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2020 – today
- 2024
[i17]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 abs/2405.00332 (2024)- [i16]Vaskar Nath, Dylan Slack, Jeff Da, Yuntao Ma, Hugh Zhang, Spencer Whitehead, Sean Hendryx:
Learning Goal-Conditioned Representations for Language Reward Models. CoRR abs/2407.13887 (2024) - 2023
- [b1]Dylan Slack:
Robust Interactions with Machine Learning Models. University of California, Irvine, USA, 2023 - [j1]Dylan Slack
, Satyapriya Krishna, Himabindu Lakkaraju, Sameer Singh:
Explaining machine learning models with interactive natural language conversations using TalkToModel. Nat. Mac. Intell. 5(8): 873-883 (2023) - [c6]Satyapriya Krishna, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh, Himabindu Lakkaraju:
Post Hoc Explanations of Language Models Can Improve Language Models. NeurIPS 2023 - [i15]Dylan Slack, Sameer Singh:
TABLET: Learning From Instructions For Tabular Data. CoRR abs/2304.13188 (2023) - [i14]Satyapriya Krishna, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh, Himabindu Lakkaraju:
Post Hoc Explanations of Language Models Can Improve Language Models. CoRR abs/2305.11426 (2023) - 2022
- [i13]Himabindu Lakkaraju, Dylan Slack, Yuxin Chen, Chenhao Tan, Sameer Singh:
Rethinking Explainability as a Dialogue: A Practitioner's Perspective. CoRR abs/2202.01875 (2022) - [i12]Dylan Slack, Yinlam Chow, Bo Dai, Nevan Wichers:
SAFER: Data-Efficient and Safe Reinforcement Learning via Skill Acquisition. CoRR abs/2202.04849 (2022) - [i11]Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju, Sameer Singh:
TalkToModel: Understanding Machine Learning Models With Open Ended Dialogues. CoRR abs/2207.04154 (2022) - 2021
- [c5]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: 3730-3740 - [c4]Dylan Slack, Anna Hilgard, Himabindu Lakkaraju, Sameer Singh:
Counterfactual Explanations Can Be Manipulated. NeurIPS 2021: 62-75 - [c3]Dylan Slack, Anna Hilgard, Sameer Singh, Himabindu Lakkaraju:
Reliable Post hoc Explanations: Modeling Uncertainty in Explainability. NeurIPS 2021: 9391-9404 - [i10]Dylan Slack, Nathalie Rauschmayr, Krishnaram Kenthapadi:
Defuse: Harnessing Unrestricted Adversarial Examples for Debugging Models Beyond Test Accuracy. CoRR abs/2102.06162 (2021) - [i9]Dylan Slack, Sophie Hilgard, Himabindu Lakkaraju, Sameer Singh:
Counterfactual Explanations Can Be Manipulated. CoRR abs/2106.02666 (2021) - [i8]Muhammad Bilal Zafar, Michele Donini, Dylan Slack, Cédric Archambeau, Sanjiv Das, Krishnaram Kenthapadi:
On the Lack of Robust Interpretability of Neural Text Classifiers. CoRR abs/2106.04631 (2021) - [i7]Dylan Slack, Sophie Hilgard, Sameer Singh, Himabindu Lakkaraju:
Feature Attributions and Counterfactual Explanations Can Be Manipulated. CoRR abs/2106.12563 (2021) - 2020
- [c2]Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, Himabindu Lakkaraju:
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods. AIES 2020: 180-186 - [c1]Dylan Slack, Sorelle A. Friedler, Emile Givental:
Fairness warnings and fair-MAML: learning fairly with minimal data. FAT* 2020: 200-209 - [i6]Dylan Slack, Sophie Hilgard, Sameer Singh, Himabindu Lakkaraju:
How Much Should I Trust You? Modeling Uncertainty of Black Box Explanations. CoRR abs/2008.05030 (2020) - [i5]Gavin Kerrigan, Dylan Slack, Jens Tuyls:
Differentially Private Language Models Benefit from Public Pre-training. CoRR abs/2009.05886 (2020)
2010 – 2019
- 2019
- [i4]Sorelle A. Friedler, Chitradeep Dutta Roy, Carlos Scheidegger, Dylan Slack:
Assessing the Local Interpretability of Machine Learning Models. CoRR abs/1902.03501 (2019) - [i3]Dylan Slack, Sorelle A. Friedler, Emile Givental:
Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data. CoRR abs/1908.09092 (2019) - [i2]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 abs/1911.02508 (2019) - [i1]Dylan Slack, Sorelle A. Friedler, Emile Givental:
Fair Meta-Learning: Learning How to Learn Fairly. CoRR abs/1911.04336 (2019)
Coauthor Index
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