33 citations · 61 across the 42 of their papers we have counts for
5 papers · 1 filter
Building Fast, Evaluating Slow: Pipeline Choices Dominate Autointerpretability Score Variance
Sinie van der Ben, Neele Roch, Anna Hedström +1
Cross-paper comparison of sparse autoencoder (SAE) interpretability often relies on autointerpretability scores. In this evaluation pipeline, a language model (LM) explains each fe…
MAVRL: Learning Reward Functions from Multiple Feedback Types with Amortized Variational Inference
Raphaël Baur, Yannick Metz, Maria Gkoulta +3
Reward learning typically relies on a single feedback type or combines multiple feedback types using manually weighted loss terms. Currently, it remains unclear how to jointly lear…
Reward Learning from Multiple Feedback Types
Yannick Metz, András Geiszl, Raphaël Baur +1
Learning rewards from preference feedback has become an important tool in the alignment of agentic models. Preference-based feedback, often implemented as a binary comparison betwe…
Mapping out the Space of Human Feedback for Reinforcement Learning: A Conceptual Framework
Yannick Metz, David Lindner, Raphaël Baur +1
Reinforcement Learning from Human feedback (RLHF) has become a powerful tool to fine-tune or train agentic machine learning models. Similar to how humans interact in social context…
PowerGraph: A power grid benchmark dataset for graph neural networks
Anna Varbella, Kenza Amara, Blazhe Gjorgiev +2
Power grids are critical infrastructures of paramount importance to modern society and, therefore, engineered to operate under diverse conditions and failures. The ongoing energy t…