188 papers
ReTabSyn is a reinforcement learning-based pipeline for realistic tabular data synthesis that prioritizes learning conditional distributions to better preserve feature correlations and improve downstream model utility in low-data, imbalanced, and shifted settings.
This paper introduces PCID, a novel permutation-based method for offline multiple change-point detection in piecewise-constant circular signals that utilizes an isolation strategy and a von Mises-derived contrast function to achieve robust performance across various noise distributions and real-world applications.
This paper introduces a set of Python tools designed to bridge the gap between the theoretical framework of Kendall's 3D shape space and practical computational workflows, addressing specific limitations in existing libraries like Geomstats to facilitate advanced 3D shape analysis.
This methodological review of 100 diagnostic test accuracy meta-analyses published in 2024 reveals that while investigations of heterogeneity are common and more frequent with larger numbers of primary studies, they often suffer from limited data support for subgroups, unclear reporting of statistical models, and insufficient prespecification in protocols.
This paper proposes a model-agnostic, distribution-free approach using split and sequential conformal prediction to construct prediction intervals for high-dimensional functional time series, demonstrating their effectiveness in forecasting subnational mortality rates for Japan and Canada compared to traditional methods.
This paper establishes a strong Gaussian approximation for high-dimensional non-degenerate U-statistics with diverging dimensions under mild assumptions, utilizing a sharp martingale maximal inequality to provide a unified framework for functional limits and inference without relying on -type bounds or bootstrap arguments.
This paper introduces PATRO, a transparent and computationally efficient plug-in method that applies distinct data-independent adjustments to experimental estimates for rollout and downstream optimization decisions, thereby minimizing avoidable losses from asymmetric estimation errors and achieving performance comparable to the complex Bayes-optimal benchmark.
This paper establishes the convergence of Dual Space Preconditioned Gradient Descent to an interpolating solution for overparameterized linear models using novel Bregman divergence techniques, while characterizing its implicit bias to show that isotropic preconditioners recover standard gradient descent solutions and general preconditioners yield solutions within a constant factor of the standard solution.
This paper introduces "Brenier isotonic regression," a novel multi-output regression framework that extends classical isotonic regression by enforcing cyclic monotonicity through optimal transport principles, demonstrating superior performance in probability calibration and generalized linear models.
This paper proposes Weighted improved Greedy Sampling (WiGS), a reinforcement learning-based active learning framework that dynamically adapts the balance between feature-space diversity and output-space uncertainty to outperform static multiplicative methods, particularly in domains with irregular data density.
This paper proposes a surrogate-assisted targeted minimum loss estimator that achieves asymptotic linearity and double robustness for causal inference with delayed outcomes under administrative censoring by leveraging a surrogate-bridge representation to avoid unstable inverse-probability weighting and eliminate second-order bias without directly estimating the conditional surrogate law.
This paper proposes a novel estimation approach for updating risk prediction models that leverages transportable risk rankings from external prognostic models rather than their specific scores, thereby improving predictive performance in internal studies with novel biomarkers even when underlying risk scores differ significantly.
This paper addresses the fixed-budget best-arm identification problem in non-stationary linear bandits by establishing a tighter, arm-set-dependent lower bound on error probability and proposing the algorithm, which utilizes an Adjacent-optimal design to achieve minimax-optimal performance that fully leverages the geometric structure of the arm set.
This paper demonstrates that a class of optimized e-value combinations remains valid even when tuning parameters are data-dependent, extending this result to a new category of "simultaneous e-variables" and proposing an improved combination test based on elementary symmetric polynomials.
This paper demonstrates that the standard criterion for identifying minimal sufficient statistics is flawed due to its dependence on specific versions of Radon-Nikodym derivatives, and subsequently proposes a version-robust alternative that extends existing methods to broader analytic and standard Borel spaces while also critiquing a separate criterion by Pfanzagl.
The paper introduces MultiwayPAM, a novel tensor clustering method designed to simultaneously estimate cluster memberships and medoids for questions, answerers, and evaluators in LLM-as-a-Judge score tensors, thereby addressing computational costs and revealing inherent evaluator biases.
This paper demonstrates that when the prior given hyperparameters in a Bayesian hierarchical model is a canonical maximum entropy distribution, the resulting dependent marginal prior also possesses a maximum entropy property, but with respect to a constraint on the marginal distribution of a function of the unknown parameters, thereby clarifying the implicit information assumptions of such models.
This paper establishes that a continuous-time diffusion approximation of policy gradient for stochastic bandits achieves logarithmic regret with a learning rate of , while demonstrating that a significantly smaller learning rate of is necessary to avoid linear regret in specific instances.
The paper introduces BSync, a Bayesian framework that improves the synchronization of proxy paleorecords with reference chronologies by inferring a monotone time-mapping function with quantified uncertainty, outperforming existing optimization-based methods especially when independent age constraints are sparse.
This paper establishes a general theoretical framework for Outcome Weighted Learning that extends convergence guarantees to Matern and Gaussian kernels under various conditions, while proposing efficient algorithms that demonstrate strong performance in both simulations and a real-world clinical application.