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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L., Athanasopoulos, G., and Hyndman, R
28 Pith papers cite this work. Polarity classification is still indexing.
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Releasing DP synthetic data built from treatment-arm and outcome moments, plus noise-aware multiple imputation, gives calibrated ATE intervals at strict privacy budgets.
A Bayesian hypergraph inference method models EHR multi-disease risk by letting risk factors modulate latent hyperedges (disease subsets) with repulsion priors and structured variational inference for uncertainty and scalability.
Variational predictive resampling iteratively imputes data from a variational predictive to produce posterior samples that converge to the exact Bayesian posterior in Gaussian models where mean-field VI retains a gap.
The authors propose an S-MILP framework that optimizes group sequential testing boundaries to achieve faster rejection of the null hypothesis compared to traditional methods while controlling type I and type II errors.
A cycle-counting-ratio estimator for the β-model achieves minimax-optimal MSE and consistency under the weak conditions θ_max→0 and θ_t‖θ‖₁→∞, even at network densities near log n/n.
Proposes FDR-controlling posterior decision rules for signal detection under horseshoe and similar continuous shrinkage priors that attain the optimal detection boundary with asymptotic FDR and FNR control in sparse normal means models.
Proposes variance deltas, an interactive tree-based visualization system that identifies subsets of unobserved quantities explaining posterior uncertainty, with demonstrations on causal inference and polling data.
Proposes ERHT-CC test based on spatial median and spatial-sign covariance with Cauchy aggregation over ridge parameters, deriving asymptotic normality and local power under elliptical symmetry.
A nonparametric estimator of the regimen-response curve for stochastic JITAIs on distal outcomes is developed, with weak convergence to a Gaussian process and asymptotic theory for the optimizing policy.
A conjugate-prior Bayesian variable-selection framework for exponential-family GLMs is proven to recover the true predictor set and the active coefficients with posterior probability tending to one.
A doubly robust calibration-plus-control-variate estimator allows causal inference with multiple misclassified binary exposures and shows throat-swab data understate P. aeruginosa's effect on lung function in cystic fibrosis.
A framework for online forecast reconciliation is developed via multivariate linear models on graph hierarchies, ridge regression, and recursive least squares, with a demonstration on district heating load data.
A Bayesian hierarchical model integrates coherence penalization and level-specific focus into forecasting estimation, yielding improved predictive accuracy on simulated and Australian tourism data.
Predictively consistent priors let complex Bayesian models match or beat the out-of-sample performance of selected simpler models across linear, logistic, and nonlinear examples without explicit selection.
A neural reconcilier produces coherent station and OD demand forecasts for urban rail transit and reduces OD error by up to 17.45 percent under multi-step disruption scenarios.
Derives exact marginal likelihood under finite-support Huber contamination via Dirichlet-Beta priors and dynamic programming over count allocations.
Sub-network Laplace approximations always underestimate an idealized predictive variance, and the proposed gradient- and greedy-based parameter selection rules provably close that gap better than existing heuristics.
A self-supervised loss integrates hierarchy reconciliation into training for time series models, producing reconciled forecasts with uncertainty estimates that improve on post-hoc methods in synthetic tests.
A unified Bayesian framework constructs simultaneous credible bands for univariate polynomial regression that achieve exact posterior coverage and asymptotic frequentist coverage under mild regularity conditions.
Ensemble voting strategies for change point detection improve F1-score by 11% over Mozilla's T-test method on a new ground-truth dataset of 174 performance time series annotated by practitioners.
An iterative procedure computes optimal ridge regularization from generative parameters in fixed-X settings and achieves near-optimal generalization with sample-based estimates across sample sizes, aspect ratios, and noise levels.
Truncation to enforce positive-definiteness in separable priors for matrices distorts interpretability and biases sparse posterior inference unless off-diagonal variances are scaled with dimension.
Review and simulation comparison of more than 40 threshold selection procedures for univariate extreme value analysis, with application to daily rainfall data.
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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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Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration
Releasing DP synthetic data built from treatment-arm and outcome moments, plus noise-aware multiple imputation, gives calibrated ATE intervals at strict privacy budgets.
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Disentangling Latent Risk Pathways via Bayesian Hypergraph Inference
A Bayesian hypergraph inference method models EHR multi-disease risk by letting risk factors modulate latent hyperedges (disease subsets) with repulsion priors and structured variational inference for uncertainty and scalability.
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Variational predictive resampling
Variational predictive resampling iteratively imputes data from a variational predictive to produce posterior samples that converge to the exact Bayesian posterior in Gaussian models where mean-field VI retains a gap.
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A General Framework for Optimal Group Sequential Testing via Mixed-Integer Linear Programming
The authors propose an S-MILP framework that optimizes group sequential testing boundaries to achieve faster rejection of the null hypothesis compared to traditional methods while controlling type I and type II errors.
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Subgraph counting estimation for the $\beta$-model in sparse networks
A cycle-counting-ratio estimator for the β-model achieves minimax-optimal MSE and consistency under the weak conditions θ_max→0 and θ_t‖θ‖₁→∞, even at network densities near log n/n.
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Multiple testing with the horseshoe
Proposes FDR-controlling posterior decision rules for signal detection under horseshoe and similar continuous shrinkage priors that attain the optimal detection boundary with asymptotic FDR and FNR control in sparse normal means models.
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Variance Deltas for Visualizing and Explaining Posterior Uncertainty
Proposes variance deltas, an interactive tree-based visualization system that identifies subsets of unobserved quantities explaining posterior uncertainty, with demonstrations on causal inference and polling data.
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Elliptical Regularized Hotelling Testing for High Dimensional Data
Proposes ERHT-CC test based on spatial median and spatial-sign covariance with Cauchy aggregation over ridge parameters, deriving asymptotic normality and local power under elliptical symmetry.
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Nonparametric Estimation of Optimal Stochastic Just-In-Time Adaptive Interventions for Distal Outcomes
A nonparametric estimator of the regimen-response curve for stochastic JITAIs on distal outcomes is developed, with weak convergence to a Gaussian process and asymptotic theory for the optimizing policy.
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Bayesian Variable Selection in Generalized Linear Models
A conjugate-prior Bayesian variable-selection framework for exponential-family GLMs is proven to recover the true predictor set and the active coefficients with posterior probability tending to one.
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Causal Inference with Multiple Misclassified Exposures: A Control Variate-Adjusted Calibration Weighting Approach
A doubly robust calibration-plus-control-variate estimator allows causal inference with multiple misclassified binary exposures and shows throat-swab data understate P. aeruginosa's effect on lung function in cystic fibrosis.
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Online forecast reconciliation using linear models
A framework for online forecast reconciliation is developed via multivariate linear models on graph hierarchies, ridge regression, and recursive least squares, with a demonstration on district heating load data.
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Hierarchical Bayes meets hierarchical forecasting: A flexible framework for level-focused forecasts
A Bayesian hierarchical model integrates coherence penalization and level-specific focus into forecasting estimation, yielding improved predictive accuracy on simulated and Australian tourism data.
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To select or not to select: predictively consistent priors instead of model selection
Predictively consistent priors let complex Bayesian models match or beat the out-of-sample performance of selected simpler models across linear, logistic, and nonlinear examples without explicit selection.
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Hierarchical Forecast Reconciliation for Urban Rail Transit Demand Prediction under Operational Disruptions
A neural reconcilier produces coherent station and OD demand forecasts for urban rail transit and reduces OD error by up to 17.45 percent under multi-step disruption scenarios.
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Marginal likelihoods for finite-support Huber contamination
Derives exact marginal likelihood under finite-support Huber contamination via Dirichlet-Beta priors and dynamic programming over count allocations.
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Optimality of Sub-network Laplace Approximations: New Results and Methods
Sub-network Laplace approximations always underestimate an idealized predictive variance, and the proposed gradient- and greedy-based parameter selection rules provably close that gap better than existing heuristics.
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A Self-supervised Approach to Hierarchical Forecasting with Applications to Groupwise Synthetic Controls
A self-supervised loss integrates hierarchy reconciliation into training for time series models, producing reconciled forecasts with uncertainty estimates that improve on post-hoc methods in synthetic tests.
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Bayesian Simultaneous Credible Bands for Polynomial Regression
A unified Bayesian framework constructs simultaneous credible bands for univariate polynomial regression that achieve exact posterior coverage and asymptotic frequentist coverage under mild regularity conditions.
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Exploring Statistical Change Point Detection Techniques for Performance Anomaly Detection at Mozilla
Ensemble voting strategies for change point detection improve F1-score by 11% over Mozilla's T-test method on a new ground-truth dataset of 174 performance time series annotated by practitioners.
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Optimal ridge regularization revisited
An iterative procedure computes optimal ridge regularization from generative parameters in fixed-X settings and achieves near-optimal generalization with sample-based estimates across sample sizes, aspect ratios, and noise levels.
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Positive-definiteness in separable priors: effects on prior interpretability and inference
Truncation to enforce positive-definiteness in separable priors for matrices distorts interpretability and biases sparse posterior inference unless off-diagonal variances are scaled with dimension.
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Choosing the threshold in extreme value analysis
Review and simulation comparison of more than 40 threshold selection procedures for univariate extreme value analysis, with application to daily rainfall data.
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Adversarial Contamination Meets Hard Thresholding: An Iterative Algorithm with Signal Adaptivity and Minimax Optimality
AC-IHT is a two-stage iterative algorithm for contaminated high-dimensional regression that attains minimax near-optimal rates, signal adaptivity under suitable conditions, and the strong oracle property.
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Feature Screening for High-Dimensional Structural Break Predictive Regression
Develops SICS and RCRS screening methods for consistent selection of sparse active predictors and change points in high-dimensional structural break predictive regressions that may involve stationary or cointegrated series.
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Proposes a generative-AI framework integrating smart metering, quantum-inspired optimization for gas distribution, billing, and carbon analytics in energy infrastructure.
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