Optimization and Generalization of Gradient Descent for Shallow ReLU Networks with Minimal Width

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Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection

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CHANI: Correlation-based Hawkes Aggregation of Neurons with bio-Inspiration

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Persistence Diagrams Estimation of Multivariate Piecewise H{"o}lder-continuous Signals

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Exploring Novel Uncertainty Quantification through Forward Intensity Function Modeling

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Generative Bayesian Inference with GANs

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Communication-efficient Distributed Statistical Inference for Massive Data with Heterogeneous Auxiliary Information

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Decorrelated Local Linear Estimator: Inference for Non-linear Effects in High-dimensional Additive Models

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Refined Risk Bounds for Unbounded Losses via Transductive Priors

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A Common Interface for Automatic Differentiation

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LazyDINO: Fast, Scalable, and Efficiently Amortized Bayesian Inversion via Structure-Exploiting and Surrogate-Driven Measure Transport

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The Distribution of Ridgeless Least Squares Interpolators

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Nonparametric Estimation of a Factorizable Density using Diffusion Models

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Learning Bayesian Network Classifiers to Minimize Class Variable Parameters

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Simulation-based Calibration of Uncertainty Intervals under Approximate Bayesian Estimation

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An Anytime Algorithm for Good Arm Identification

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Extrapolated Markov Chain Oversampling Method for Imbalanced Text Classification

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Neural Network Parameter-optimization of Gaussian Pre-marginalized Directed Acyclic Graphs

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Flexible Functional Treatment Effect Estimation

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Error Analysis for Deep ReLU Feedforward Density-Ratio Estimation with Bregman Divergence

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