35 papers
This paper introduces parallel algorithms for simulating zeroth-order Metropolis Markov chains based on Picard maps that significantly accelerate convergence in high-dimensional settings by leveraging parallel computing to generate samples from log-concave distributions using only point-wise evaluations of the log-density.
This paper introduces a novel sampling framework using multivariate temporal point processes modeled as coupled infinite-server queues to efficiently sample from discrete distributions with downward-closed support, demonstrating superior performance over existing birth-death and Zanella processes while enabling biologically plausible recurrent neural network applications.
This paper introduces Bayesian Generative Modeling (BGM), a unified framework that leverages a stochastic iterative Bayesian updating algorithm to learn a single generative model capable of performing arbitrary conditional inference with principled uncertainty quantification, without requiring retraining for different conditioning structures.
This paper introduces an efficient algorithm that rapidly computes a complete curve of post hoc false discovery proportion bounds along a path of increasing selection sets by leveraging the forest structure of the reference family and the incremental nature of adding single hypotheses.
This paper proposes TV-Select, a unified framework that simultaneously identifies relevant variables and distinguishes between constant and time-varying effects in longitudinal models by employing a doubly penalized B-spline approach with group Lasso and roughness penalties to achieve accurate structural recovery, smooth estimation, and improved predictive performance.
This paper establishes that Metropolis–Hastings algorithms with non-geometrically ergodic proposals and high acceptance rates at large states fail to be geometrically ergodic, while further demonstrating that guided walk variants converge twice as fast as random walk versions for polynomial-tailed targets but exhibit similar ballistic speeds to random walks for strictly convex potentials.
This paper establishes the unique determination of both linear and nonlinear coefficients in a semilinear Helmholtz equation from Neumann-to-Dirichlet boundary measurements using higher-order linearization and provides a Bayesian numerical reconstruction framework with uncertainty quantification.
By applying geometric branch growth and renormalization to real mouse suprachiasmatic nucleus networks, this study reveals that circadian rhythms remain robust across different network scales, demonstrating that average connectivity degree, rather than network size or clustering, is the primary structural driver of rhythmic stability.
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 introduces BNEM, a robust diffusion-based sampler that learns from energy functions via bootstrapped noised energy matching to efficiently generate independent samples from Boltzmann distributions, outperforming existing methods on complex molecular dynamics benchmarks.
This paper introduces Momentum SVGD-EM, an accelerated algorithm for Maximum Marginal Likelihood Estimation that integrates Nesterov momentum into both parameter updates and probability measure optimization via Stein variational gradient descent, demonstrating consistently faster convergence across diverse low- and high-dimensional tasks.
This paper reports the first realization of fractional topological phases, characterized by winding numbers and robust edge states, in a fully unitary, noninteracting single-coin split-step quantum walk on finite cyclic graphs, demonstrating how step-dependent protocols enable the engineering of flat bands and unconventional bulk-boundary correspondence in small-scale synthetic quantum systems.
This paper proposes a fast, deterministic tensor completion method that leverages standard linear algebra to recover tensors from fiberwise observations along a single mode, offering efficient recovery guarantees without relying on random sampling assumptions.
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 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 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.
The 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 directly applicable to multi-class classification problems.
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 "ForwardFlow," a frequentist deep learning framework that utilizes a branched neural network trained on simulated data to directly estimate statistical parameters, demonstrating advantages such as finite sample exactness, robustness to contamination, and the ability to automatically approximate complex algorithms like the EM-algorithm.
This paper investigates the selection of optimal two-block partitions to accelerate Markov chain mixing by establishing theoretical connections between KL divergence and Frobenius distance objectives and their respective decay rates, while proposing computationally feasible approximation algorithms to solve the resulting combinatorial optimization problem.