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Found 8 papers, 6 papers with code
Randomized Smoothing of All Shapes and Sizes
Randomized smoothing is the current state-of-the-art defense with provable robustness against $\ell_2$ adversarial attacks.
Missingness as Stability: Understanding the Structure of Missingness in Longitudinal EHR data and its Impact on Reinforcement Learning in Healthcare
There is an emerging trend in the reinforcement learning for healthcare literature.
Graph Embedding VAE: A Permutation Invariant Model of Graph Structure
Generative models of graph structure have applications in biology and social sciences.
NGBoost: Natural Gradient Boosting for Probabilistic Prediction
NGBoost generalizes gradient boosting to probabilistic regression by treating the parameters of the conditional distribution as targets for a multiparameter boosting algorithm.
Counterfactual Reasoning for Fair Clinical Risk Prediction
We investigate the extent to which the augmented counterfactual fairness criteria may be applied to develop fair models for prolonged inpatient length of stay and mortality with observational electronic health records data.
Countdown Regression: Sharp and Calibrated Survival Predictions
Probabilistic survival predictions from models trained with Maximum Likelihood Estimation (MLE) can have high, and sometimes unacceptably high variance.
MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs
To evaluate models robustly and to get an estimate of radiologist performance, we collect additional labels from six board-certified Stanford radiologists on the test set, consisting of 207 musculoskeletal studies.
CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
We develop an algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists.
Ranked #3 on
Pneumonia Detection
on ChestX-ray14
