Sana Tonekaboni

According to our database1, Sana Tonekaboni authored at least 24 papers between 2017 and 2025.

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Bibliography

2025
DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection.
CoRR, June, 2025

When Style Breaks Safety: Defending Language Models Against Superficial Style Alignment.
CoRR, June, 2025

Machine Learning for Health symposium 2024 - Findings track.
CoRR, March, 2025

HDP-Flow: Generalizable Bayesian Nonparametric Model for Time Series State Discovery.
Proceedings of the Conference on Uncertainty in Artificial Intelligence, 2025

An Information Criterion for Controlled Disentanglement of Multimodal Data.
Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Learning under Temporal Label Noise.
Proceedings of the Thirteenth International Conference on Learning Representations, 2025

The Latentverse: An Open-Source Benchmarking Toolkit for Evaluating Latent Representations.
Proceedings of the Conference on Health, 2025

2024
A collection of the accepted papers for the Human-Centric Representation Learning workshop at AAAI 2024.
CoRR, 2024

Learning from Time Series under Temporal Label Noise.
CoRR, 2024


2023
Encoding the Underlying Dynamics of Complex Time Series With a Focus on Healthcare Applications
PhD thesis, 2023

Modeling personalized heart rate response to exercise and environmental factors with wearables data.
npj Digit. Medicine, 2023

RiskFix: Supporting Expert Validation of Predictive Timeseries Models in High-Intensity Settings.
Proceedings of the 25th Eurographics Conference on Visualization, 2023

Dynamic Interpretable Change Point Detection for Physiological Data Analysis.
Proceedings of the Machine Learning for Health, 2023

2022
Time-Varying Correlation Networks for Interpretable Change Point Detection.
CoRR, 2022

Learning Unsupervised Representations for ICU Timeseries.
Proceedings of the Conference on Health, Inference, and Learning, 2022

How to validate Machine Learning Models Prior to Deployment: Silent trial protocol for evaluation of real-time models at ICU.
Proceedings of the Conference on Health, Inference, and Learning, 2022

Decoupling Local and Global Representations of Time Series.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding.
Proceedings of the 9th International Conference on Learning Representations, 2021

2020
What went wrong and when? Instance-wise Feature Importance for Time-series Models.
CoRR, 2020

What went wrong and when? Instance-wise feature importance for time-series black-box models.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019
What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use.
Proceedings of the Machine Learning for Healthcare Conference, 2019

2018
Prediction of Cardiac Arrest from Physiological Signals in the Pediatric ICU.
Proceedings of the Machine Learning for Healthcare Conference, 2018

2017
Closed-Loop Neurostimulators: A Survey and A Seizure-Predicting Design Example for Intractable Epilepsy Treatment.
IEEE Trans. Biomed. Circuits Syst., 2017


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