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04/25/2023
TABLET: Learning From Instructions For Tabular Data
Acquiring high-quality data is often a significant challenge in training...
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07/08/2022
TalkToModel: Understanding Machine Learning Models With Open Ended Dialogues
Machine Learning (ML) models are increasingly used to make critical deci...
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02/10/2022
SAFER: Data-Efficient and Safe Reinforcement Learning via Skill Acquisition
Though many reinforcement learning (RL) problems involve learning polici...
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02/03/2022
Rethinking Explainability as a Dialogue: A Practitioner's Perspective
As practitioners increasingly deploy machine learning models in critical...
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06/23/2021
Feature Attributions and Counterfactual Explanations Can Be Manipulated
As machine learning models are increasingly used in critical decision-ma...
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06/08/2021
On the Lack of Robust Interpretability of Neural Text Classifiers
With the ever-increasing complexity of neural language models, practitio...
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06/04/2021
Counterfactual Explanations Can Be Manipulated
Counterfactual explanations are emerging as an attractive option for pro...
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02/11/2021
Defuse: Harnessing Unrestricted Adversarial Examples for Debugging Models Beyond Test Accuracy
We typically compute aggregate statistics on held-out test data to asses...
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09/13/2020
Differentially Private Language Models Benefit from Public Pre-training
Language modeling is a keystone task in natural language processing. Whe...
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08/11/2020
How Much Should I Trust You? Modeling Uncertainty of Black Box Explanations
As local explanations of black box models are increasingly being employe...
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11/06/2019
Fair Meta-Learning: Learning How to Learn Fairly
Data sets for fairness relevant tasks can lack examples or be biased acc...
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11/06/2019
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...
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08/24/2019
Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data
In this paper, we advocate for the study of fairness techniques in low d...
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02/09/2019