Seuraa
Sana Tonekaboni
Sana Tonekaboni
University of Toronto, Vector Institute
Vahvistettu sähköpostiosoite verkkotunnuksessa cs.toronto.edu - Kotisivu
Nimike
Viittaukset
Viittaukset
Vuosi
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
4022019
Unsupervised representation learning for time series with temporal neighborhood coding
S Tonekaboni, D Eytan, A Goldenberg
International Conference on Learning Representations, 2021
1912021
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
1162017
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
592020
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
312018
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
122022
Explaining time series by counterfactuals
S Tonekaboni, S Joshi, D Duvenaud, A Goldenberg
82019
Proceedings of the 4th Machine Learning for Healthcare Conference
S Tonekaboni, S Joshi, MD McCradden, F Doshi-Velez, J Fackler, K Jung
PMLR, Ann Arbor, Michigan, 2019
82019
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
72022
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
52022
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
22022
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
Encoding the Underlying Dynamics of Complex Time Series With a Focus on Healthcare Applications
S Tonekaboni
University of Toronto (Canada), 2023
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, ...
The Eurographics Association, 2023
2023
Time-Varying Correlation Networks for Interpretable Change Point Detection
K Garg, S Tonekaboni, A Goldenberg
arXiv preprint arXiv:2211.03991, 2022
2022
ISCAS 2016 SPECIAL ISSUE
G Wang, MD Poscente, SS Park, CN Andrews, O Yadid-Pecht, ...
Järjestelmä ei voi suorittaa toimenpidettä nyt. Yritä myöhemmin uudelleen.
Artikkelit 1–19