Multi-source transfer learning incurs an intrinsic adaptation cost that can exceed one, with phase transitions separating regimes where bias-agnostic estimators match oracle performance from those where they cannot.
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6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
years
2026 6verdicts
UNVERDICTED 6representative citing papers
Derives joint asymptotic jump-diffusion limit for global parameters and latent variables in SGLD-Gibbs under space-time rescaling, yielding explicit hyperparameter tuning guidance for calibrated uncertainty quantification.
Covariate-augmented spectral clustering for heterogeneous networks with misclustering bounds under a contextualized stochastic blockmodel, applied to UNGA voting data.
Develops two methods for inference on unit-specific coefficients in latent-group panel data models that incorporate uncertainty in group assignments to gain efficiency over unit-by-unit approaches.
A Bayesian CP tensor factorization model with Poisson rate for occurrence and conditional Gamma for magnitude, with slice-specific dispersion, applied to 60 million international trade flows to recover multiway dependencies.
A weighted K-means plus decision-tree pipeline learns multi-action policies from observational data and is applied to HCV treatment choices for HIV co-infected patients, finding a high-clearance subgroup and potential cost savings of CAN$3.6-4.9 million.
citing papers explorer
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The Statistical Cost of Adaptation in Multi-Source Transfer Learning
Multi-source transfer learning incurs an intrinsic adaptation cost that can exceed one, with phase transitions separating regimes where bias-agnostic estimators match oracle performance from those where they cannot.
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Large-scale Uncertainty Quantification for Latent Variable Models Using Subsampling Markov Chain Monte Carlo
Derives joint asymptotic jump-diffusion limit for global parameters and latent variables in SGLD-Gibbs under space-time rescaling, yielding explicit hyperparameter tuning guidance for calibrated uncertainty quantification.
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Understanding Geopolitical Alignments Through Covariate Augmented Spectral Clustering of Heterogeneous UNGA Voting Data
Covariate-augmented spectral clustering for heterogeneous networks with misclustering bounds under a contextualized stochastic blockmodel, applied to UNGA voting data.
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Inference methods for unit-specific coefficients in panel data models with latent group structure
Develops two methods for inference on unit-specific coefficients in latent-group panel data models that incorporate uncertainty in group assignments to gain efficiency over unit-by-unit approaches.
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Bayesian Poisson-Randomized Gamma Tensor Factorization with Application to International Trade Flows
A Bayesian CP tensor factorization model with Poisson rate for occurrence and conditional Gamma for magnitude, with slice-specific dispersion, applied to 60 million international trade flows to recover multiway dependencies.
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Policy Learning with Observational Data: The Case of Hepatitis C Treatment for HIV/HCV Co-Infected Patients
A weighted K-means plus decision-tree pipeline learns multi-action policies from observational data and is applied to HCV treatment choices for HIV co-infected patients, finding a high-clearance subgroup and potential cost savings of CAN$3.6-4.9 million.