Graph-accelerated Markov Chain Monte Carlo using Approximate Samples

Subjects: “…Artificial Intelligence & Machine Learning…”
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Online Quantile Regression

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Statistical Inference of Random Graphs With a Surrogate Likelihood Function

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On the Representation of Pairwise Causal Background Knowledge and Its Applications in Causal Inference

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Algorithms for ridge estimation with convergence guarantees

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Inferring Change Points in High-Dimensional Regression via Approximate Message Passing

Subjects: “…Artificial Intelligence & Machine Learning…”
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Universality of Kernel Random Matrices and Kernel Regression in the Quadratic Regime

Subjects: “…Artificial Intelligence & Machine Learning…”
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Lexicographic Lipschitz Bandits: New Algorithms and a Lower Bound

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On the Natural Gradient of the Evidence Lower Bound

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Optimal Rates of Kernel Ridge Regression under Source Condition in Large Dimensions

Subjects: “…Artificial Intelligence & Machine Learning…”
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Scalable and Adaptive Variational Bayes Methods for Hawkes Processes

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Biological Sequence Kernels with Guaranteed Flexibility

Subjects: “…Artificial Intelligence & Machine Learning…”
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Unified Discrete Diffusion for Categorical Data

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

Subjects: “…Artificial Intelligence & Machine Learning…”
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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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Decentralized Asynchronous Optimization with DADAO allows Decoupling and Acceleration

Subjects: “…Artificial Intelligence & Machine Learning…”
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Mixtures of Gaussian Process Experts with SMC^2

Subjects: “…Artificial Intelligence & Machine Learning…”
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Robust Point Matching with Distance Profiles

Subjects: “…Artificial Intelligence & Machine Learning…”
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BoFire: Bayesian Optimization Framework Intended for Real Experiments

Subjects: “…Artificial Intelligence & Machine Learning…”
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