61 citations · 110 across the 6 of their papers we have counts for
6 papers
Personalized Execution Time Optimization for Billion-Scale Scheduled Jobs
Yang Liu, Juan Wang, Idris Malik +7
Scheduled batch jobs are widely used on asynchronous computing platforms to execute enterprise applications such as promotional notifications and candidate pre-computation for reco…
Deep Reinforcement Learning for Closed-Loop Blood Glucose Control
Ian Fox, Joyce Lee, Rodica Pop-Busui +1
People with type 1 diabetes (T1D) lack the ability to produce the insulin their bodies need. As a result, they must continually make decisions about how much insulin to self-admini…
Advocacy Learning: Learning through Competition and Class-Conditional Representations
Ian Fox, Jenna Wiens
We introduce advocacy learning, a novel supervised training scheme for attention-based classification problems. Advocacy learning relies on a framework consisting of two connected…
Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories
Ian Fox, Lynn Ang, Mamta Jaiswal +2
In many forecasting applications, it is valuable to predict not only the value of a signal at a certain time point in the future, but also the values leading up to that point. This…
The Advantage of Doubling: A Deep Reinforcement Learning Approach to Studying the Double Team in the NBA
Jiaxuan Wang, Ian Fox, Jonathan Skaza +3
During the 2017 NBA playoffs, Celtics coach Brad Stevens was faced with a difficult decision when defending against the Cavaliers: "Do you double and risk giving up easy shots, or…
Contextual Motifs: Increasing the Utility of Motifs using Contextual Data
Ian Fox, Lynn Ang, Mamta Jaiswal +2
Motifs are a powerful tool for analyzing physiological waveform data. Standard motif methods, however, ignore important contextual information (e.g., what the patient was doing at…