activity
20202025
most citedWhat's Wrong with Deep Learning in Tree Search for Combinatorial Optimization

10 citations · 17 across the 5 of their papers we have counts for

collaborators

8 papers

cs.LG2025

Mixtera: A Data Plane for Foundation Model Training

Maximilian Böther, Xiaozhe Yao, Tolga Kerimoglu +3

State-of-the-art large language and vision models are trained over trillions of tokens that are aggregated from a large variety of sources. As training data collections grow, manua…

cs.LG2024

On Distributed Larger-Than-Memory Subset Selection With Pairwise Submodular Functions

Maximilian Böther, Abraham Sebastian, Pranjal Awasthi +2

Modern datasets span billions of samples, making training on all available data infeasible. Selecting a high quality subset helps in reducing training costs and enhancing model qua…

cs.LG2023★ 6 cited

Modyn: Data-Centric Machine Learning Pipeline Orchestration

Maximilian Böther, Ties Robroek, Viktor Gsteiger +4

In real-world machine learning (ML) pipelines, datasets are continuously growing. Models must incorporate this new training data to improve generalization and adapt to potential di…

cs.LG2022★ 10 cited

What's Wrong with Deep Learning in Tree Search for Combinatorial Optimization

Maximilian Böther, Otto Kißig, Martin Taraz +3

Combinatorial optimization lies at the core of many real-world problems. Especially since the rise of graph neural networks (GNNs), the deep learning community has been developing…

cs.IR2021★ 1 cited

Law Smells: Defining and Detecting Problematic Patterns in Legal Drafting

Corinna Coupette, Dirk Hartung, Janis Beckedorf +2

Building on the computer science concept of code smells, we initiate the study of law smells, i.e., patterns in legal texts that pose threats to the comprehensibility and maintaina…

cs.LO2020

Learning Languages with Decidable Hypotheses

Julian Berger, Maximilian Böther, Vanja Doskoč +9

In language learning in the limit, the most common type of hypothesis is to give an enumerator for a language. This so-called -index allows for naming arbitrary computably enume…

cs.LG2020

Maps for Learning Indexable Classes

Julian Berger, Maximilian Böther, Vanja Doskoč +9

We study learning of indexed families from positive data where a learner can freely choose a hypothesis space (with uniformly decidable membership) comprising at least the language…

cs.DS2020

A Strategic Routing Framework and Algorithms for Computing Alternative Paths

Thomas Bläsius, Maximilian Böther, Philipp Fischbeck +9

Traditional navigation services find the fastest route for a single driver. Though always using the fastest route seems desirable for every individual, selfish behavior can have un…