More Like this

1. Differentiating Variance for Variance-Aware Inverse Rendering

   https://doi.org/10.1145/3680528.3687603  

   Yan, Kai; Pegoraro, Vincent; Droske, Marc; Vorba, Jiří; Zhao, Shuang (December 2024, ACM)
2. Empirical Privacy Variance

   Hu, Yuzheng; Wu, Fan; Xian, Ruicheng; Liu, Yuhang; Zakynthinou, Lydia; Kamath, Pritish; Zhang, Chiyuan; Forsyth, David (July 2025, openreview.net)
3. Adversarial defense by restricting in-variance and co-variance of representations

   Jeevithan Alagurajah (April 2022, 37th ACM/SIGAPP Symposium on Applied Computing)
4. Minimum variance constrained estimator

   https://doi.org/10.1016/j.automatica.2021.110106  

   Mishra, Prabhat K.; Chowdhary, Girish; Mehta, Prashant G. (March 2022, Automatica)
5. Deep variance gamma processes

   https://doi.org/10.1002/sta4.580  

   Berry, Caitlin M.; Kleiber, William (May 2023, Stat)

   Lévy processes are useful tools for analysis and modeling of jump‐diffusion processes. Such processes are commonly used in the financial and physical sciences. One approach to building new Lévy processes is through subordination, or a random time change. In this work, we discuss and examine a type of multiply subordinated Lévy process model that we term a deep variance gamma (DVG) process, including estimation and inspection methods for selecting the appropriate level of subordination given data. We perform an extensive simulation study to identify situations in which different subordination depths are identifiable and provide a rigorous theoretical result detailing the behavior of a DVG process as the levels of subordination tend to infinity. We test the model and estimation approach on a data set of intraday 1‐min cryptocurrency returns and show that our approach outperforms other state‐of‐the‐art subordinated Lévy process models. 

   more » « less