Heavy-Tailed Distributions

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3 papers in the last four weeks, level with the four weeks before. 0.0% of all new papers.

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DeepLévy: Learning Heavy-Tailed Uncertainty in Highly Volatile Time Series
May 11, 2026Yang Yang, Du Yin, Hao Xue +1Time Series Forecasting Distribution Matching
University of New South Wales Australia NSW Sydney · The Hong Kong University of Science and Technology (Guangzhou)
TailedTS: Benchmark Dataset for Heavy-Tailed Time Series Prediction and Periodicity Quantification
May 9, 2026Xinyu Chen, HanQin Cai, Lijun Ding +1Time Series Forecasting Heavy-Tailed Noise
University of Central Florida Orlando, FL 32816 · Massachusetts Institute of Technology Cambridge, MA 02139
Robust stochastic first order methods in heavy-tailed noise via medoid mini-batch gradient sampling
May 8, 2026Manojlo Vukovic, Dusan JakoveticStochastic Optimization Nonconvex Stochastic Optimization
Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia · Faculty of Sciences, University of Novi Sad, Novi Sad, Serbia
SHIFT: Robust Double Machine Learning for Average Dose-Response Functions under Heavy-Tailed Contamination
Apr 30, 2026Eichi UeharaDouble ML Robust Regression
Independent researcher.
Bayesian X-Learner: Calibrated Posterior Inference for Heterogeneous Treatment Effects under Heavy-Tailed Outcomes
Apr 30, 2026Eichi UeharaRobust Regression Causal Effect Estimation
Independent researcher
The Power of Power Law: Asymmetry Enables Compositional Reasoning
Apr 24, 2026Zixuan Wang, Xingyu Dang, Jason D. Lee +1Long-Tail Learning Compositional Reasoning
1Princeton University · University of California, Berke · 3Tsinghua University
Batched Bandits with Heavy-Tailed Rewards
Oct 4, 2025Yunwen Guo, Yunlun Shu, Gongyi Zhuo +1Multi-Armed Bandits Heavy-Tailed Noise
School of Mathematical Sciences, Fudan University, 220 Handan Rd., 200433, Shanghai, China · Shanghai Center for Mathematical Sciences, Fudan University, 220 Handan Rd., 200433, Shanghai, China
How Does the Pretraining Distribution Shape In-Context Learning? A Fundamental Trade-Off
Oct 1, 2025Waïss Azizian, Ali HasanLanguage Model Pretraining OOD Generalization
Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, 38000 Grenoble, France · Work done during an internship at Morgan Stanley Machine Learning Research. · Machine Learning Research, Morgan Stanley, New York, USA
Spherical Cauchy Variational Autoencoders: Heavy Angular Tails and Exact KL Evaluation
Jun 26, 2025Lukas Sablica, Kurt HornikVariational Autoencoders Hyperspherical Representation Learning
Institute for Statistics and Mathematics Vienna University of Economics and Business Austria

May 11, 2026, cs.LGJ/K move · Enter open · S save

DeepLévy: Learning Heavy-Tailed Uncertainty in Highly Volatile Time Series

Yang Yang, Du Yin, Hao Xue, Flora Salim

University of New South Wales Australia NSW Sydney · The Hong Kong University of Science and Technology (Guangzhou)

Time Series Forecasting Distribution Matching Heavy-Tailed Distributions Probabilistic Time Series Forecasting

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Modeling uncertainty in heavy-tailed time series remains a critical challenge for deep probabilistic forecasting models, which often struggle to capture abrupt, extreme events. While Lévy stable distributions offer a natural framework for modeling such non-Gaussian behaviors, the intractability of their probability density functions severely limits conventional likelihood-based inference. To address this, we introduce DeepLévy, a neural framework that learns mixtures of Lévy stable distributions by minimizing the discrepancy between empirical and parametric characteristic functions. DeepLévy incorporates a mixture mechanism that adaptively learns context-dependent weights and parameters over multiple Lévy components, enabling flexible multi-horizon uncertainty modeling. Evaluations on both real and synthetic datasets demonstrate that DeepLévy outperforms state-of-the-art deep probabilistic forecasting approaches in tail risk metrics, especially under extreme volatility.

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