261 citations · 310 across the 7 of their papers we have counts for
7 papers
DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection
Tina Behrouzi, Sana Tonekaboni, Rahul G. Krishnan +1
Real-world observational data often contain existing or emerging heterogeneous subpopulations that deviate from global patterns. The majority of models tend to overlook these under…
Learning under Temporal Label Noise
Sujay Nagaraj, Walter Gerych, Sana Tonekaboni +3
Many time series classification tasks, where labels vary over time, are affected by label noise that also varies over time. Such noise can cause label quality to improve, worsen, o…
Dynamic Interpretable Change Point Detection
Kopal Garg, Jennifer Yu, Tina Behrouzi +2
Identifying change points (CPs) in a time series is crucial to guide better decision making across various fields like finance and healthcare and facilitating timely responses to p…
Decoupling Local and Global Representations of Time Series
Sana Tonekaboni, Chun-Liang Li, Sercan Arik +2
Real-world time series data are often generated from several sources of variation. Learning representations that capture the factors contributing to this variability enables a bett…
Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding
Sana Tonekaboni, Danny Eytan, Anna Goldenberg
Time series are often complex and rich in information but sparsely labeled and therefore challenging to model. In this paper, we propose a self-supervised framework for learning ge…
What went wrong and when? Instance-wise Feature Importance for Time-series Models
Sana Tonekaboni, Shalmali Joshi, Kieran Campbell +2
Explanations of time series models are useful for high stakes applications like healthcare but have received little attention in machine learning literature. We propose FIT, a fram…
What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use
Sana Tonekaboni, Shalmali Joshi, Melissa D McCradden +1
Translating machine learning (ML) models effectively to clinical practice requires establishing clinicians' trust. Explainability, or the ability of an ML model to justify its outc…