arXiv Machine Learning

Sequential sparse Gaussian process quantile regression

arXiv:2606. 31284v1 Announce Type: new Abstract: Quantile regression aims to estimate the conditional quantiles of a response variable from observed data.

arXiv Machine Learning

Aug 11

Conditional Diffusion for Nonparametric Instrumental Variable Quantile Regression

arXiv:2608. 08204v1 Announce Type: cross Abstract: This work proposes deep nonparametric Instrumental variable quantile regression (IVQR), a two-stage estimator that combines conditional diffusion modeling with a kernel-smoothed conditional moment formulation.

By Xingdong Feng, Xinhong Jiang, Yuling Jiao, Lican Kang, Junwei Liu

diffusion

arXiv Machine Learning

Jun 25

Efficient Analytic Uncertainty Quantification for Multi-Modal Regression

arXiv:2606. 25188v1 Announce Type: new Abstract: Efficient uncertainty quantification (UQ) is essential for trustworthy large-scale learning.

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

Jun 30

Adaptive Iterative Hard Thresholding for Online High-dimensional Quantile Regression

arXiv:2606. 28652v1 Announce Type: cross Abstract: Online high-dimensional regression requires algorithms that can update sequentially while preserving structural sparsity.

By Zitian Zhou, Nan Lin

efficiency

arXiv Machine Learning

Jun 30

Warm-Starting Iterative Gaussian Processes for Faster Sequential Inference

arXiv:2511. 16340v2 Announce Type: replace Abstract: Efficient Gaussian process (GP) inference is critical for sequential decision-making tasks such as active learning, online prediction, and Bayesian optimization.

By Alan Yufei Dong, Jihao Andreas Lin, Jos\'e Miguel Hern\'andez-Lobato

benchmarks

arXiv Machine Learning

Jun 8

MIST: Mutual Information Estimation Via Supervised Training

arXiv:2511. 18945v4 Announce Type: replace Abstract: We propose a fully data-driven approach to designing mutual information (MI) estimators.

By German Gritsai, Megan Richards, Maxime M\'eloux, Kyunghyun Cho, Maxime Peyrard

arXiv Machine Learning

Jun 30

A Bayesian latent Gaussian process framework for aerodynamic uncertainty quantification

arXiv:2606. 28871v1 Announce Type: cross Abstract: Predicting the aerodynamic performance (e.

By Geoffrey Davis, Ashwin Renganathan

arXiv Machine Learning

Jun 17

Instrumental and Proximal Causal Inference with Gaussian Processes

arXiv:2603. 02159v2 Announce Type: replace-cross Abstract: Instrumental variable (IV) and proximal causal learning (Proxy) methods are central frameworks for causal inference in the presence of unobserved confounding.

By Yuqi Zhang, Krikamol Muandet, Dino Sejdinovic, Edwin Fong, Siu Lun Chau

arXiv Machine Learning

Jun 26

Ribbon: Scalable Approximation and Robust Uncertainty Quantification

arXiv:2606. 27269v1 Announce Type: cross Abstract: Reliably quantifying predictive uncertainty is difficult for complex, high-dimensional, or misspecified models.

By Graham Gibson, John Tipton, Kellin Rumsey, Natalie Klein

benchmarks

arXiv Machine Learning

Jun 2

Out-of-Distribution generalization of quantile regression with heavy tailed inputs: an SVM approach

arXiv:2606. 00265v1 Announce Type: cross Abstract: We study quantile regression in an extrapolation regime where the covariate takes unusually large values.

By Baptiste Leroux, Cl\'ement Dombry, Anne Sabourin

arXiv AI

Jul 15

Learning-based Probabilistic Load Forecasting with Post-hoc and In-model Uncertainty

arXiv:2607. 12730v1 Announce Type: cross Abstract: Smart-building load forecasters are often trained offline on dense, multivariate, high-frequency data, but deployment may provide only hourly, feature-limited inputs.

By Sarah Al-Shareeda, Gulcihan Ozdemir, Heung Seok Jeon

llms

arXiv AI

Jul 7

On Pairwise Quantile Regression -- Statistical Guarantees and Applications

arXiv:2607. 04431v1 Announce Type: cross Abstract: Quantile regression provides a powerful tool for summarizing the conditional distribution of a real valued random variable (r.

By Romain Th\'er\'ezien, Stephan Cl\'emen\c{c}on, Fantin Girard, Hamza El-Abdouni

arXiv Machine Learning

Jun 2

Frequentist Consistency of Prior-Data Fitted Networks for Causal Inference

arXiv:2603. 12037v2 Announce Type: replace Abstract: Foundation models based on prior-data fitted networks (PFNs) have shown strong empirical performance in causal inference by framing the task as an in-context learning problem.

By Valentyn Melnychuk, Vahid Balazadeh, Stefan Feuerriegel, Rahul G. Krishnan

llms safety

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