Consumers transfer brand-level regularities across contexts using low-D boundedly rational meta-learning approximations that fit choice data better than no-transfer or fully integrated Bayesian benchmarks.
IEEE Transactions on knowledge and data engineering , volume=
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Language-Induced Priors from LLMs guide source selection in cold-start domain adaptation through an EM algorithm, matching oracle MSE under a correct prior and remaining asymptotically consistent.
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.
FACTOR uses counterfactual image perturbations to quantify and suppress attribute-dependent predictions in open-vocabulary object detection, improving robustness on corrupted datasets without any training.
citing papers explorer
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Boundedly Rational Meta-Learning in Sequential Consumer Choice
Consumers transfer brand-level regularities across contexts using low-D boundedly rational meta-learning approximations that fit choice data better than no-transfer or fully integrated Bayesian benchmarks.
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Language-Induced Priors for Domain Adaptation
Language-Induced Priors from LLMs guide source selection in cold-start domain adaptation through an EM algorithm, matching oracle MSE under a correct prior and remaining asymptotically consistent.
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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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FACTOR: Counterfactual Training-Free Test-Time Adaptation for Open-Vocabulary Object Detection
FACTOR uses counterfactual image perturbations to quantify and suppress attribute-dependent predictions in open-vocabulary object detection, improving robustness on corrupted datasets without any training.
- Sample-efficient inductive matrix completion with noise and inexact side-information
- Query-efficient model evaluation using cached responses