General Loss Functions Lead to (Approximate) Interpolation in High Dimensions

Subjects: “…Artificial Intelligence & Machine Learning…”
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Piecewise deterministic sampling with splitting schemes

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Hierarchical and Stochastic Crystallization Learning: Geometrically Leveraged Nonparametric Regression with Delaunay Triangulation

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Gold-medalist Performance in Solving Olympiad Geometry with AlphaGeometry2

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Decentralized Bilevel Optimization: A Perspective from Transient Iteration Complexity

Subjects: “…Artificial Intelligence & Machine Learning…”
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Fair Text Classification via Transferable Representations

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Stochastic Interior-Point Methods for Smooth Conic Optimization with Applications

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Revisiting Gradient Normalization and Clipping for Nonconvex SGD under Heavy-Tailed Noise: Necessity, Sufficiency, and Acceleration

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Generalized multi-view model: Adaptive density estimation under low-rank constraints

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(De)-regularized Maximum Mean Discrepancy Gradient Flow

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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

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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

Subjects: “…Artificial Intelligence & Machine Learning…”
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