arXiv Machine Learning June 30, 2026 By Nolan Alexander, Henning Mortveit

A Mathematical Optimization Approach for Expert-Informed Bayesian Best Subset Selection

Read the original on arXiv Machine Learning →

arXiv:2606. 29516v1 Announce Type: new Abstract: A central challenge in statistical modeling is identifying the subset of features that belong in the true regression model.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Aug 26

A Heterogeneous Mixture of Experts Framework for Interpretable Machine Learning

arXiv:2608.24195v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models provide a flexible framework for partitioning complex prediction problems into simpler local learning tasks through a...

By Soham Chatterjee, Rwitobroto Dey, Smarajit Bose

benchmarks safety

arXiv AI

Sep 1

Perturbation Sensitivity of Maximum-Likelihood Pairwise Ranking in Computational Decision Systems

arXiv:2604.17805v3 Announce Type: replace-cross Abstract: Maximum-likelihood pairwise ranking is a com- mon computational mechanism for prioritization, reputation estimation, and comparison-driven de...

By Junyi Yao, Zihao Zheng, Jiayu Long

arXiv AI

Jul 1

When to Truncate a Feature Ranking: A Residual-Overlap Stopping Rule for Subset Selection

arXiv:2606. 31686v1 Announce Type: cross Abstract: Feature rankings are widely used in supervised feature selection because they are simple, scalable and easy to interpret.

By Jesus S. Aguilar-Ruiz

arXiv Statistics ML

Sep 10

Regularized Estimation and Feature Selection in Mixtures of Generalized Linear Experts

arXiv:1907.06994v2 Announce Type: replace-cross Abstract: Mixtures of experts (MoE) are conditional mixture models in which both the mixing proportions and the component densities depend on the predi...

By Thin Nguyen-Van, Faicel Chamroukhi, Ha Hoang Van, Bao Tuyen Huynh

efficiency benchmarks

arXiv Machine Learning

Sep 22

Bayesian Deck-of-cards-based Ordinal Regression with Sequential Preference Elicitation

arXiv:2609.23212v1 Announce Type: cross Abstract: The Deck-of-cards-based Ordinal Regression (DOR) infers a value function from a ranking of reference alternatives in which the Decision Maker (DM) in...

By Marco Grillo, Silvano Zappal\`a

arXiv Machine Learning

Jul 24

PreMoE: Proactive Inference for Efficient Mixture-of-Experts

arXiv:2505. 17639v4 Announce Type: replace Abstract: Mixture-of-Experts (MoE) models offer dynamic computation, but are typically deployed as static full-capacity models, missing opportunities for deployment-specific specialization.

By Zehua Pei, Ying Zhang, Hui-Ling Zhen, Tao Yuan, Xianzhi Yu, Zhenhua Dong, Sinno Jialin Pan, Mingxuan Yuan, Bei Yu

efficiency

One email a morning, machine-written

Related stories

A Heterogeneous Mixture of Experts Framework for Interpretable Machine Learning

Perturbation Sensitivity of Maximum-Likelihood Pairwise Ranking in Computational Decision Systems

When to Truncate a Feature Ranking: A Residual-Overlap Stopping Rule for Subset Selection

Regularized Estimation and Feature Selection in Mixtures of Generalized Linear Experts

Bayesian Deck-of-cards-based Ordinal Regression with Sequential Preference Elicitation

PreMoE: Proactive Inference for Efficient Mixture-of-Experts