Dylan Slack

According to our database, Dylan Slack authored at least 19 papers between 2019 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Explaining machine learning models with interactive natural language conversations using TalkToModel.

Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju, Sameer Singh

Nat. Mac. Intell., August, 2023

TABLET: Learning From Instructions For Tabular Data.

Dylan Slack, Sameer Singh

CoRR, 2023

Post Hoc Explanations of Language Models Can Improve Language Models.

Satyapriya Krishna, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh, Himabindu Lakkaraju

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

TalkToModel: Understanding Machine Learning Models With Open Ended Dialogues.

Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju, Sameer Singh

CoRR, 2022

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

Dylan Slack, Yinlam Chow, Bo Dai, Nevan Wichers

CoRR, 2022

Rethinking Explainability as a Dialogue: A Practitioner's Perspective.

Himabindu Lakkaraju, Dylan Slack, Yuxin Chen, Chenhao Tan, Sameer Singh

CoRR, 2022

2021

Feature Attributions and Counterfactual Explanations Can Be Manipulated.

Dylan Slack, Sophie Hilgard, Sameer Singh, Himabindu Lakkaraju

CoRR, 2021

Counterfactual Explanations Can Be Manipulated.

Dylan Slack, Sophie Hilgard, Himabindu Lakkaraju, Sameer Singh

CoRR, 2021

Defuse: Harnessing Unrestricted Adversarial Examples for Debugging Models Beyond Test Accuracy.

Dylan Slack, Nathalie Rauschmayr, Krishnaram Kenthapadi

CoRR, 2021

Reliable Post hoc Explanations: Modeling Uncertainty in Explainability.

Dylan Slack, Anna Hilgard, Sameer Singh, Himabindu Lakkaraju

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Counterfactual Explanations Can Be Manipulated.

Dylan Slack, Anna Hilgard, Himabindu Lakkaraju, Sameer Singh

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

On the Lack of Robust Interpretability of Neural Text Classifiers.

Muhammad Bilal Zafar, Michele Donini, Dylan Slack, Cédric Archambeau, Sanjiv Das, Krishnaram Kenthapadi

Proceedings of the Findings of the Association for Computational Linguistics: ACL/IJCNLP 2021, 2021

2020

Differentially Private Language Models Benefit from Public Pre-training.

Gavin Kerrigan, Dylan Slack, Jens Tuyls

CoRR, 2020

How Much Should I Trust You? Modeling Uncertainty of Black Box Explanations.

Dylan Slack, Sophie Hilgard, Sameer Singh, Himabindu Lakkaraju

CoRR, 2020

Fairness warnings and fair-MAML: learning fairly with minimal data.

Dylan Slack, Sorelle A. Friedler, Emile Givental

Proceedings of the FAT* '20: Conference on Fairness, 2020

Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods.

Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, Himabindu Lakkaraju

Proceedings of the AIES '20: AAAI/ACM Conference on AI, 2020

2019

Fair Meta-Learning: Learning How to Learn Fairly.

Dylan Slack, Sorelle A. Friedler, Emile Givental

CoRR, 2019

How can we fool LIME and SHAP? Adversarial Attacks on Post hoc Explanation Methods.

Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, Himabindu Lakkaraju

CoRR, 2019

Assessing the Local Interpretability of Machine Learning Models.

Sorelle A. Friedler, Chitradeep Dutta Roy, Carlos Scheidegger, Dylan Slack

CoRR, 2019