10 citations · 17 across the 5 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…
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…