arXiv Machine Learning June 8, 2026 By Muqing Cui, Yidong Zhou, Su I Iao, Hans-Georg M\"uller

Deep Single-Index Fr\'echet Regression

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arXiv:2606. 06957v1 Announce Type: cross Abstract: Predicting outputs that are located in non-Euclidean spaces, such as probability distributions, networks, and symmetric positive-definite matrices, is becoming increasingly important in modern data analysis, particularly when inputs are high-dimensional.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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arXiv AI

Jun 2

End-to-End Deep Learning for Predicting Metric Space-Valued Outputs

arXiv:2509. 23544v2 Announce Type: replace-cross Abstract: Many modern applications involve predicting structured, non-Euclidean outputs such as probability distributions, networks, and symmetric positive-definite matrices.

By Yidong Zhou, Su I Iao, Hans-Georg M\"uller

rag benchmarks

arXiv Machine Learning

Jun 5

Mitigating the Curse of Dimensionality in Uniform Convergence of Deep Neural Networks via Smooth Activations

arXiv:2606. 05599v1 Announce Type: new Abstract: This paper establishes a theoretical framework for the uniform convergence of smoothly activated deep neural network (DNN) estimators.

By Yizhe Ding, Runze Li, Jia Liu, Lingzhou Xue

arXiv Machine Learning

Aug 12

FiGuRO: Intrinsic Dimension Estimation for Multi-Modal Data

arXiv:2608. 10857v1 Announce Type: new Abstract: Determining the complexity, or Intrinsic Dimension (ID), of data is fundamental to efficient and interpretable representation learning.

By Viktoria Schuster, Sana Tonekaboni, Caroline Uhler

diffusion multimodal

arXiv Machine Learning

Jun 25

Efficient Analytic Uncertainty Quantification for Multi-Modal Regression

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By Kun Jin, James Harrison, Jiawei Li, Sihan Liu, Jiayi Liu, Randolph Linderman, Yuening Li, Arnab Bhadury, Sourabh Prakash Bansod, Liang Liu, Jasper Snoek

multimodal benchmarks

arXiv Machine Learning

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Sufficient Dimesion Reduction via Generalized Stein's Lemma

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By Ye Tian

efficiency

arXiv Machine Learning

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Semi-Supervised Conditional Generative Learning through Stochastic Interpolation and Sufficient Representations

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