5 citations · 12 across the 8 of their papers we have counts for
4 papers · 1 filter
TorchSurv: A Lightweight Package for Deep Survival Analysis
Mélodie Monod, Peter Krusche, Qian Cao +4
TorchSurv is a Python package that serves as a companion tool to perform deep survival modeling within the PyTorch environment. Unlike existing libraries that impose specific param…
Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens
Jean Feng, Adarsh Subbaswamy, Alexej Gossmann +7
After a machine learning (ML)-based system is deployed, monitoring its performance is important to ensure the safety and effectiveness of the algorithm over time. When an ML algori…
Is this model reliable for everyone? Testing for strong calibration
Jean Feng, Alexej Gossmann, Romain Pirracchio +3
In a well-calibrated risk prediction model, the average predicted probability is close to the true event rate for any given subgroup. Such models are reliable across heterogeneous…
Data AUDIT: Identifying Attribute Utility- and Detectability-Induced Bias in Task Models
Mitchell Pavlak, Nathan Drenkow, Nicholas Petrick +2
To safely deploy deep learning-based computer vision models for computer-aided detection and diagnosis, we must ensure that they are robust and reliable. Towards that goal, algorit…