(De)-regularized Maximum Mean Discrepancy Gradient Flow

Subjects: “…Artificial Intelligence & Machine Learning…”
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On Probabilistic Embeddings in Optimal Dimension Reduction

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Physics Informed Kolmogorov-Arnold Neural Networks for Dynamical Analysis via Efficient-KAN and WAV-KAN

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Graph-accelerated Markov Chain Monte Carlo using Approximate Samples

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Online Quantile Regression

Subjects: “…Artificial Intelligence & Machine Learning…”
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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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An Augmentation Overlap Theory of Contrastive Learning

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Algorithms for ridge estimation with convergence guarantees

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Talent: A Tabular Analytics and Learning Toolbox

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

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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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Geometry and Stability of Supervised Learning Problems

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Understanding Deep Representation Learning via Layerwise Feature Compression and Discrimination

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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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A Hybrid Weighted Nearest Neighbour Classifier for Semi-Supervised Learning

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

Subjects: “…Artificial Intelligence & Machine Learning…”
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Biological Sequence Kernels with Guaranteed Flexibility

Subjects: “…Artificial Intelligence & Machine Learning…”
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