289 papers
This paper introduces a scalable multitask Gaussian process model with a fully separable kernel structure that effectively handles functional covariates and correlated tasks, demonstrating superior accuracy and computational efficiency over single-task approaches in predicting the behavior of complex mechanical systems like riveted assemblies with limited data.
This paper introduces an adaptive replication strategy within a trust-region Bayesian optimization framework that dynamically allocates repeated evaluations to effectively manage high-variance stochastic functions, significantly improving both solution accuracy and computational efficiency compared to baseline methods.
This paper demonstrates that by strategically combining sample splitting with specific nuisance function tuning strategies (such as undersmoothing or oversmoothing), both plug-in and first-order bias-corrected estimators can achieve minimax rates of convergence for doubly robust functionals across all Hölder smoothness classes, overcoming limitations of existing literature.
This paper introduces a novel Fréchet regression framework for multivariate distributional responses that leverages the nonparanormal transport metric to efficiently decompose the problem into marginal and dependence regressions, offering theoretical guarantees on convergence and dimensionality while demonstrating practical utility in continuous glucose monitoring.
This paper presents a unified framework that proves the equivalence of variational, Green's function, and characteristic-based methods for constructing reproducing kernels, enabling a data-driven, mesh-free approach to learning kernels that accurately approximate Koopman eigenfunctions and solve various linear transport equations.
This paper introduces a new class of non-equal volume partitions that achieve a lower expected star discrepancy and improved upper bounds compared to classical jittered sampling, thereby providing a theoretical foundation for their use in high-dimensional numerical integration.
This paper proposes an amortized Bayesian inference framework for graph-structured data that combines permutation-invariant summary networks with neural posterior estimators to enable fast, likelihood-free inference on node, edge, and graph-level parameters, demonstrating its effectiveness through evaluations on synthetic benchmarks and real-world applications in biology and logistics.
This paper proposes actor-critic methods with sparse robust estimator oracles to achieve the first non-vacuous guarantees for learning near-optimal policies in high-dimensional sparse offline reinforcement learning under strong data corruption and single-policy concentrability, overcoming the limitations of traditional Least Square Value Iteration approaches in such regimes.
This paper proposes a parameter-free, equivariant topological spatial graph coarsening method that reduces graph size by collapsing short edges while preserving key topological features through a novel triangle-aware graph filtration adapted from persistent diagrams.
This paper introduces "Mercer priors," a new class of interpretable priors for Bayesian neural networks derived from Mercer representations of covariance kernels, which enable the networks to approximate Gaussian process samples and thereby combine the scalability of neural networks with the uncertainty quantification interpretability of Gaussian processes.
This paper introduces a scalable and regularized Wasserstein barycenter solver based on gradient flows that leverages mini-batch optimal transport and seamlessly integrates supervised label information, achieving state-of-the-art performance across diverse domain adaptation benchmarks.
This paper introduces the DRQ-learner, a novel meta-learner for estimating individualized potential outcomes in Markov Decision Processes that combines double robustness, Neyman orthogonality, and quasi-oracle efficiency to outperform existing state-of-the-art methods in sequential decision-making.
This paper introduces the first fully empirical PAC-Bayes bound for Markov chains by deriving a data-dependent estimate for the pseudo-spectral gap, thereby eliminating the need for unknown constants related to mixing properties that typically hinder practical generalization guarantees.
This paper presents a fast, scalable method that combines deep learning with Dynamic Input Conductances (DICs) to reconstruct diverse, degenerate populations of conductance-based neuron models directly from spike times, effectively bridging experimental recordings and mechanistic biophysical parameters.
This paper proposes the F²SA- method, which utilizes -th order finite differences to achieve a nearly optimal complexity for finding -stationary points in stochastic bilevel optimization with highly smooth objectives, thereby improving upon previous first-order bounds and matching the fundamental lower limit.
This paper benchmarks Multivariate Imputation by Chained Equations (MICE) against deep generative models like Variational Autoencoders and Conditional Tabular GANs for synthetic ratemaking data, finding that MICE offers a simpler yet high-fidelity alternative that effectively preserves statistical distributions and supports robust Generalized Linear Model training.
This paper proposes a penalized pessimistic personalized policy learning (P4L) framework that leverages individual latent variables to derive optimal policies for heterogeneous populations from offline data, achieving fast regret rates under weak coverage assumptions and outperforming existing methods in both simulations and real-world applications.
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 establishes the statistical foundations of the mini-batch maximum partial-likelihood estimator (mb-MPLE) for deep Cox models optimized via stochastic gradient descent, proving its consistency and optimal convergence rates while providing practical guidance on hyperparameter tuning and demonstrating its effectiveness in large-scale applications where standard estimation is intractable.
This paper introduces Sparse Isotonic Shapley Regression (SISR), a unified framework that simultaneously learns a monotonic transformation to restore additivity and enforces sparsity constraints to provide robust, efficient, and theoretically grounded feature attributions for nonlinear, high-dimensional Explainable AI.