← Authors

Jenna Wiens

29 papers · 2010–2026 · 8 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (8) πŸƒ Academic Marathon (15) 🌈 Renaissance Researcher (6) 🐝 Cross-Pollinator (12) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (13) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸ† Keyword Champion πŸ—ƒοΈ Keyword Collector (134) ⚑ Prolific Year (5) πŸ“ˆ Trend Setter πŸ’Ž Century Club (27) πŸ”₯ Unstoppable (8) ❓ The Questioner

Conferences

MLHC (9) NIPS (6) AAAI (5) AISTATS (4) ICML (2) ECCV (1) IJCAI (1) JMLR (1)

Top co-authors

Shengpu Tang (5) Michael Sjoding (4) Maggie Makar (4) Michael W. Sjoding (4) Trenton Chang (4) Jiaxuan Wang (4) Sarah Jabbour (3) Jeeheh Oh (3) Danai Koutra (2) Joyce Lee (2)

Keywords

patient risk stratification (4) reinforcement learning (3) risk stratification (3) shortcut learning (2) off-policy evaluation (2) causal inference (2) spurious correlation (2) deep learning (2) domain knowledge (2) convolutional neural network (2) medical imaging (2) survival analysis (2) domain adaptation (2) mortality prediction (2) offline reinforcement learning (1) algorithmic fairness (1) representation learning (1) feature learning (1) adversarial adaptation (1) active learning (1)

Papers

A Course Correction in Steerability Evaluation: Revealing Miscalibration and Side Effects in LLMs AAAI 2026
Measuring Model Performance in the Presence of an Intervention AAAI 2026
Learning Laplacian Positional Encodings for Heterophilous Graphs AISTATS 2025
Understanding GNNs and Homophily in Dynamic Node Classification AISTATS 2025
DEPICT: Diffusion-Enabled Permutation Importance for Image Classification Tasks ECCV 2024
From Biased Selective Labels to Pseudo-Labels: An Expectation-Maximization Framework for Learning from Biased Decisions ICML 2024
Who’s Gaming the System? A Causally-Motivated Approach for Detecting Strategic Adaptation NIPS 2024
Learning to Rank for Optimal Treatment Allocation Under Resource Constraints AISTATS 2024
Forecasting with Sparse but Informative Variables: A Case Study in Predicting Blood Glucose AAAI 2023
Updating Clinical Risk Stratification Models Using Rank-Based Compatibility: Approaches for Evaluating and Optimizing Clinician-Model Team Performance MLHC 2023
Counterfactual-Augmented Importance Sampling for Semi-Offline Policy Evaluation NIPS 2023
Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare NIPS 2022
Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning MLHC 2022
Learning Concept Credible Models for Mitigating Shortcuts NIPS 2022
Mind the Performance Gap: Examining Dataset Shift During Prospective Validation MLHC 2021
Estimating Calibrated Individualized Survival Curves with Deep Learning AAAI 2021
A Hierarchical Approach to Multi-Event Survival Analysis AAAI 2021
Shapley Flow: A Graph-based Approach to Interpreting Model Predictions AISTATS 2021
Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare Settings MLHC 2021
Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies ICML 2020
Deep Reinforcement Learning for Closed-Loop Blood Glucose Control MLHC 2020
Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts MLHC 2020
Advocacy Learning: Learning through Competition and Class-Conditional Representations IJCAI 2019
Relaxed Parameter Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Series MLHC 2019
Learning to Exploit Invariances in Clinical Time-Series Data using Sequence Transformer Networks MLHC 2018
A Domain Guided CNN Architecture for Predicting Age from Structural Brain Images MLHC 2018
Patient Risk Stratification with Time-Varying Parameters: A Multitask Learning Approach JMLR 2016
Patient Risk Stratification for Hospital-Associated C. diff as a Time-Series Classification Task NIPS 2012
Active Learning Applied to Patient-Adaptive Heartbeat Classification NIPS 2010