8 papers
Universality of Benign Overfitting in Binary Linear Classification
Ichiro Hashimoto, Stanislav Volgushev, Piotr Zwiernik
The practical success of deep learning has led to the discovery of several surprising phenomena. One of these phenomena, that has spurred intense theoretical research, is ``benign…
Empirical tail dependence functions in high dimensions: uniform linearizations and inference
Axel Bücher, Yeonjoon Choi, Katharina Effertz +1
The analysis of extremal dependence in high dimensions is a key challenge in modern extreme-value statistics. Existing methodology primarily focuses on modeling and estimation of e…
Adaptivity of the NPMLE to finitely discrete mixing distributions in Gaussian/Poisson mixtures
Yan Zhang, Stanislav Volgushev
We study the nonparametric maximum likelihood estimator (NPMLE) for Gaussian and Poisson mixture models, assuming the support of the true mixing distribution lies in a fixed bounde…
Graph structure learning for stable processes
Florian Brück, Sebastian Engelke, Stanislav Volgushev
We introduce Ising-Hüsler-Reiss processes, a new class of multivariate Lévy processes that allows for sparse modeling of the path-wise conditional independence structure between…
Learning extremal graphical structures in high dimensions
Sebastian Engelke, Michaël Lalancette, Stanislav Volgushev
Extremal graphical models encode the conditional independence structure of multivariate extremes. Key statistics for learning extremal graphical structures are empirical extremal v…
On a surprising behavior of the likelihood ratio test in non-parametric mixture models
Yan Zhang, Stanislav Volgushev
We study the likelihood ratio test in general mixture models where the base density is parametric, the null is a known fixed mixing distribution, and the alternative is a general m…