| Model agnostic supervised local explanations G Plumb, D Molitor, A Talwalkar NeurIPS 2018, 2018 | 166 | 2018 |
| Regularizing black-box models for improved interpretability G Plumb, M Al-Shedivat, AA Cabrera, A Perer, E Xing, A Talwalkar NeurIPS 2020, 2020 | 55* | 2020 |
| Interpretable machine learning: Moving from mythos to diagnostics V Chen, J Li, JS Kim, G Plumb, A Talwalkar Queue 19 (6), 28-56, 2022 | 35* | 2022 |
| Explaining groups of points in low-dimensional representations G Plumb, J Terhorst, S Sankararaman, A Talwalkar ICML 2020, 2020 | 15 | 2020 |
| Finding and fixing spurious patterns with explanations G Plumb, MT Ribeiro, A Talwalkar arXiv preprint arXiv:2106.02112, 2021 | 14 | 2021 |
| A Learning Theoretic Perspective on Local Explainability J Li, V Nagarajan, G Plumb, A Talwalkar ICLR 2021, 2021 | 13 | 2021 |
| Sanity simulations for saliency methods JS Kim, G Plumb, A Talwalkar arXiv preprint arXiv:2105.06506, 2021 | 9 | 2021 |
| 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 | 7* | 2015 |
| Use-case-grounded simulations for explanation evaluation V Chen, N Johnson, N Topin, G Plumb, A Talwalkar arXiv preprint arXiv:2206.02256, 2022 | 4 | 2022 |
| Evaluating Systemic Error Detection Methods using Synthetic Images G Plumb, N Johnson, ÁA Cabrera, MT Ribeiro, A Talwalkar arXiv preprint arXiv:2207.04104, 2022 | 3 | 2022 |
| Simulated user studies for explanation evaluation V Chen, G Plumb, N Topin, A Talwalkar eXplainable AI approaches for debugging and diagnosis., 2021 | 1 | 2021 |
| Towards a More Rigorous Science of Blindspot Discovery in Image Models G Plumb, N Johnson, A Cabrera, A Talwalkar | | 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 |
