Assessing the Local Interpretability of Machine Learning Models. - Sorelle A. Friedler; Chitradeep Dutta Roy; Carlos Scheidegger; Dylan Slack (CoRR, 2019)

How can we fool LIME and SHAP? Adversarial Attacks on Post hoc Explanation Methods. - Dylan Slack; Sophie Hilgard; Emily Jia; Sameer Singh 001; Himabindu Lakkaraju (CoRR, 2019)

Fair Meta-Learning: Learning How to Learn Fairly. - Dylan Slack; Sorelle A. Friedler; Emile Givental (CoRR, 2019)

Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods. - Dylan Slack; Sophie Hilgard; Emily Jia; Sameer Singh 001; Himabindu Lakkaraju (AIES, 2020)

Fairness warnings and fair-MAML: learning fairly with minimal data. - Dylan Slack; Sorelle A. Friedler; Emile Givental (FAT*, 2020)

How Much Should I Trust You? Modeling Uncertainty of Black Box Explanations. - Dylan Slack; Sophie Hilgard; Sameer Singh 001; Himabindu Lakkaraju (CoRR, 2020)

Differentially Private Language Models Benefit from Public Pre-training. - Gavin Kerrigan; Dylan Slack; Jens Tuyls (CoRR, 2020)

On the Lack of Robust Interpretability of Neural Text Classifiers. - Muhammad Bilal Zafar; Michele Donini; Dylan Slack; Cédric Archambeau; Sanjiv Das; Krishnaram Kenthapadi (ACL/IJCNLP (Findings), 2021)

Counterfactual Explanations Can Be Manipulated. - Dylan Slack; Anna Hilgard; Himabindu Lakkaraju; Sameer Singh 001 (NeurIPS, 2021)

Reliable Post hoc Explanations: Modeling Uncertainty in Explainability. - Dylan Slack; Anna Hilgard; Sameer Singh 001; Himabindu Lakkaraju (NeurIPS, 2021)

Defuse: Harnessing Unrestricted Adversarial Examples for Debugging Models Beyond Test Accuracy. - Dylan Slack; Nathalie Rauschmayr; Krishnaram Kenthapadi (CoRR, 2021)

Counterfactual Explanations Can Be Manipulated. - Dylan Slack; Sophie Hilgard; Himabindu Lakkaraju; Sameer Singh 001 (CoRR, 2021)

Feature Attributions and Counterfactual Explanations Can Be Manipulated. - Dylan Slack; Sophie Hilgard; Sameer Singh 001; Himabindu Lakkaraju (CoRR, 2021)

Rethinking Explainability as a Dialogue: A Practitioner's Perspective. - Himabindu Lakkaraju; Dylan Slack; Yuxin Chen 001; Chenhao Tan; Sameer Singh 001 (CoRR, 2022)

SAFER: Data-Efficient and Safe Reinforcement Learning via Skill Acquisition. - Dylan Slack; Yinlam Chow; Bo Dai 001; Nevan Wichers (CoRR, 2022)

TalkToModel: Understanding Machine Learning Models With Open Ended Dialogues. - Dylan Slack; Satyapriya Krishna; Himabindu Lakkaraju; Sameer Singh 001 (CoRR, 2022)

Post Hoc Explanations of Language Models Can Improve Language Models. - Satyapriya Krishna; Jiaqi Ma 004; Dylan Slack; Asma Ghandeharioun; Sameer Singh 001; Himabindu Lakkaraju (NeurIPS, 2023)

TABLET: Learning From Instructions For Tabular Data. - Dylan Slack; Sameer Singh 001 (CoRR, 2023)

Robust Interactions with Machine Learning Models - Dylan Slack (2023)

Explaining machine learning models with interactive natural language conversations using TalkToModel. - Dylan Slack; Satyapriya Krishna; Himabindu Lakkaraju; Sameer Singh 001 (Nat. Mac. Intell., 2023)

Learning Goal-Conditioned Representations for Language Reward Models. - Vaskar Nath; Dylan Slack; Jeff Da; Yuntao Ma; Hugh Zhang; Spencer Whitehead; Sean Hendryx (NeurIPS, 2024)

A Careful Examination of Large Language Model Performance on Grade School Arithmetic. - Hugh Zhang; Jeff Da; Dean Lee; Vaughn Robinson; Catherine Wu; William Song; Tiffany Zhao; Pranav Raja; Charlotte Zhuang; Dylan Slack; Qin Lyu; Sean Hendryx; et al. (NeurIPS, 2024)

A Careful Examination of Large Language Model Performance on Grade School Arithmetic. - Hugh Zhang; Jeff Da; Dean Lee; Vaughn Robinson; Catherine Wu; Will Song; Tiffany Zhao; Pranav Raja; Dylan Slack; Qin Lyu; Sean Hendryx; Russell Kaplan; et al. (CoRR, 2024)

Towards physician-centered oversight of conversational diagnostic AI. - Elahe Vedadi; David G. T. Barrett; Natalie Harris; Ellery Wulczyn; Shashir Reddy; Roma Ruparel; Mike Schaekermann; Tim Strother; Ryutaro Tanno; Yash Sharma; Jihyeon Lee; Cían Hughes; et al. (CoRR, 2025)