Search Results for author:
Found 12 papers, 6 papers with code
Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning
Anomaly, or out-of-distribution, detection is a promising tool for aiding discoveries of new particles or processes in particle physics.
Don't blame Dataset Shift! Shortcut Learning due to Gradients and Cross Entropy
However, even when the stable feature determines the label in the training distribution and the shortcut does not provide any additional information, like in perception tasks, default-ERM still exhibits shortcut learning.
When More is Less: Incorporating Additional Datasets Can Hurt Performance By Introducing Spurious Correlations
In machine learning, incorporating more data is often seen as a reliable strategy for improving model performance; this work challenges that notion by demonstrating that the addition of external datasets in many cases can hurt the resulting model's performance.
A dynamic risk score for early prediction of cardiogenic shock using machine learning
We developed a deep learning-based risk stratification tool, called CShock, for patients admitted into the cardiac ICU with acute decompensated heart failure and/or myocardial infarction to predict onset of cardiogenic shock.
Beyond Distribution Shift: Spurious Features Through the Lens of Training Dynamics
(3) We empirically show that the harmful spurious features can be detected by observing the learning dynamics of the DNN's early layers.
Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation
In prediction tasks, there exist features that are related to the label in the same way across different settings for that task; these are semantic features or semantics.
New-Onset Diabetes Assessment Using Artificial Intelligence-Enhanced Electrocardiography
We found ECG-based assessment outperforms the ADA Risk test, achieving a higher area under the curve (0. 80 vs. 0. 68) and positive predictive value (13% vs. 9%) -- 2. 6 times the prevalence of diabetes in the cohort.
Learning Invariant Representations with Missing Data
Enforcing such independencies requires nuisances to be observed during training.
Inverse-Weighted Survival Games
When the loss is proper, we show that the games always have the true failure and censoring distributions as a stationary point.
Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations
NURD finds a representation from this set that is most informative of the label under the nuisance-randomized distribution, and we prove that this representation achieves the highest performance regardless of the nuisance-label relationship.
Causal Estimation with Functional Confounders
Causal inference relies on two fundamental assumptions: ignorability and positivity.
X-CAL: Explicit Calibration for Survival Analysis
A survival model's calibration can be measured using, for instance, distributional calibration (D-CALIBRATION) [Haider et al., 2020] which computes the squared difference between the observed and predicted number of events within different time intervals.
