4 citations · 6 across the 6 of their papers we have counts for
6 papers
Counterfactual-Augmented Importance Sampling for Semi-Offline Policy Evaluation
Shengpu Tang, Jenna Wiens
In applying reinforcement learning (RL) to high-stakes domains, quantitative and qualitative evaluation using observational data can help practitioners understand the generalizatio…
Updating Clinical Risk Stratification Models Using Rank-Based Compatibility: Approaches for Evaluating and Optimizing Clinician-Model Team Performance
Erkin Ötleş, Brian T. Denton, Jenna Wiens
As data shift or new data become available, updating clinical machine learning models may be necessary to maintain or improve performance over time. However, updating a model can i…
Leveraging Factored Action Spaces for Off-Policy Evaluation
Aaman Rebello, Shengpu Tang, Jenna Wiens +1
Off-policy evaluation (OPE) aims to estimate the benefit of following a counterfactual sequence of actions, given data collected from executed sequences. However, existing OPE esti…
Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare
Shengpu Tang, Maggie Makar, Michael W. Sjoding +2
Many reinforcement learning (RL) applications have combinatorial action spaces, where each action is a composition of sub-actions. A standard RL approach ignores this inherent fact…
Forecasting with Sparse but Informative Variables: A Case Study in Predicting Blood Glucose
Harry Rubin-Falcone, Joyce Lee, Jenna Wiens
In time-series forecasting, future target values may be affected by both intrinsic and extrinsic effects. When forecasting blood glucose, for example, intrinsic effects can be infe…
Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning
Trenton Chang, Michael W. Sjoding, Jenna Wiens
As machine learning (ML) models gain traction in clinical applications, understanding the impact of clinician and societal biases on ML models is increasingly important. While bias…