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Journal Articles
- 2023
[j5]Gregory Plumb, Nari Johnson, Ángel Alexander Cabrera, Ameet Talwalkar:
Towards a More Rigorous Science of Blindspot Discovery in Image Classification Models. Trans. Mach. Learn. Res. 2023 (2023)- 2022
- [j4]Valerie Chen, Jeffrey Li, Joon Sik Kim, Gregory Plumb, Ameet Talwalkar:
Interpretable machine learning: moving from mythos to diagnostics. Commun. ACM 65(8): 43-50 (2022) - [j3]Gregory Plumb, Marco Túlio Ribeiro, Ameet Talwalkar:
Finding and Fixing Spurious Patterns with Explanations. Trans. Mach. Learn. Res. 2022 (2022) - 2021
- [j2]Valerie Chen, Jeffrey Li, Joon Sik Kim, Gregory Plumb, Ameet Talwalkar:
Interpretable Machine Learning: Moving from mythos to diagnostics. ACM Queue 19(6): 28-56 (2021) - 2015
- [j1]Gregory Plumb, Deepti Pachauri, Risi Kondor, Vikas Singh:
SnFFT: a Julia toolkit for Fourier analysis of functions over permutations. J. Mach. Learn. Res. 16: 3469-3473 (2015)
Conference and Workshop Papers
- 2022
- [c7]Joon Sik Kim, Gregory Plumb, Ameet Talwalkar:
Sanity Simulations for Saliency Methods. ICML 2022: 11173-11200 - [c6]Valerie Chen, Nari Johnson, Nicholay Topin, Gregory Plumb, Ameet Talwalkar:
Use-Case-Grounded Simulations for Explanation Evaluation. NeurIPS 2022 - 2021
- [c5]Jeffrey Li, Vaishnavh Nagarajan, Gregory Plumb, Ameet Talwalkar:
A Learning Theoretic Perspective on Local Explainability. ICLR 2021 - 2020
- [c4]Gregory Plumb, Jonathan Terhorst, Sriram Sankararaman, Ameet Talwalkar:
Explaining Groups of Points in Low-Dimensional Representations. ICML 2020: 7762-7771 - [c3]Gregory Plumb, Maruan Al-Shedivat, Ángel Alexander Cabrera, Adam Perer, Eric P. Xing, Ameet Talwalkar:
Regularizing Black-box Models for Improved Interpretability. NeurIPS 2020 - 2018
- [c2]Gregory Plumb, Denali Molitor, Ameet Talwalkar:
Model Agnostic Supervised Local Explanations. NeurIPS 2018: 2520-2529 - 2017
- [c1]Gregory Plumb, Lindsay Clark, Sterling C. Johnson, Vikas Singh:
Modeling Cognitive Trends in Preclinical Alzheimer's Disease (AD) via Distributions over Permutations. MICCAI (3) 2017: 683-691
Informal and Other Publications
- 2023
- [i11]Nari Johnson, Ángel Alexander Cabrera, Gregory Plumb, Ameet Talwalkar:
Where Does My Model Underperform? A Human Evaluation of Slice Discovery Algorithms. CoRR abs/2306.08167 (2023) - 2022
- [i10]Valerie Chen, Nari Johnson, Nicholay Topin, Gregory Plumb, Ameet Talwalkar:
Use-Case-Grounded Simulations for Explanation Evaluation. CoRR abs/2206.02256 (2022) - [i9]Gregory Plumb, Nari Johnson, Ángel Alexander Cabrera, Marco Túlio Ribeiro, Ameet Talwalkar:
Evaluating Systemic Error Detection Methods using Synthetic Images. CoRR abs/2207.04104 (2022) - 2021
- [i8]Valerie Chen, Jeffrey Li, Joon Sik Kim, Gregory Plumb, Ameet Talwalkar:
Towards Connecting Use Cases and Methods in Interpretable Machine Learning. CoRR abs/2103.06254 (2021) - [i7]Joon Sik Kim, Gregory Plumb, Ameet Talwalkar:
Sanity Simulations for Saliency Methods. CoRR abs/2105.06506 (2021) - [i6]Gregory Plumb, Marco Túlio Ribeiro, Ameet Talwalkar:
Finding and Fixing Spurious Patterns with Explanations. CoRR abs/2106.02112 (2021) - 2020
- [i5]Gregory Plumb, Jonathan Terhorst, Sriram Sankararaman, Ameet Talwalkar:
Explaining Groups of Points in Low-Dimensional Representations. CoRR abs/2003.01640 (2020) - [i4]Jeffrey Li, Vaishnavh Nagarajan, Gregory Plumb, Ameet Talwalkar:
A Learning Theoretic Perspective on Local Explainability. CoRR abs/2011.01205 (2020) - 2019
- [i3]Gregory Plumb, Maruan Al-Shedivat, Eric P. Xing, Ameet Talwalkar:
Regularizing Black-box Models for Improved Interpretability. CoRR abs/1902.06787 (2019) - [i2]Gregory Plumb, Maruan Al-Shedivat, Eric P. Xing, Ameet Talwalkar:
Regularizing Black-box Models for Improved Interpretability (HILL 2019 Version). CoRR abs/1906.01431 (2019) - 2018
- [i1]Gregory Plumb, Denali Molitor, Ameet Talwalkar:
Supervised Local Modeling for Interpretability. CoRR abs/1807.02910 (2018)
Coauthor Index
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