182 papers
This paper proposes additive tree models for two-sample density ratio estimation using a novel balancing loss that enables efficient training via supervised learning algorithms and generalized Bayesian inference for uncertainty quantification, with demonstrated effectiveness in high-dimensional settings and generative model assessment.
This paper proposes a systematic framework for empirically developing evidence-based tailoring variables for adaptive interventions, arguing that while secondary data can be used, specifically designed optimization experiments (such as SMARTs or factorial designs) provide the most direct causal evidence for determining optimal measurement times, decision points, and cutoffs.
This paper proposes a causal framework for meta-analysis that introduces novel aggregation formulas for nonlinear effect measures to address the limitations of conventional methods, revealing that standard approaches can sometimes misleadingly suggest treatment benefits where causal effects are actually harmful.
This paper proposes an adaptive algorithm that optimally balances the computational resources between estimating oracle statistics and constructing a multi-fidelity estimator, achieving mean-squared error comparable to the theoretical optimum while significantly reducing costs in applications like parametric PDEs and ice-sheet modeling.
This paper proposes a novel data-driven framework using offline reinforcement learning and survival analysis to estimate optimal pricing and inventory control policies in sequential decision-making environments characterized by censored and dependent demand, overcoming challenges like missing profit information and non-stationarity through high-order Markov decision process approximations and finite-sample regret guarantees.
This paper introduces NETCROP, a computationally efficient and accurate cross-validation method for large networks that utilizes overlapping subnetwork partitions to facilitate model selection and parameter tuning.
This paper proposes a novel family of statistical models for pairwise comparisons that relaxes the restrictive stochastic transitivity assumption by utilizing a low-dimensional skew-symmetric matrix, thereby achieving minimax-rate optimal estimation and superior predictive performance in complex, real-world scenarios where traditional models like Bradley-Terry fail.
This paper proposes a framework for calibrating clinical trial designs to formally link statistical significance with clinical equipoise imbalance, demonstrating that standard power and error rates in phase 2 and 3 oncology studies provide robust evidence of equipoise imbalance when outcomes are consistent, while inconsistent results would require impractically large sample sizes to achieve the same level of evidence.
This paper introduces CLIQUE, a novel model-agnostic method for calculating local variable importance that overcomes the limitations of existing techniques like LIME and SHAP by capturing locally dependent relationships and interaction behaviors while being natively adapted for multi-class classification problems.
This paper advances the theory of small area prediction intervals by analytically demonstrating that the coverage error of empirical best linear predictors depends critically on the existence of a pivot, revealing that standard parametric bootstrap methods fail to achieve optimal accuracy without it and proposing a double parametric bootstrap approach to correct this deficiency.
This paper establishes the theoretical foundations of Vecchia approximations for Matérn Gaussian Processes by characterizing their conditional distributions via polynomial interpolation, deriving key probabilistic properties, and proving that the resulting posterior achieves optimal minimax convergence rates in nonparametric regression.
This paper proposes a geometric memorization theory demonstrating that as training data becomes scarce, diffusion models undergo a smooth, gradual collapse in latent dimensionality where salient features and finer details progressively freeze out, leading to near point-wise replication of training examples rather than an abrupt transition from generalization to exact copying.
This paper proposes and evaluates three multi-fidelity parameter estimation methods—joint maximum likelihood, moment estimation, and marginal maximum likelihood—to efficiently fit parametric models to high-fidelity data by leveraging abundant low-fidelity data, with a specific focus on extreme value analysis and applications to ship motion extremes.
This paper proposes two robust criteria and a corresponding sampling-based difference-of-convex algorithm for learning optimal treatment rules that maximize truncated mean survival time and buffered survival probabilities in the presence of censored survival data, demonstrating superior performance through simulations and an AIDS clinical trial application.
This paper proposes and evaluates two nonparametric methods for estimating state entry time distributions and occupation probabilities in progressive multistate models with current status data, addressing the challenges of interval censoring through fractional at-risk sets and marginal probability ratios, and demonstrating their effectiveness via simulations and a breast cancer case study.
This paper establishes asymptotic properties of Bayesian cut distributions, including a Bernstein-von Mises theorem and Laplace approximation, and proposes a Posterior Bootstrap algorithm to achieve nominal frequentist coverage in the presence of model misspecification.
This paper introduces Scorio, an open-source library that formalizes and implements statistical methods for reliably ranking reasoning LLMs under test-time scaling, demonstrating that most approaches achieve high agreement with a Bayesian gold standard across multiple Olympiad-style math benchmarks.
This paper introduces a selective prediction framework for molecular structure retrieval from mass spectra that leverages retrieval-level uncertainty and distribution-free risk control to allow models to abstain from low-confidence predictions, thereby ensuring annotations meet specified error rate constraints in high-stakes applications.
This paper proposes a practical Kernel Density Estimator-based method to quantify membership disclosure risk in tabular synthetic data by modeling nearest-neighbor distances, demonstrating through empirical evaluation that it outperforms existing baselines in accuracy and efficiency without requiring computationally expensive shadow models.
This paper compares Monte Carlo Dropout and Conformal Prediction for uncertainty estimation in CNNs trained on Fashion-MNIST, revealing that while H-CNN VGG16 achieves higher accuracy, GoogLeNet offers better calibration and Conformal Prediction provides statistically guaranteed reliability for high-stakes applications.