Timezone: »
Normalizing flows are powerful non-parametric statistical models that function as a hybrid between density estimators and generative models. Current learning algorithms for normalizing flows assume that data points are sampled independently, an assumption that is frequently violated in practice, which may lead to erroneous density estimation and data generation. We propose a likelihood objective of normalizing flows incorporating dependencies between the data points, for which we derive a flexible and efficient learning algorithm suitable for different dependency structures. We show that respecting dependencies between observations can improve empirical results on both synthetic and real-world data, and leads to higher statistical power in a downstream application to genome-wide association studies.
Author Information
Matthias Kirchler (Hasso Plattner Institute)
Christoph Lippert (Hasso Plattner Insitute for Digital Engineering, Universität Potsdam)
Marius Kloft (TU Kaiserslautern)
More from the Same Authors
-
2023 : Computing non-vacuous PAC-Bayes generalization bounds for Models under Adversarial Corruptions »
Waleed Mustafa · Philipp Liznerski · Dennis Wagner · Puyu Wang · Marius Kloft -
2023 Poster: Deep Anomaly Detection under Labeling Budget Constraints »
Aodong Li · Chen Qiu · Marius Kloft · Padhraic Smyth · Stephan Mandt · Maja Rudolph -
2022 Poster: Latent Outlier Exposure for Anomaly Detection with Contaminated Data »
Chen Qiu · Aodong Li · Marius Kloft · Maja Rudolph · Stephan Mandt -
2022 Poster: On the Generalization Analysis of Adversarial Learning »
Waleed Mustafa · Yunwen Lei · Marius Kloft -
2022 Spotlight: Latent Outlier Exposure for Anomaly Detection with Contaminated Data »
Chen Qiu · Aodong Li · Marius Kloft · Maja Rudolph · Stephan Mandt -
2022 Spotlight: On the Generalization Analysis of Adversarial Learning »
Waleed Mustafa · Yunwen Lei · Marius Kloft -
2021 Poster: Neural Transformation Learning for Deep Anomaly Detection Beyond Images »
Chen Qiu · Timo Pfrommer · Marius Kloft · Stephan Mandt · Maja Rudolph -
2021 Spotlight: Neural Transformation Learning for Deep Anomaly Detection Beyond Images »
Chen Qiu · Timo Pfrommer · Marius Kloft · Stephan Mandt · Maja Rudolph -
2018 Poster: Deep One-Class Classification »
Lukas Ruff · Nico Görnitz · Lucas Deecke · Shoaib Ahmed Siddiqui · Robert Vandermeulen · Alexander Binder · Emmanuel Müller · Marius Kloft -
2018 Oral: Deep One-Class Classification »
Lukas Ruff · Nico Görnitz · Lucas Deecke · Shoaib Ahmed Siddiqui · Robert Vandermeulen · Alexander Binder · Emmanuel Müller · Marius Kloft