arXiv Machine Learning By Farnaz Faramarzi Lighvan, Mehrdad Asadi, Lynn Houthuys

Adaptive Weighted LSSVM for Multi-View Classification

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arXiv:2512. 02653v2 Announce Type: replace Abstract: Multi-view learning integrates diverse representations of the same instances and can improve performance when interactions across views are effectively exploited.

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arXiv Machine Learning
Aug 20

Pretraining Reusable Inference Across Views with Synthetic Task Priors

The paper introduces SIMPLE, a prior‑fitted multi‑view in‑context learner that learns a reusable, task‑conditioned inference procedure instead of a fixed fusion function. By generating synthetic task priors in embedding space, SIMPLE can handle diverse view configurations, class structures, and missingness patterns. Experiments on multi‑view and multi‑omics benchmarks show that a frozen SIMPLE model performs competitively, and lightweight adapter calibration further improves performance across most datasets.

By Jielong Lu, Zhihao Wu, Jiajun Yu, Zhaoliang Chen, Haishuai Wang