Publications (12)
Polynomial-Time Exact MAP Inference on Discrete Models with Global Dependencies
Alexander Bauer, Shinichi Nakajima
Considering the worst-case scenario, junction tree algorithm remains the most general solution for exact MAP inference with polynomial run-time guarantees. Unfortunately, its main…
ConStellaration: A dataset of QI-like stellarator plasma boundaries and optimization benchmarks
Santiago A. Cadena, Andrea Merlo, Emanuel Laude +8
Stellarators are magnetic confinement devices under active development to deliver steady-state carbon-free fusion energy. Their design involves a high-dimensional, constrained opti…
Self-Supervised Training with Autoencoders for Visual Anomaly Detection
Alexander Bauer, Shinichi Nakajima, Klaus-Robert Müller
We focus on a specific use case in anomaly detection where the distribution of normal samples is supported by a lower-dimensional manifold. Here, regularized autoencoders provide a…
Noise & pattern: identity-anchored Tikhonov regularization for robust structural anomaly detection
Alexander Bauer, Klaus-Robert Müller
Anomaly detection plays a pivotal role in automated industrial inspection, aiming to identify subtle or rare defects in otherwise uniform visual patterns. As collecting representat…
Towards Best Practice in Explaining Neural Network Decisions with LRP
Maximilian Kohlbrenner, Alexander Bauer, Shinichi Nakajima +3
Within the last decade, neural network based predictors have demonstrated impressive - and at times super-human - capabilities. This performance is often paid for with an intranspa…
Partial Optimality of Dual Decomposition for MAP Inference in Pairwise MRFs
Alexander Bauer, Shinichi Nakajima, Nico Görnitz +1
Markov random fields (MRFs) are a powerful tool for modelling statistical dependencies for a set of random variables using a graphical representation. An important computational pr…
Registration for Incomplete Non-Gaussian Functional Data
Alexander Bauer, Fabian Scheipl, Helmut Küchenhoff +1
Accounting for phase variability is a critical challenge in functional data analysis. To separate it from amplitude variation, functional data are registered, i.e., their observed…
Mundus vult decipi, ergo decipiatur: Visual Communication of Uncertainty in Election Polls
Alexander Bauer, André Klima, Jana Gauà +3
Election poll reporting often focuses on mean values and only subordinately discusses the underlying uncertainty. Subsequent interpretations are too often phrased as certain. Moreo…
KOALA: A new paradigm for election coverage
Alexander Bauer, Andreas Bender, André Klima +1
Common election poll reporting is often misleading as sample uncertainty is addressed insufficiently or not covered at all. Furthermore, main interest usually lies beyond the simpl…
Rethinking Structural Anomaly Detection: From Decision Boundaries to Projection Operators
Alexander Bauer
Most existing anomaly detection methods rely on estimating a probability density or learning an enclosing decision boundary, implicitly assuming that normal data occupies a region…
Optimizing for Measure of Performance in Max-Margin Parsing
Alexander Bauer, Shinichi Nakajima, Nico Görnitz +1
Many statistical learning problems in the area of natural language processing including sequence tagging, sequence segmentation and syntactic parsing has been successfully approach…
Pair-copula Bayesian networks
Alexander Bauer, Claudia Czado
Pair-copula Bayesian networks (PCBNs) are a novel class of multivariate statistical models, which combine the distributional flexibility of pair-copula constructions (PCCs) with th…