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Gregory Plumb
Gregory Plumb
Verified email at andrew.cmu.edu
Title
Cited by
Cited by
Year
Model agnostic supervised local explanations
G Plumb, D Molitor, A Talwalkar
NeurIPS 2018, 2018
2182018
Regularizing black-box models for improved interpretability
G Plumb, M Al-Shedivat, AA Cabrera, A Perer, E Xing, A Talwalkar
NeurIPS 2020, 2020
80*2020
Interpretable machine learning: Moving from mythos to diagnostics
V Chen, J Li, JS Kim, G Plumb, A Talwalkar
Communications of the ACM 65 (8), 43-50, 2022
51*2022
Finding and fixing spurious patterns with explanations
G Plumb, MT Ribeiro, A Talwalkar
Transactions on Machine Learning Research, 2022
332022
Explaining groups of points in low-dimensional representations
G Plumb, J Terhorst, S Sankararaman, A Talwalkar
ICML 2020, 2020
222020
Use-case-grounded simulations for explanation evaluation
V Chen, N Johnson, N Topin, G Plumb, A Talwalkar
In Advances in Neural Information Processing Systems., 2022
19*2022
A Learning Theoretic Perspective on Local Explainability
J Li, V Nagarajan, G Plumb, A Talwalkar
ICLR 2021, 2021
162021
Sanity simulations for saliency methods
JS Kim, G Plumb, A Talwalkar
Proceedings of the 39th International Conference on Machine Learning, 2021
152021
SnFFT: a Julia toolkit for Fourier analysis of functions over permutations
G Plumb, D Pachauri, R Kondor, V Singh
The Journal of Machine Learning Research 16 (1), 3469-3473, 2015
102015
Towards a More Rigorous Science of Blindspot Discovery in Image Classification Models
G Plumb, N Johnson, A Cabrera, A Talwalkar
Transactions on Machine Learning Research, 2023
4*2023
Where Does My Model Underperform? A Human Evaluation of Slice Discovery Algorithms
N Johnson, ┴A Cabrera, G Plumb, A Talwalkar
ICML 2023: The Second Workshop on Spurious Correlations, Invariance andá…, 2023
12023
Modeling Cognitive Trends in Preclinical Alzheimer’s Disease (AD) via Distributions over Permutations
G Plumb, L Clark, SC Johnson, V Singh
Medical Image Computing and Computer Assisted Intervention− MICCAI 2017á…, 2017
2017
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