Dylan Slack

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 24

Top Topics

Learning Models

Machine Learning Models

Top Venues

Nat. Mac. Intell.

Publications

Hugh Zhang, Jeff Da, Dean Lee, Vaughn Robinson, Catherine Wu, Will Song, Tiffany Zhao, Pranav Raja, Dylan Slack, Qin Lyu, Sean Hendryx, Russell Kaplan, Michele Lunati, Summer Yue
A Careful Examination of Large Language Model Performance on Grade School Arithmetic. CoRR (2024)
Vaskar Nath, Dylan Slack, Jeff Da, Yuntao Ma, Hugh Zhang, Spencer Whitehead, Sean Hendryx
Learning Goal-Conditioned Representations for Language Reward Models. CoRR (2024)
Satyapriya Krishna, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh, Himabindu Lakkaraju
Post Hoc Explanations of Language Models Can Improve Language Models. NeurIPS (2023)
Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju, Sameer Singh
Explaining machine learning models with interactive natural language conversations using TalkToModel. Nat. Mac. Intell. 5 (8) (2023)
Dylan Slack, Sameer Singh
TABLET: Learning From Instructions For Tabular Data. CoRR (2023)
Satyapriya Krishna, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh, Himabindu Lakkaraju
Post Hoc Explanations of Language Models Can Improve Language Models. CoRR (2023)
Dylan Slack, Yinlam Chow, Bo Dai, Nevan Wichers
SAFER: Data-Efficient and Safe Reinforcement Learning via Skill Acquisition. CoRR (2022)
Himabindu Lakkaraju, Dylan Slack, Yuxin Chen, Chenhao Tan, Sameer Singh
Rethinking Explainability as a Dialogue: A Practitioner's Perspective. CoRR (2022)
Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju, Sameer Singh
TalkToModel: Understanding Machine Learning Models With Open Ended Dialogues. CoRR (2022)
Muhammad Bilal Zafar, Michele Donini, Dylan Slack, Cédric Archambeau, Sanjiv Das, Krishnaram Kenthapadi
On the Lack of Robust Interpretability of Neural Text Classifiers. CoRR (2021)
Dylan Slack, Anna Hilgard, Sameer Singh, Himabindu Lakkaraju
Reliable Post hoc Explanations: Modeling Uncertainty in Explainability. NeurIPS (2021)
Dylan Slack, Sophie Hilgard, Himabindu Lakkaraju, Sameer Singh
Counterfactual Explanations Can Be Manipulated. CoRR (2021)
Dylan Slack, Anna Hilgard, Himabindu Lakkaraju, Sameer Singh
Counterfactual Explanations Can Be Manipulated. NeurIPS (2021)
Dylan Slack, Sophie Hilgard, Sameer Singh, Himabindu Lakkaraju
Feature Attributions and Counterfactual Explanations Can Be Manipulated. CoRR (2021)
Muhammad Bilal Zafar, Michele Donini, Dylan Slack, Cédric Archambeau, Sanjiv Das, Krishnaram Kenthapadi
On the Lack of Robust Interpretability of Neural Text Classifiers. ACL/IJCNLP (Findings) (2021)
Dylan Slack, Nathalie Rauschmayr, Krishnaram Kenthapadi
Defuse: Harnessing Unrestricted Adversarial Examples for Debugging Models Beyond Test Accuracy. CoRR (2021)
Dylan Slack, Sophie Hilgard, Sameer Singh, Himabindu Lakkaraju
How Much Should I Trust You? Modeling Uncertainty of Black Box Explanations. CoRR (2020)
Gavin Kerrigan, Dylan Slack, Jens Tuyls
Differentially Private Language Models Benefit from Public Pre-training. CoRR (2020)
Dylan Slack, Sorelle A. Friedler, Emile Givental
Fairness warnings and fair-MAML: learning fairly with minimal data. FAT* (2020)
Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, Himabindu Lakkaraju
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods. AIES (2020)
Sorelle A. Friedler, Chitradeep Dutta Roy, Carlos Scheidegger, Dylan Slack
Assessing the Local Interpretability of Machine Learning Models. CoRR (2019)
Dylan Slack, Sorelle A. Friedler, Emile Givental
Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data. CoRR (2019)
Dylan Slack, Sorelle A. Friedler, Emile Givental
Fair Meta-Learning: Learning How to Learn Fairly. CoRR (2019)
Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, Himabindu Lakkaraju
How can we fool LIME and SHAP? Adversarial Attacks on Post hoc Explanation Methods. CoRR (2019)