ProjectionTL is a hierarchical Bayesian plus adaptive projection framework that performs simultaneous source selection and feature selection to mitigate negative transfer in cross-domain learning.
H., Russo, M., and Paul, S
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Tutorial on a statistical roadmap and R packages for selective borrowing in hybrid controlled trials, demonstrated on synthetic lung cancer data.
LLMs can execute specific technical steps in statistical proofs when given precise guidance but become unreliable for open-ended problem formulation or multi-step reasoning, relocating rather than reducing the demand for human expertise.
A review organizes externally controlled trial methodology through causal estimands and identifiability assumptions for single-arm and hybrid designs with borrowing strategies.
citing papers explorer
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Hierarchical Projection for Adaptive Knowledge Transfer
ProjectionTL is a hierarchical Bayesian plus adaptive projection framework that performs simultaneous source selection and feature selection to mitigate negative transfer in cross-domain learning.
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Robust Estimation and Inference with Selective Borrowing in Hybrid Controlled Trials: A Tutorial with SelectiveIntegrative and intFRT
Tutorial on a statistical roadmap and R packages for selective borrowing in hybrid controlled trials, demonstrated on synthetic lung cancer data.
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Statistical Proof as a Window into Human-AI Collaboration: Practical Insights and a Community Agenda
LLMs can execute specific technical steps in statistical proofs when given precise guidance but become unreliable for open-ended problem formulation or multi-step reasoning, relocating rather than reducing the demand for human expertise.
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Externally Controlled Trials: A Review of Design and Borrowing Through a Causal Lens
A review organizes externally controlled trial methodology through causal estimands and identifiability assumptions for single-arm and hybrid designs with borrowing strategies.