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Wasserstein Exponential Smoothing

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arXiv:2606. 05560v1 Announce Type: cross Abstract: Exponential smoothing (ES) often outperforms other techniques in time series forecasting across a wide range of data-generating processes.

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Authors: Takuo Matsubara, Peiwen Jiang, Minh-Ngoc Tran, Wilson Ye Chen
Released: 2026-06-05 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Wasserstein Exponential Smoothing overlaps with clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "Only continuous soft prefixes hit both EM axes at once on Qwen-2.5-7B-Instruct: discrete prompt searches split between the alignment objective and the distributional objective, and both discretizations of the soft prefix collapse (MODERATE confidence)". Matching terms: class, distributional, rate, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2606.05560

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