Dylan Slack | DeepAI

DeepAI AI Chat

Featured Co-authors

Bo Dai
123 publications
Yuxin Chen
96 publications
Sameer Singh
72 publications
Chenhao Tan
46 publications
Himabindu Lakkaraju
37 publications
Cedric Archambeau
35 publications
Yinlam Chow
31 publications
Krishnaram Kenthapadi
30 publications
Carlos Scheidegger
20 publications
Michele Donini
18 publications
Muhammad Bilal Zafar
17 publications

∙ 04/25/2023

TABLET: Learning From Instructions For Tabular Data

Acquiring high-quality data is often a significant challenge in training...

0 Dylan Slack, et al. ∙

∙ 07/08/2022

TalkToModel: Understanding Machine Learning Models With Open Ended Dialogues

Machine Learning (ML) models are increasingly used to make critical deci...

0 Dylan Slack, et al. ∙

∙ 02/10/2022

SAFER: Data-Efficient and Safe Reinforcement Learning via Skill Acquisition

Though many reinforcement learning (RL) problems involve learning polici...

7 Dylan Slack, et al. ∙

∙ 02/03/2022

Rethinking Explainability as a Dialogue: A Practitioner's Perspective

As practitioners increasingly deploy machine learning models in critical...

5 Himabindu Lakkaraju, et al. ∙

∙ 06/23/2021

Feature Attributions and Counterfactual Explanations Can Be Manipulated

As machine learning models are increasingly used in critical decision-ma...

0 Dylan Slack, et al. ∙

∙ 06/08/2021

On the Lack of Robust Interpretability of Neural Text Classifiers

With the ever-increasing complexity of neural language models, practitio...

0 Muhammad Bilal Zafar, et al. ∙

∙ 06/04/2021

Counterfactual Explanations Can Be Manipulated

Counterfactual explanations are emerging as an attractive option for pro...

1 Dylan Slack, et al. ∙

∙ 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...

26 Dylan Slack, et al. ∙

∙ 09/13/2020

Differentially Private Language Models Benefit from Public Pre-training

Language modeling is a keystone task in natural language processing. Whe...

0 Gavin Kerrigan, et al. ∙

∙ 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...

24 Dylan Slack, et al. ∙

∙ 11/06/2019

Fair Meta-Learning: Learning How to Learn Fairly

Data sets for fairness relevant tasks can lack examples or be biased acc...

0 Dylan Slack, et al. ∙

∙ 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...

20 Dylan Slack, et al. ∙

∙ 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...

0 Dylan Slack, et al. ∙

∙ 02/09/2019

Assessing the Local Interpretability of Machine Learning Models

The increasing adoption of machine learning tools has led to calls for a...

0 Sorelle A. Friedler, et al. ∙