Exploring Practitioner Perspectives On Training Data Attribution
Explanations

Exploring Practitioner Perspectives On Training Data Attribution Explanations

31 October 2023

Evgenii Kortukov

Papers citing "Exploring Practitioner Perspectives On Training Data Attribution Explanations"

19 / 19 papers shown

Title
TRAK: Attributing Model Behavior at Scale Sung Min Park Kristian Georgiev Andrew Ilyas Guillaume Leclerc Aleksander Madry TDI 49 138 0 24 Mar 2023
Training Data Influence Analysis and Estimation: A Survey Zayd Hammoudeh Daniel Lowd TDI 42 89 0 09 Dec 2022
Robust Speech Recognition via Large-Scale Weak Supervision Alec Radford Jong Wook Kim Tao Xu Greg Brockman C. McLeavey Ilya Sutskever OffRL 89 3,442 0 06 Dec 2022
Datamodels: Predicting Predictions from Training Data Andrew Ilyas Sung Min Park Logan Engstrom Guillaume Leclerc Aleksander Madry TDI 71 133 0 01 Feb 2022
Scaling Up Influence Functions Andrea Schioppa Polina Zablotskaia David Vilar Artem Sokolov TDI 56 95 0 06 Dec 2021
The Who in XAI: How AI Background Shapes Perceptions of AI Explanations Upol Ehsan Samir Passi Q. V. Liao Larry Chan I-Hsiang Lee Michael J. Muller Mark O. Riedl 39 88 0 28 Jul 2021
Combining Feature and Instance Attribution to Detect Artifacts Pouya Pezeshkpour Sarthak Jain Sameer Singh Byron C. Wallace TDI 44 43 0 01 Jul 2021
Interactive Label Cleaning with Example-based Explanations Stefano Teso A. Bontempelli Fausto Giunchiglia Andrea Passerini 43 45 0 07 Jun 2021
FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging Han Guo Nazneen Rajani Peter Hase Joey Tianyi Zhou Caiming Xiong TDI 64 104 0 31 Dec 2020
Influence Functions in Deep Learning Are Fragile S. Basu Phillip E. Pope Soheil Feizi TDI 69 223 0 25 Jun 2020
Estimating Training Data Influence by Tracing Gradient Descent G. Pruthi Frederick Liu Mukund Sundararajan Satyen Kale TDI 21 389 0 19 Feb 2020
Human-centered Explainable AI: Towards a Reflective Sociotechnical Approach Upol Ehsan Mark O. Riedl 22 215 0 04 Feb 2020
Questioning the AI: Informing Design Practices for Explainable AI User Experiences Q. V. Liao D. Gruen Sarah Miller 63 707 0 08 Jan 2020
On the Accuracy of Influence Functions for Measuring Group Effects Pang Wei Koh Kai-Siang Ang H. Teo Percy Liang TDI 25 186 0 30 May 2019
Understanding the Origins of Bias in Word Embeddings Marc-Etienne Brunet Colleen Alkalay-Houlihan Ashton Anderson R. Zemel FaML 26 200 0 08 Oct 2018
A Survey Of Methods For Explaining Black Box Models Riccardo Guidotti A. Monreale Salvatore Ruggieri Franco Turini D. Pedreschi F. Giannotti XAI 52 3,916 0 06 Feb 2018
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 25 21,372 0 22 May 2017
Understanding Black-box Predictions via Influence Functions Pang Wei Koh Percy Liang TDI 74 2,849 0 14 Mar 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 284 3,728 0 28 Feb 2017