| What clinicians want: contextualizing explainable machine learning for clinical end use S Tonekaboni, S Joshi, MD McCradden, A Goldenberg Machine Learning for Healthcare Conference, 359-380, 2019 | 449 | 2019 |
| Unsupervised representation learning for time series with temporal neighborhood coding S Tonekaboni, D Eytan, A Goldenberg International Conference on Learning Representations, 2021 | 251 | 2021 |
| Closed-loop neurostimulators: A survey and a seizure-predicting design example for intractable epilepsy treatment H Kassiri, S Tonekaboni, MT Salam, N Soltani, K Abdelhalim, ... IEEE transactions on biomedical circuits and systems 11 (5), 1026-1040, 2017 | 120 | 2017 |
| What went wrong and when? Instance-wise feature importance for time-series black-box models S Tonekaboni, S Joshi, K Campbell, DK Duvenaud, A Goldenberg Advances in Neural Information Processing Systems 33, 2020 | 73 | 2020 |
| Prediction of cardiac arrest from physiological signals in the pediatric ICU S Tonekaboni, M Mazwi, P Laussen, D Eytan, R Greer, SD Goodfellow, ... Machine Learning for Healthcare Conference, 534-550, 2018 | 33 | 2018 |
| Decoupling local and global representations of time series S Tonekaboni, CL Li, SO Arik, A Goldenberg, T Pfister International Conference on Artificial Intelligence and Statistics, 8700-8714, 2022 | 16 | 2022 |
| Learning unsupervised representations for icu timeseries A Weatherhead, R Greer, MA Moga, M Mazwi, D Eytan, A Goldenberg, ... Conference on Health, Inference, and Learning, 152-168, 2022 | 11 | 2022 |
| How to validate machine learning models prior to deployment: silent trial protocol for evaluation of real-time models at ICU S Tonekaboni, G Morgenshtern, A Assadi, A Pokhrel, X Huang, ... Conference on Health, Inference, and Learning, 169-182, 2022 | 9 | 2022 |
| Explaining time series by counterfactuals S Tonekaboni, S Joshi, D Duvenaud, A Goldenberg | 8 | 2019 |
| Modeling Heart Rate Response to Exercise with Wearables Data A Nazaret, S Tonekaboni, G Darnell, S Ren, G Sapiro, A Miller NeurIPS 2022 Workshop on Learning from Time Series for Health, 2022 | 3 | 2022 |
| Modeling personalized heart rate response to exercise and environmental factors with wearables data A Nazaret, S Tonekaboni, G Darnell, SY Ren, G Sapiro, AC Miller NPJ Digital Medicine 6 (1), 207, 2023 | 1 | 2023 |
| A collection of the accepted papers for the Human-Centric Representation Learning workshop at AAAI 2024 D Spathis, A Saeed, A Etemad, S Tonekaboni, S Laskaridis, S Deldari, ... arXiv preprint arXiv:2403.10561, 2024 | | 2024 |
| Learning from Time Series under Temporal Label Noise S Nagaraj, W Gerych, S Tonekaboni, A Goldenberg, B Ustun, ... arXiv preprint arXiv:2402.04398, 2024 | | 2024 |
| Dynamic Interpretable Change Point Detection for Physiological Data Analysis J Yu, T Behrouzi, K Garg, A Goldenberg, S Tonekaboni Machine Learning for Health (ML4H), 636-649, 2023 | | 2023 |
| Learning with Temporal Label Noise S Nagaraj, W Gerych, S Tonekaboni, A Goldenberg, B Ustun, ... | | 2023 |
| RiskFix: Supporting Expert Validation of Predictive Timeseries Models in High-Intensity Settings G Morgenshtern, A Verma, S Tonekaboni, R Greer, J Bernard, M Mazwi, ... EuroVis 2023-Short Papers, 13-17, 2023 | | 2023 |
| Encoding the Underlying Dynamics of Complex Time Series With a Focus on Healthcare Applications S Tonekaboni University of Toronto (Canada), 2023 | | 2023 |
| Dynamic Interpretable Change Point Detection K Garg, J Yu, T Behrouzi, S Tonekaboni, A Goldenberg arXiv preprint arXiv:2211.03991, 2022 | | 2022 |
| Time-Varying Correlation Networks for Interpretable Change Point Detection. K Garg, S Tonekaboni, A Goldenberg CoRR, 2022 | | 2022 |
| Workshop on Learning from Time Series for Health S Nagaraj, W Gerych, S Tonekaboni, T Hartvigsen, A Alaa, E Fox, ... ICLR 2024 Workshops, 0 | | |
Anna GoldenbergScientist, SickKids Research Institute; Assistant Professor Department of Computer Science, University of Toronto在 utoronto.ca 的电子邮件经过验证
