Model agnostic supervised local explanations G Plumb, D Molitor, A Talwalkar NeurIPS 2018, 2018 | 272 | 2018 |
Regularizing black-box models for improved interpretability G Plumb, M Al-Shedivat, AA Cabrera, A Perer, E Xing, A Talwalkar NeurIPS 2020, 2020 | 91* | 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 | 73* | 2022 |
Finding and fixing spurious patterns with explanations G Plumb, MT Ribeiro, A Talwalkar Transactions on Machine Learning Research, 2022 | 47 | 2022 |
Explaining groups of points in low-dimensional representations G Plumb, J Terhorst, S Sankararaman, A Talwalkar ICML 2020, 2020 | 30 | 2020 |
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 | 25* | 2022 |
A Learning Theoretic Perspective on Local Explainability J Li, V Nagarajan, G Plumb, A Talwalkar ICLR 2021, 2021 | 19 | 2021 |
Sanity simulations for saliency methods JS Kim, G Plumb, A Talwalkar Proceedings of the 39th International Conference on Machine Learning, 2021 | 18 | 2021 |
Where Does My Model Underperform? A Human Evaluation of Slice Discovery Algorithms N Johnson, ÁA Cabrera, G Plumb, A Talwalkar ICML 2023: The Second Workshop on Spurious Correlations, Invariance and …, 2023 | 11 | 2023 |
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 | 10 | 2015 |
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 | 7* | 2023 |
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 2017 …, 2017 | | 2017 |