stat.ML · 2026-07-29 · No. 68
Machine Learning, 2026-07-29.
2 new papers in stat.ML. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
2 entries-
01
Can Deep Generative Models Reproduce Non-Stationary Gaussian Random Fields?
Daniel Kua, Yan Song
stat.ML · cs.LG
Deep generative models (DGMs) are widely used for complex high-dimensional data and increasingly applied to spatial and spatio-temporal modeling. Their generated samples implicitly represent the learned data distribution and associated uncertainty. However, for real-world data, assessing whether DGMs have learned the underlying process is difficult because the ground truth is unknown and evaluation often relies on observations alone. We...
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02
Learning from the Unseen: Offline Reinforcement Learning with Hidden Actions
Zeyu Bian, Ying Zhou, Yifan Cui
stat.ML · cs.LG
Standard offline reinforcement learning (RL) algorithms typically assume that the actions in the dataset are observed without error. However, in many real-world applications, the true actions are unobserved and only noisy proxies are available, causing existing RL methods to yield biased and potentially misleading conclusions. We study off-policy evaluation in infinite-horizon discounted Markov decision processes with hidden actions. By...
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