Dylan Slack - RankMe

28 papers A* 5C 1Journal 19Unranked 2

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Towards physician-centered oversight of conversational diagnostic AI. CoRR 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, Dylan Slack, Anil Palepu, Jan Freyberg, Khaled Saab, Valentin Liévin, Wei-Hung Weng, Tao Tu, Yun Liu, Nenad Tomasev, Kavita Kulkarni, S. Sara Mahdavi, Kelvin Guu, Joelle K. Barral, Dale R. Webster, James Manyika, Avinatan Hassidim, Katherine Chou, Yossi Matias, Pushmeet Kohli, Adam Rodman, Vivek Natarajan, Alan Karthikesalingam, David Stutz
2024	A*	`conf`	A Careful Examination of Large Language Model Performance on Grade School Arithmetic. NeurIPS Hugh Zhang, Jeff Da, Dean Lee, Vaughn Robinson, Catherine Wu, William Song, Tiffany Zhao, Pranav Raja, Charlotte Zhuang, Dylan Slack, Qin Lyu, Sean Hendryx, Russell Kaplan, Michele Lunati, Summer Yue
2024	J	`jnl`	A Careful Examination of Large Language Model Performance on Grade School Arithmetic. CoRR 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
2024	A*	`conf`	Learning Goal-Conditioned Representations for Language Reward Models. NeurIPS Vaskar Nath, Dylan Slack, Jeff Da, Yuntao Ma, Hugh Zhang, Spencer Whitehead, Sean Hendryx
2024	J	`jnl`	Learning Goal-Conditioned Representations for Language Reward Models. CoRR Vaskar Nath, Dylan Slack, Jeff Da, Yuntao Ma, Hugh Zhang, Spencer Whitehead, Sean Hendryx
2023	J	`jnl`	Explaining machine learning models with interactive natural language conversations using TalkToModel. Nat. Mac. Intell. Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju, Sameer Singh
2023	A*	`conf`	Post Hoc Explanations of Language Models Can Improve Language Models. NeurIPS Satyapriya Krishna, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh, Himabindu Lakkaraju
2023	J	`jnl`	Post Hoc Explanations of Language Models Can Improve Language Models. CoRR Satyapriya Krishna, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh, Himabindu Lakkaraju
2023	—	`—`	Robust Interactions with Machine Learning Models Dylan Slack
2023	J	`jnl`	TABLET: Learning From Instructions For Tabular Data. CoRR Dylan Slack, Sameer Singh
2022	J	`jnl`	Rethinking Explainability as a Dialogue: A Practitioner's Perspective. CoRR Himabindu Lakkaraju, Dylan Slack, Yuxin Chen, Chenhao Tan, Sameer Singh
2022	J	`jnl`	SAFER: Data-Efficient and Safe Reinforcement Learning via Skill Acquisition. CoRR Dylan Slack, Yinlam Chow, Bo Dai, Nevan Wichers
2022	J	`jnl`	TalkToModel: Understanding Machine Learning Models With Open Ended Dialogues. CoRR Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju, Sameer Singh
2021	A*	`conf`	Counterfactual Explanations Can Be Manipulated. NeurIPS Dylan Slack, Anna Hilgard, Himabindu Lakkaraju, Sameer Singh
2021	J	`jnl`	Counterfactual Explanations Can Be Manipulated. CoRR Dylan Slack, Sophie Hilgard, Himabindu Lakkaraju, Sameer Singh
2021	J	`jnl`	Defuse: Harnessing Unrestricted Adversarial Examples for Debugging Models Beyond Test Accuracy. CoRR Dylan Slack, Nathalie Rauschmayr, Krishnaram Kenthapadi
2021	J	`jnl`	Feature Attributions and Counterfactual Explanations Can Be Manipulated. CoRR Dylan Slack, Sophie Hilgard, Sameer Singh, Himabindu Lakkaraju
2021	—	`conf`	On the Lack of Robust Interpretability of Neural Text Classifiers. ACL/IJCNLP (Findings) Muhammad Bilal Zafar, Michele Donini, Dylan Slack, Cédric Archambeau, Sanjiv Das, Krishnaram Kenthapadi
2021	J	`jnl`	On the Lack of Robust Interpretability of Neural Text Classifiers. CoRR Muhammad Bilal Zafar, Michele Donini, Dylan Slack, Cédric Archambeau, Sanjiv Das, Krishnaram Kenthapadi
2021	A*	`conf`	Reliable Post hoc Explanations: Modeling Uncertainty in Explainability. NeurIPS Dylan Slack, Anna Hilgard, Sameer Singh, Himabindu Lakkaraju
2020	J	`jnl`	Differentially Private Language Models Benefit from Public Pre-training. CoRR Gavin Kerrigan, Dylan Slack, Jens Tuyls
2020	—	`conf`	Fairness warnings and fair-MAML: learning fairly with minimal data. FAT* Dylan Slack, Sorelle A. Friedler, Emile Givental
2020	C	`conf`	Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods. AIES Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, Himabindu Lakkaraju
2020	J	`jnl`	How Much Should I Trust You? Modeling Uncertainty of Black Box Explanations. CoRR Dylan Slack, Sophie Hilgard, Sameer Singh, Himabindu Lakkaraju
2019	J	`jnl`	Assessing the Local Interpretability of Machine Learning Models. CoRR Sorelle A. Friedler, Chitradeep Dutta Roy, Carlos Scheidegger, Dylan Slack
2019	J	`jnl`	Fair Meta-Learning: Learning How to Learn Fairly. CoRR Dylan Slack, Sorelle A. Friedler, Emile Givental
2019	J	`jnl`	Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data. CoRR Dylan Slack, Sorelle A. Friedler, Emile Givental
2019	J	`jnl`	How can we fool LIME and SHAP? Adversarial Attacks on Post hoc Explanation Methods. CoRR Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, Himabindu Lakkaraju