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Marformer: A Transformer for Predicting Missing Data Distributions

Paper recorded by Signals 4 on 2026-10-08 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-08 on arXiv · recorded by Signals 4 on 2026-10-09

Category: cs.LG · 机器学习 · first seen 2026-10-09

Abstract

Real decisions are made under incomplete information. If we observe only some of the random variables we need, we can predict the others. The \textbf{conditional marginals} over the missing variables are the key ingredient for computing Bayes risk and Value of Information (VOI), the expected gain from acquiring one more observation before deciding. We present the Marformer, a Transformer trained t

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#329 most recent of 749 cs.LG papers we have recorded · ↑ newer: Long Text to Predictive Features: LLM-Guided Blockwise Feature Enginee · ↓ older: Decentralized SGD under Heavy-Tailed Noise: Optimal Convergence Rates
Cite this page: Marformer: A Transformer for Predicting Missing Data Distributions: the #329 most recent of 749 cs.LG papers we have recorded (as of 2026-10-08). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/marformer-a-transformer-for-predicting-missing-data-distributions.html
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