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Sana Tonekaboni
Sana Tonekaboni
Broad Institute of MIT and Harvard
Verified email at cs.toronto.edu - Homepage
Title
Cited by
Cited by
Year
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
4402019
Unsupervised representation learning for time series with temporal neighborhood coding
S Tonekaboni, D Eytan, A Goldenberg
International Conference on Learning Representations, 2021
2392021
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
1202017
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
682020
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
332018
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
142022
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
102022
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
92022
Explaining time series by counterfactuals
S Tonekaboni, S Joshi, D Duvenaud, A Goldenberg
82019
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
32022
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
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
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
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Articles 1–20