33 citations · 102 across the 12 of their papers we have counts for
14 papers · 1 filter
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…
Forecasting Emergency Department Capacity Constraints for COVID Isolation Beds
Erik Drysdale, Devin Singh, Anna Goldenberg
Predicting patient volumes in a hospital setting is a well-studied application of time series forecasting. Existing tools usually make forecasts at the daily or weekly level to ass…
Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings
Vinith M. Suriyakumar, Nicolas Papernot, Anna Goldenberg +1
Machine learning models in health care are often deployed in settings where it is important to protect patient privacy. In such settings, methods for differentially private (DP) le…
A Comprehensive Evaluation of Multi-task Learning and Multi-task Pre-training on EHR Time-series Data
Matthew B. A. McDermott, Bret Nestor, Evan Kim +4
Multi-task learning (MTL) is a machine learning technique aiming to improve model performance by leveraging information across many tasks. It has been used extensively on various d…
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…