09/18/2015