73 citations · 439 across the 61 of their papers we have counts for
64 papers
Clustering of large deviations in heavy-tailed moving averages: the catastrophe principle in the long-memory case
Jiaqi Wang, Gennady Samorodnitsky
Clustering of large deviations events in a stationary stochastic process depends critically on the interplay between the tail behavior of the marginal distribution and the strength…
Clustering of large deviations events in heavy-tailed moving average processes: the catastrophe principle in the short-memory case
Jiaqi Wang, Gennady Samorodnitsky
How do large deviation events in a stationary process cluster? The answer depends not only on the type of large deviations, but also on the length of memory in the process. Somewha…
Empirical Risk Minimization for Losses without Variance
Guanhua Fang, Ping Li, Gennady Samorodnitsky
This paper considers an empirical risk minimization problem under heavy-tailed settings, where data does not have finite variance, but only has -th moment with . In…
Limit theorems for high-dimensional Betti numbers in the multiparameter random simplicial complexes
Takashi Owada, Gennady Samorodnitsky
We consider the multiparameter random simplicial complex on a vertex set , which is parameterized by multiple connectivity probabilities. Our key results concern t…
Epsilon*: Privacy Metric for Machine Learning Models
Diana M. Negoescu, Humberto Gonzalez, Saad Eddin Al Orjany +9
We introduce Epsilon*, a new privacy metric for measuring the privacy risk of a single model instance prior to, during, or after deployment of privacy mitigation strategies. The me…
Adaptive Privacy Composition for Accuracy-first Mechanisms
Ryan Rogers, Gennady Samorodnitsky, Zhiwei Steven Wu +1
In many practical applications of differential privacy, practitioners seek to provide the best privacy guarantees subject to a target level of accuracy. A recent line of work by Li…