arXiv:2510.00463v4 Announce Type: replace-cross
Abstract: This paper studies the adversarial robustness of conformal novelty detection. In particular, we focus on two powerful learning-based frameworks that come with finite-sample false discovery rate…
arXiv:2510.18520v2 Announce Type: replace
Abstract: The ROC curve is widely used to assess binary classifiers. Yet for some applications, such as alert systems for monitoring hospitalized patients, conventional ROC analysis cannot meet two key deploym…
arXiv:2509.07013v3 Announce Type: replace
Abstract: Agent-based models (ABMs) are widely used to study infectious disease dynamics, but their calibration is often computationally intensive, limiting their applicability in time-sensitive public health …
arXiv:2604.01946v1 Announce Type: new
Abstract: Estimating optimal individualized treatment rules (ITRs) via outcome weighted learning (OWL) often relies on observed rewards that are noisy or optimistic proxies for the true latent utility. Ignoring th…
arXiv:2604.01339v1 Announce Type: new
Abstract: Vision transformers (ViT) rely on attention mechanism to weigh input features, and therefore attention scores have naturally been considered as explanations for its decision-making process. However, atte…
arXiv:2412.10683v3 Announce Type: replace-cross
Abstract: Parametric Bayesian modeling offers a powerful and flexible toolbox for machine learning. Yet the model, however detailed, may still be wrong, and this can make inferences untrustworthy. In thi…
arXiv:2604.01501v1 Announce Type: cross
Abstract: Causal mediation analysis provides techniques for defining and estimating effects that may be endowed with mechanistic interpretations. With many scientific investigations seeking to address mechanisti…
arXiv:2406.08097v2 Announce Type: replace
Abstract: Nonlinear dimensional reduction with the manifold assumption, often called manifold learning, has proven its usefulness in a wide range of high-dimensional data analysis. The significant impact of t-…
arXiv:2509.07013v2 Announce Type: replace
Abstract: Calibrating agent-based epidemic models is computationally demanding. We present a supervised machine learning calibrator that learns the inverse mapping from epidemic time series to SIR parameters. …
arXiv:2604.00811v1 Announce Type: new
Abstract: Overlap, also known as positivity, is a key condition for causal treatment effect estimation. Many popular estimators suffer from high variance and become brittle when features differ strongly across tre…