7 citations · 14 across the 10 of their papers we have counts for
7 papers · 1 filter
BSAT: B-Spline Adaptive Tokenizer for Long-Term Time Series Forecasting
Maximilian Reinwardt, Michael Eichelbeck, Matthias Althoff
Long-term time series forecasting using transformers is hampered by the quadratic complexity of self-attention and the rigidity of uniform patching, which may be misaligned with th…
Improving Stochastic Action-Constrained Reinforcement Learning via Truncated Distributions
Roland Stolz, Michael Eichelbeck, Matthias Althoff
In reinforcement learning (RL), it is often advantageous to consider additional constraints on the action space to ensure safety or action relevance. Existing work on such action-c…
Zono-Conformal Prediction: Zonotope-Based Uncertainty Quantification for Regression and Classification Tasks
Laura Lützow, Michael Eichelbeck, Mykel J. Kochenderfer +1
Conformal prediction is a popular uncertainty quantification method that augments a base predictor to return sets of predictions with statistically valid coverage guarantees. Howev…
PyTupli: A Scalable Infrastructure for Collaborative Offline Reinforcement Learning Projects
Hannah Markgraf, Michael Eichelbeck, Daria Cappey +3
Offline reinforcement learning (RL) has gained traction as a powerful paradigm for learning control policies from pre-collected data, eliminating the need for costly or risky onlin…
Predicting building types and functions at transnational scale
Jonas Fill, Michael Eichelbeck, Michael Ebner
Building-specific knowledge such as building type and function information is important for numerous energy applications. However, comprehensive datasets containing this informatio…
Excluding the Irrelevant: Focusing Reinforcement Learning through Continuous Action Masking
Roland Stolz, Hanna Krasowski, Jakob Thumm +3
Continuous action spaces in reinforcement learning (RL) are commonly defined as multidimensional intervals. While intervals usually reflect the action boundaries for tasks well, th…
Formal Verification of Graph Convolutional Networks with Uncertain Node Features and Uncertain Graph Structure
Tobias Ladner, Michael Eichelbeck, Matthias Althoff
Graph neural networks are becoming increasingly popular in the field of machine learning due to their unique ability to process data structured in graphs. They have also been appli…