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Defuse: Harnessing Unrestricted Adversarial Examples for Debugging Models Beyond Test Accuracy
We typically compute aggregate statistics on held-out test data to asses...
02/11/2021 ∙ by Dylan Slack, et al. ∙
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Differentially Private Language Models Benefit from Public Pre-training
Language modeling is a keystone task in natural language processing. Whe...
09/13/2020 ∙ by Gavin Kerrigan, et al. ∙
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How Much Should I Trust You? Modeling Uncertainty of Black Box Explanations
As local explanations of black box models are increasingly being employe...
08/11/2020 ∙ by Dylan Slack, et al. ∙
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Fair Meta-Learning: Learning How to Learn Fairly
Data sets for fairness relevant tasks can lack examples or be biased acc...
11/06/2019 ∙ by Dylan Slack, et al. ∙
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How can we fool LIME and SHAP? Adversarial Attacks on Post hoc Explanation Methods
As machine learning black boxes are increasingly being deployed in domai...
11/06/2019 ∙ by Dylan Slack, et al. ∙
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Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data
In this paper, we advocate for the study of fairness techniques in low d...
08/24/2019 ∙ by Dylan Slack, et al. ∙
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Assessing the Local Interpretability of Machine Learning Models
The increasing adoption of machine learning tools has led to calls for a...
02/09/2019 ∙ by Sorelle A. Friedler, et al. ∙
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Dylan Slack
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University of California, Irvine
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Haverford College
