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Gradient Flows in Dataset Space
The current practice in machine learning is traditionally model-centric,...
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Geometric Dataset Distances via Optimal Transport
The notion of task similarity is at the core of various machine learning...
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Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces
This paper focuses on the problem of unsupervised alignment of hierarchi...
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Probabilistic Bias Mitigation in Word Embeddings
It has been shown that word embeddings derived from large corpora tend t...
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Weight of Evidence as a Basis for Human-Oriented Explanations
Interpretability is an elusive but highly sought-after characteristic of...
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Towards Robust, Locally Linear Deep Networks
Deep networks realize complex mappings that are often understood by thei...
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Learning Generative Models across Incomparable Spaces
Generative Adversarial Networks have shown remarkable success in learnin...
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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...
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Gromov-Wasserstein Alignment of Word Embedding Spaces
Cross-lingual or cross-domain correspondences play key roles in tasks ra...
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Game-Theoretic Interpretability for Temporal Modeling
Interpretability has arisen as a key desideratum of machine learning mod...
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Towards Optimal Transport with Global Invariances
Many problems in machine learning involve calculating correspondences be...
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On the Robustness of Interpretability Methods
We argue that robustness of explanations---i.e., that similar inputs sho...
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Towards Robust Interpretability with Self-Explaining Neural Networks
Most recent work on interpretability of complex machine learning models ...
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Structured Optimal Transport
Optimal Transport has recently gained interest in machine learning for a...
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Word, graph and manifold embedding from Markov processes
Continuous vector representations of words and objects appear to carry s...
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