Deep Neural Networks are Adaptive to Function Regularity and Data Distribution in Approximation and Estimation

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Generation of Geodesics with Actor-Critic Reinforcement Learning to Predict Midpoints

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Learning-to-Optimize with PAC-Bayesian Guarantees: Theoretical Considerations and Practical Implementation

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Sparse Semiparametric Discriminant Analysis for High-dimensional Zero-inflated Data

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Stochastic Interpolants: A Unifying Framework for Flows and Diffusions

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Efficient Methods for Non-stationary Online Learning

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Decentralized Asynchronous Optimization with DADAO allows Decoupling and Acceleration

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Mixtures of Gaussian Process Experts with SMC^2

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Robust Point Matching with Distance Profiles

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BoFire: Bayesian Optimization Framework Intended for Real Experiments

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Reliever: Relieving the Burden of Costly Model Fits for Changepoint Detection

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Variational Inference for Uncertainty Quantification: an Analysis of Trade-offs

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Are Ensembles Getting Better All the Time?

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An Adaptive Parameter-free and Projection-free Restarting Level Set Method for Constrained Convex Optimization Under the Error Bound Condition

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Operator Learning for Hyperbolic PDEs

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Optimal subsampling for high-dimensional partially linear models via machine learning methods

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Decentralized Sparse Linear Regression via Gradient-Tracking

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Calibrated Inference: Statistical Inference that Accounts for Both Sampling Uncertainty and Distributional Uncertainty

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Relaxed Gaussian Process Interpolation: a Goal-Oriented Approach to Bayesian Optimization

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Linear Separation Capacity of Self-Supervised Representation Learning

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