1 citations · 4 across the 12 of their papers we have counts for
14 papers
Fetch Me If You Can: A Comprehensive Analysis of Fetch Metadata
Pedro Bernardo, Alex Ploner, Marco Squarcina +2
Effective server-side defenses against cross-site attacks depend on understanding the context in which browser requests are generated. However, servers often lack the information n…
Closing the Cross-Dialect Gap: Query Plans as a Portable Interface in Text-to-SQL
Corentin Royer, Robin Oester, Yotam Perlitz +3
Text-to-SQL systems are typically trained and evaluated on a single dialect (SQLite), yet production deployments span PostgreSQL, MySQL, ClickHouse, and beyond. We show that this s…
Where Do Models Find Happiness? Emotion Vectors in Open-Source LLMs
Sinie van der Ben, Raphaël Baur, Yannick Metz +1
Recent work identified emotion vectors in Claude Sonnet 4.5, which are internal representations that encode emotion concepts, causally influence behavior, and exhibit geometry mirr…
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…
ResponseRank: Data-Efficient Reward Modeling through Preference Strength Learning
Timo Kaufmann, Yannick Metz, Daniel Keim +1
Binary choices, as often used for reinforcement learning from human feedback (RLHF), convey only the direction of a preference. A person may choose apples over oranges and bananas…
Evaluating Autoencoders for Parametric and Invertible Multidimensional Projections
Frederik L. Dennig, Nina Geyer, Daniela Blumberg +2
Recently, neural networks have gained attention for creating parametric and invertible multidimensional data projections. Parametric projections allow for embedding previously unse…
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…
Leveraging Color Channel Independence for Improved Unsupervised Object Detection
Bastian Jäckl, Yannick Metz, Udo Schlegel +2
Object-centric architectures can learn to extract distinct object representations from visual scenes, enabling downstream applications on the object level. Similarly to autoencoder…
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…
Interactive Public Transport Infrastructure Analysis through Mobility Profiles: Making the Mobility Transition Transparent
Yannick Metz, Dennis Ackermann, Daniel A. Keim +1
Efficient public transport systems are crucial for sustainable urban development as cities face increasing mobility demands. Yet, many public transport networks struggle to meet di…
MULTI-CASE: A Transformer-based Ethics-aware Multimodal Investigative Intelligence Framework
Maximilian T. Fischer, Yannick Metz, Lucas Joos +2
AI-driven models are increasingly deployed in operational analytics solutions, for instance, in investigative journalism or the intelligence community. Current approaches face two…
RLHF-Blender: A Configurable Interactive Interface for Learning from Diverse Human Feedback
Yannick Metz, David Lindner, Raphaël Baur +2
To use reinforcement learning from human feedback (RLHF) in practical applications, it is crucial to learn reward models from diverse sources of human feedback and to consider huma…
How to Enable Uncertainty Estimation in Proximal Policy Optimization
Eugene Bykovets, Yannick Metz, Mennatallah El-Assady +2
While deep reinforcement learning (RL) agents have showcased strong results across many domains, a major concern is their inherent opaqueness and the safety of such systems in real…
BARReL: Bottleneck Attention for Adversarial Robustness in Vision-Based Reinforcement Learning
Eugene Bykovets, Yannick Metz, Mennatallah El-Assady +2
Robustness to adversarial perturbations has been explored in many areas of computer vision. This robustness is particularly relevant in vision-based reinforcement learning, as the…