166 citations · 424 across the 44 of their papers we have counts for
6 papers · 1 filter
Set-Membership Inference Attacks using Data Watermarking
Mike Laszkiewicz, Denis Lukovnikov, Johannes Lederer +1
In this work, we propose a set-membership inference attack for generative models using deep image watermarking techniques. In particular, we demonstrate how conditional sampling fr…
Single-Model Attribution of Generative Models Through Final-Layer Inversion
Mike Laszkiewicz, Jonas Ricker, Johannes Lederer +1
Recent breakthroughs in generative modeling have sparked interest in practical single-model attribution. Such methods predict whether a sample was generated by a specific generator…
Lag selection and estimation of stable parameters for multiple autoregressive processes through convex programming
Somnath Chakraborty, Johannes Lederer, Rainer von Sachs
Motivated by a variety of applications, high-dimensional time series have become an active topic of research. In particular, several methods and finite-sample theories for individu…
Extremes in High Dimensions: Methods and Scalable Algorithms
Johannes Lederer, Marco Oesting
Extreme value theory for univariate and low-dimensional observations has been explored in considerable detail, but the field is still in an early stage regarding high-dimensional s…
The DeepCAR Method: Forecasting Time-Series Data That Have Change Points
Ayla Jungbluth, Johannes Lederer
Many methods for time-series forecasting are known in classical statistics, such as autoregression, moving averages, and exponential smoothing. The DeepAR framework is a novel, rec…
Cardinality Sparsity: Applications in Matrix-Matrix Multiplications and Machine Learning
Ali Mohades, Johannes Lederer
High-dimensional data has become ubiquitous across the sciences but presents computational and statistical challenges. A common approach to addressing these challenges is through s…