Derivative-Informed Neural Operator Acceleration of Geometric MCMC for Infinite-Dimensional Bayesian Inverse Problems

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Wasserstein F-tests for Frechet regression on Bures-Wasserstein manifolds

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Distributed Stochastic Bilevel Optimization: Improved Complexity and Heterogeneity Analysis

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Learning causal graphs via nonlinear sufficient dimension reduction

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On Consistent Bayesian Inference from Synthetic Data

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Optimization Over a Probability Simplex

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Laplace Meets Moreau: Smooth Approximation to Infimal Convolutions Using Laplace's Method

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Sampling and Estimation on Manifolds using the Langevin Diffusion

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Sharp Bounds for Sequential Federated Learning on Heterogeneous Data

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Local Linear Recovery Guarantee of Deep Neural Networks at Overparameterization

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Stabilizing Sharpness-Aware Minimization Through A Simple Renormalization Strategy

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Fine-Grained Change Point Detection for Topic Modeling with Pitman-Yor Process

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Deletion Robust Non-Monotone Submodular Maximization over Matroids

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Instability, Computational Efficiency and Statistical Accuracy

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Estimation of Local Geometric Structure on Manifolds from Noisy Data

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Ontolearn---A Framework for Large-scale OWL Class Expression Learning in Python

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Continuously evolving rewards in an open-ended environment

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Recursive Causal Discovery

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Evaluation of Active Feature Acquisition Methods for Time-varying Feature Settings

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On Adaptive Stochastic Optimization for Streaming Data: A Newton's Method with O(dN) Operations

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