1 citations · 2 across the 7 of their papers we have counts for
6 papers · 1 filter
SurvivalPFN: Amortizing Survival Prediction via In-Context Bayesian Inference
Shi-ang Qi, Vahid Balazadeh, Michael Cooper +2
Survival analysis provides a powerful statistical framework for modeling time-to-event outcomes in the presence of censoring. However, selecting an appropriate estimator from the m…
Mitigating Privacy Risk via Forget Set-Free Unlearning
Aviraj Newatia, Michael Cooper, Viet Nguyen +1
Training machine learning models requires the storage of large datasets, which often contain sensitive or private data. Storing data is associated with a number of potential risks…
Diverse Prototypical Ensembles Improve Robustness to Subpopulation Shift
Minh Nguyen Nhat To, Paul F RWilson, Viet Nguyen +6
The subpopulationtion shift, characterized by a disparity in subpopulation distributibetween theween the training and target datasets, can significantly degrade the performance of…
Predicting Long-Term Allograft Survival in Liver Transplant Recipients
Xiang Gao, Michael Cooper, Maryam Naghibzadeh +3
Liver allograft failure occurs in approximately 20% of liver transplant recipients within five years post-transplant, leading to mortality or the need for retransplantation. Provid…
InterpreTabNet: Distilling Predictive Signals from Tabular Data by Salient Feature Interpretation
Jacob Si, Wendy Yusi Cheng, Michael Cooper +1
Tabular data are omnipresent in various sectors of industries. Neural networks for tabular data such as TabNet have been proposed to make predictions while leveraging the attention…
Copula-Based Deep Survival Models for Dependent Censoring
Ali Hossein Gharari Foomani, Michael Cooper, Russell Greiner +1
A survival dataset describes a set of instances (e.g. patients) and provides, for each, either the time until an event (e.g. death), or the censoring time (e.g. when lost to follow…