3.2k citations · 4.2k across the 46 of their papers we have counts for
62 papers
On Learning Prediction-Focused Mixtures
Abhishek Sharma, Catherine Zeng, Sanjana Narayanan +2
Probabilistic models help us encode latent structures that both model the data and are ideally also useful for specific downstream tasks. Among these, mixture models and their time…
Learning Predictive and Interpretable Timeseries Summaries from ICU Data
Nari Johnson, Sonali Parbhoo, Andrew Slavin Ross +1
Machine learning models that utilize patient data across time (rather than just the most recent measurements) have increased performance for many risk stratification tasks in the i…
Comparison and Unification of Three Regularization Methods in Batch Reinforcement Learning
Sarah Rathnam, Susan A. Murphy, Finale Doshi-Velez
In batch reinforcement learning, there can be poorly explored state-action pairs resulting in poorly learned, inaccurate models and poorly performing associated policies. Various r…
State Relevance for Off-Policy Evaluation
Simon P. Shen, Yecheng Jason Ma, Omer Gottesman +1
Importance sampling-based estimators for off-policy evaluation (OPE) are valued for their simplicity, unbiasedness, and reliance on relatively few assumptions. However, the varianc…
Online structural kernel selection for mobile health
Eura Shin, Pedja Klasnja, Susan Murphy +1
Motivated by the need for efficient and personalized learning in mobile health, we investigate the problem of online kernel selection for Gaussian Process regression in the multi-t…
Promises and Pitfalls of Black-Box Concept Learning Models
Anita Mahinpei, Justin Clark, Isaac Lage +2
Machine learning models that incorporate concept learning as an intermediate step in their decision making process can match the performance of black-box predictive models while re…