Search Results for author: Ian Fox

Found 9 papers, 2 papers with code

Personalized Execution Time Optimization for the Scheduled Jobs

no code implementations11 Mar 2022 Yang Liu, Juan Wang, Zhengxing Chen, Ian Fox, Imani Mufti, Jason Sukumaran, Baokun He, Xiling Sun, Feng Liang

Scheduled batch jobs have been widely used on the asynchronous computing platforms to execute various enterprise applications, including the scheduled notifications and the candidate pre-computation for the modern recommender systems.

Learning-To-Rank Recommendation Systems +1

Deep Reinforcement Learning for Closed-Loop Blood Glucose Control

no code implementations18 Sep 2020 Ian Fox, Joyce Lee, Rodica Pop-Busui, Jenna Wiens

We highlight the flexibility of RL approaches, demonstrating how they can adapt to new individuals with little additional data.

reinforcement-learning Reinforcement Learning (RL)

Learning Through Limited Self-Supervision: Improving Time-Series Classification Without Additional Data via Auxiliary Tasks

no code implementations25 Sep 2019 Ian Fox, Harry Rubin-Falcone, Jenna Wiens

We explore a novel self-supervision framework for time-series data, in which multiple auxiliary tasks (e. g., forecasting) are included to improve overall performance on a sequence-level target task without additional training data.

Time Series Time Series Analysis +1

Deep RL for Blood Glucose Control: Lessons, Challenges, and Opportunities

no code implementations25 Sep 2019 Ian Fox, Joyce Lee, Rodica Busui, Jenna Wiens

We highlight the flexibility of RL approaches, demonstrating how they can adapt to new individuals with little additional data.

Reinforcement Learning (RL)

Advocacy Learning: Learning through Competition and Class-Conditional Representations

no code implementations7 Aug 2019 Ian Fox, Jenna Wiens

Advocacy learning relies on a framework consisting of two connected networks: 1) $N$ Advocates (one for each class), each of which outputs an argument in the form of an attention map over the input, and 2) a Judge, which predicts the class label based on these arguments.

Classification General Classification

Advocacy Learning

no code implementations27 Sep 2018 Ian Fox, Jenna Wiens

In contrast to a standard network, in which all subnetworks are trained to jointly cooperate, we train the Advocates to competitively argue for their class, even when the input belongs to a different class.

Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories

1 code implementation14 Jun 2018 Ian Fox, Lynn Ang, Mamta Jaiswal, Rodica Pop-Busui, Jenna Wiens

Overall, the results suggest the efficacy of our proposed approach in predicting blood glucose level and multi-step forecasting more generally.

The Advantage of Doubling: A Deep Reinforcement Learning Approach to Studying the Double Team in the NBA

1 code implementation8 Mar 2018 Jiaxuan Wang, Ian Fox, Jonathan Skaza, Nick Linck, Satinder Singh, Jenna Wiens

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 stay at home and do the best you can?"

Contextual Motifs: Increasing the Utility of Motifs using Contextual Data

no code implementations6 Mar 2017 Ian Fox, Lynn Ang, Mamta Jaiswal, Rodica Pop-Busui, Jenna Wiens

Applied to both simulated and real physiological data, our proposed approach improves upon existing motif methods in terms of the discriminative utility of the discovered motifs.

Cannot find the paper you are looking for? You can Submit a new open access paper.