177 papers
This paper proposes a minimax-optimal estimator for varying coefficient models using truncated Laguerre series, establishing its asymptotic normality and finite-sample performance through theoretical analysis, simulations, and real-data applications.
This paper develops a theory of asymptotic post-hoc inference that utilizes e-values to create sharper confidence sets and p-values with weaker assumptions than existing nonasymptotic methods, thereby allowing valid statistical conclusions even when significance levels are chosen after observing the data.
This paper formalizes the goal of demonstrating positive treatment effects in adaptive experiments within a multi-armed bandit framework, proposing novel inference procedures for threshold testing and an adaptive allocation rule that optimizes signal-to-noise ratio with logarithmic regret.
This paper introduces Partial Decomposition of Granger Causality (PDGC), a novel framework leveraging Partial Information Decomposition to dissect multivariate spectral Granger causality into unique, redundant, and synergistic components, which was successfully applied to physiological networks to reveal distinct patterns of autonomic dysfunction in patients prone to neurally-mediated syncope.
This paper introduces a framework for operational certification of split conformal predictors that moves beyond marginal coverage by providing finite-sample guarantees for critical deployment metrics like commitment frequency and error exposure through Small-Sample Beta Correction, an independent audit-based auditing protocol, and a geometric analysis of Pareto trade-offs.
This paper proposes Adaptive Transfer Clustering (ATC), a unified framework that automatically leverages commonalities between a main and an auxiliary dataset to improve clustering performance despite unknown discrepancies, while providing theoretical optimality guarantees under Gaussian mixture models and demonstrating effectiveness through extensive experiments.
This paper proposes a structured semiparametric framework combining an algorithm choice model and a viewer response model to correct the severe bias in standard estimators caused by algorithmic interference in two-sided marketplaces, thereby enabling accurate global treatment effect estimation for platform-wide algorithm updates.
This paper introduces StablePCA, a distributionally robust framework for extracting shared low-dimensional representations from multi-source data by maximizing worst-case explained variance, and addresses its inherent nonconvexity through a convex relaxation solved by an efficient Mirror-Prox algorithm with global convergence guarantees and a data-dependent certificate for solution tightness.
This paper unifies online A/B testing and off-policy evaluation by proving that standard variance reduction methods in both domains are mathematically equivalent, specifically showing that Difference-in-Means estimators correspond to optimal Inverse Propensity Scoring and that regression adjustment techniques align with Doubly Robust estimation.
This monograph offers a rigorous theoretical and methodological overview of probabilistic inference and learning with Stein's method, detailing the construction and properties of Stein discrepancies, their connection to Stein variational gradient descent, and providing precise definitions and proofs.
This paper proposes a deep learning-based framework using deep conditional transformation models and cross-fitting to estimate conditional rank-rank regression for measuring within-group intergenerational mobility, offering improved accuracy and interpretability over traditional methods for both continuous and discrete outcomes while providing rigorous asymptotic theory and empirical evidence of significant mobility patterns in the U.S. and India.
This paper introduces deep spatiotemporal engression methods that leverage lightweight generative architectures with pre-additive noise to provide accurate, reliable, and explainable probabilistic forecasts for epidemic incidences, demonstrating superior performance over existing benchmarks across multiple datasets.
This paper introduces the TEA-Time framework for extrapolating treatment effects across different time periods by proposing two identification strategies with doubly robust estimators, which are validated through simulations and applied to Upworthy A/B tests to demonstrate a trade-off between precision and bias.
The paper introduces CREDO, a method that combines interpretable credal envelopes with conformal calibration to generate distribution-free prediction intervals that explicitly account for epistemic uncertainty while maintaining rigorous coverage guarantees.
This paper proposes the Latent Autoencoder Ensemble Kalman Filter (LAE-EnKF), a novel data assimilation method that learns a stable, linear state-space model in a latent space to overcome the performance limitations of standard EnKF on strongly nonlinear and chaotic systems while maintaining computational efficiency.
This paper introduces the c-delta coefficient, a novel statistical measure that quantifies the similarity of internal divergence patterns between two groups of values to assess whether their variability structures are mirrored, offering a scale-invariant tool for applications ranging from quantum physics to machine learning.
This paper establishes that the A-optimality criterion can be factored into a D-optimal scale term and a dimensionless sphericity factor dependent on eigenvalue dispersion, thereby explaining performance differences among D-equivalent designs and providing a lightweight method for refining candidate pools.
This paper introduces a unified framework for online LLM watermark detection based on e-processes, which provides anytime-valid statistical guarantees and enhances detection power through empirically adaptive methods applicable to various sequential testing problems.
This paper proposes a control-variates-based transfer learning approach for scalar-on-function regression that utilizes dataset-specific summary statistics to preserve privacy, establishes a theoretical equivalence between offset and control-variates methods, and derives convergence rates that account for discretization errors and cross-dataset covariance similarities.
This paper introduces computationally efficient and statistically consistent Random Forest variants based on the Maximum Risk Minimization (MaxRM) principle to improve out-of-distribution generalization across multiple environments, offering novel guarantees for risks including mean squared error, negative reward, and regret.