8 citations · 17 across the 8 of their papers we have counts for
8 papers
SciEx: Benchmarking Large Language Models on Scientific Exams with Human Expert Grading and Automatic Grading
Tu Anh Dinh, Carlos Mullov, Leonard Bärmann +15
With the rapid development of Large Language Models (LLMs), it is crucial to have benchmarks which can evaluate the ability of LLMs on different domains. One common use of LLMs is…
A graph database for feature characterization of dislocation networks
Balduin Katzer, Daniel Betsche, Klemens Böhm +2
Three-dimensional dislocation networks control the mechanical properties such as strain hardening of crystals. Due to the complexity of dislocation networks and their temporal evol…
Adaptive Bernstein Change Detector for High-Dimensional Data Streams
Marco Heyden, Edouard Fouché, Vadim Arzamasov +3
Change detection is of fundamental importance when analyzing data streams. Detecting changes both quickly and accurately enables monitoring and prediction systems to react, e.g., b…
Budgeted Multi-Armed Bandits with Asymmetric Confidence Intervals
Marco Heyden, Vadim Arzamasov, Edouard Fouché +1
We study the stochastic Budgeted Multi-Armed Bandit (MAB) problem, where a player chooses from arms with unknown expected rewards and costs. The goal is to maximize the total r…
Maximum Mean Discrepancy on Exponential Windows for Online Change Detection
Florian Kalinke, Marco Heyden, Georg Gntuni +2
Detecting changes is of fundamental importance when analyzing data streams and has many applications, e.g., in predictive maintenance, fraud detection, or medicine. A principled ap…
Pedagogical Rule Extraction to Learn Interpretable Models - an Empirical Study
Vadim Arzamasov, Benjamin Jochum, Klemens Böhm
Machine-learning models are ubiquitous. In some domains, for instance, in medicine, the models' predictions must be interpretable. Decision trees, classification rules, and subgrou…
Efficient Subspace Search in Data Streams
Edouard Fouché, Florian Kalinke, Klemens Böhm
In the real world, data streams are ubiquitous -- think of network traffic or sensor data. Mining patterns, e.g., outliers or clusters, from such data must take place in real time.…
Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary
Adrian Englhardt, Holger Trittenbach, Daniel Kottke +2
Support Vector Data Description (SVDD) is a popular one-class classifiers for anomaly and novelty detection. But despite its effectiveness, SVDD does not scale well with data size.…