10 citations · 12 across the 8 of their papers we have counts for
9 papers
Mitigating Errors in LLM-Generated Web API Invocations via Retrieval-Augmented Generation and Constrained Decoding
Daniel Maninger, Leon Chemnitz, Jannis Brugger +3
Integration of web APIs is a cornerstone of modern software systems, yet writing correct web API invocation code remains challenging due to complex and evolving API specifications.…
Prompting Neural-Guided Equation Discovery Based on Residuals
Jannis Brugger, Viktor Pfanschilling, David Richter +2
Neural-guided equation discovery systems use a data set as prompt and predict an equation that describes the data set without extensive search. However, if the equation does not me…
Adaptable Hindsight Experience Replay for Search-Based Learning
Alexandros Vazaios, Jannis Brugger, Cedric Derstroff +2
AlphaZero-like Monte Carlo Tree Search systems, originally introduced for two-player games, dynamically balance exploration and exploitation using neural network guidance. This com…
Benchmarking Web API Integration Code Generation
Daniel Maninger, Leon Chemnitz, Amir Molzam Sharifloo +2
API integration is a cornerstone of our digital infrastructure, enabling software systems to connect and interact. However, as shown by many studies, writing or generating correct…
Neural-Guided Equation Discovery
Jannis Brugger, Mattia Cerrato, David Richter +4
Deep learning approaches are becoming increasingly attractive for equation discovery. We show the advantages and disadvantages of using neural-guided equation discovery by giving a…
Amplifying Exploration in Monte-Carlo Tree Search by Focusing on the Unknown
Cedric Derstroff, Jannis Brugger, Jannis Blüml +3
Monte-Carlo tree search (MCTS) is an effective anytime algorithm with a vast amount of applications. It strategically allocates computational resources to focus on promising segmen…
Peer Learning: Learning Complex Policies in Groups from Scratch via Action Recommendations
Cedric Derstroff, Mattia Cerrato, Jannis Brugger +2
Peer learning is a novel high-level reinforcement learning framework for agents learning in groups. While standard reinforcement learning trains an individual agent in trial-and-er…
Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery Systems
Stefan Kramer, Mattia Cerrato, Jannis Brugger +2
The paper surveys automated scientific discovery, from equation discovery and symbolic regression to autonomous discovery systems and agents. It discusses the individual approaches…
Rule Extraction from Binary Neural Networks with Convolutional Rules for Model Validation
Sophie Burkhardt, Jannis Brugger, Nicolas Wagner +3
Most deep neural networks are considered to be black boxes, meaning their output is hard to interpret. In contrast, logical expressions are considered to be more comprehensible sin…