Initiates finite-sample theory for differentially private hypothesis testing in survival analysis, with private tests for Cox models and cumulative hazards plus minimax bounds.
Journal of the Royal Statistical Society: Series B (Methodological) , volume=
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Adapts bandit algorithms to the Cox PH survival model for online treatment optimization under censoring, with theoretical sublinear regret and validation on simulations plus SEER cancer data.
Derives new analytical sample size and power formulas for marginal hazard ratios in causal inference with time-to-event outcomes, applicable to randomized trials and observational studies via IPW estimators.
HEXST applies a hexagonal shifted-window Transformer with rotary positional encodings, contrast-sensitive training objectives, and single-cell priors to predict gene expression from histology slides, outperforming prior models on seven datasets while preserving spatial heterogeneity.
FedAvg DeepSurv across Lifelines (n=148k, self-report) and Rotterdam Study (n=10k, linked outcomes) raised C-statistics from 0.728 to 0.739 and 0.783 to 0.787 versus local training.
citing papers explorer
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Differentially private hypothesis testing in survival analysis
Initiates finite-sample theory for differentially private hypothesis testing in survival analysis, with private tests for Cox models and cumulative hazards plus minimax bounds.
-
Online Survival Analysis: A Bandit Approach under Cox PH Model
Adapts bandit algorithms to the Cox PH survival model for online treatment optimization under censoring, with theoretical sublinear regret and validation on simulations plus SEER cancer data.
-
Sample size and power calculations for causal inference with time-to-event outcomes
Derives new analytical sample size and power formulas for marginal hazard ratios in causal inference with time-to-event outcomes, applicable to randomized trials and observational studies via IPW estimators.
-
HEXST: Hexagonal Shifted-Window Transformer for Spatial Transcriptomics Gene Expression Prediction
HEXST applies a hexagonal shifted-window Transformer with rotary positional encodings, contrast-sensitive training objectives, and single-cell priors to predict gene expression from histology slides, outperforming prior models on seven datasets while preserving spatial heterogeneity.
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Federated Deep Learning for Privacy-Preserving Cardiovascular Disease Risk Prediction
FedAvg DeepSurv across Lifelines (n=148k, self-report) and Rotterdam Study (n=10k, linked outcomes) raised C-statistics from 0.728 to 0.739 and 0.783 to 0.787 versus local training.