MediEncoder jointly learns nonlinear low-dimensional covariate and mediator representations via a coupled encoder-decoder with cross-factor network, then applies them in an efficient influence function estimator for natural direct and indirect effects.
Journal of the Royal Statistical Society Series B: Statistical Methodology , volume=
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Text embeddings recover 57-63% of the reliable variance in exam-item difficulty, and apparent differences in predictability across IRT parameters are mostly artifacts of calibration noise rather than text signal.
spBART extends BART by modeling low-dimensional covariates parametrically for interpretability and high-dimensional epigenetic predictors nonparametrically, with a CV-based variable selection procedure, achieving AUC 0.96 on multiple myeloma epigenetic data.
PerturbedVAE disentangles perturbation-specific signals from invariant gene expression structure to recover causal representations and improve out-of-distribution prediction in single-cell perturbation modeling.
Three-average primal-dual methods achieve accelerated rates for computable accuracy certificates in convex optimization.
Frontal and fronto-central EEG regions show the most consistent predictive utility for cognitive workload in subject-independent settings, outperforming full-scalp baselines by 15-20% in relative rank across datasets.
citing papers explorer
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MediEncoder: Nonlinear Representation Learning for High-Dimensional Causal Mediation Analysis
MediEncoder jointly learns nonlinear low-dimensional covariate and mediator representations via a coupled encoder-decoder with cross-factor network, then applies them in an efficient influence function estimator for natural direct and indirect effects.
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From Text to Parameters: Predicting Item Parameters from Embedding Regularization with Reliability and Design Ceilings
Text embeddings recover 57-63% of the reliable variance in exam-item difficulty, and apparent differences in predictability across IRT parameters are mostly artifacts of calibration noise rather than text signal.
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Semi-Parametric Bayesian Additive Regression Trees for Risk Prediction with High-Dimensional Epigenetic Signatures and Low-Dimensional Covariates
spBART extends BART by modeling low-dimensional covariates parametrically for interpretability and high-dimensional epigenetic predictors nonparametrically, with a CV-based variable selection procedure, achieving AUC 0.96 on multiple myeloma epigenetic data.
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What Makes a Representation Good for Single-Cell Perturbation Prediction?
PerturbedVAE disentangles perturbation-specific signals from invariant gene expression structure to recover causal representations and improve out-of-distribution prediction in single-cell perturbation modeling.
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Accuracy Certificates for Convex Optimization at Accelerated Rates via Primal-Dual Averaging
Three-average primal-dual methods achieve accelerated rates for computable accuracy certificates in convex optimization.
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Assessing Region-Level EEG Contributions to Cognitive Workload Prediction
Frontal and fronto-central EEG regions show the most consistent predictive utility for cognitive workload in subject-independent settings, outperforming full-scalp baselines by 15-20% in relative rank across datasets.
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