16 citations · 63 across the 39 of their papers we have counts for
13 papers · 1 filter
Learning DFAs from Positive Examples Only via Word Counting
Benjamin Bordais, Daniel Neider
Learning finite automata from positive examples has recently gained attention as a powerful approach for understanding, explaining, analyzing, and verifying black-box systems. The…
Focused Chain-of-Thought: Efficient LLM Reasoning via Structured Input Information
Lukas Struppek, Dominik Hintersdorf, Hannah Struppek +2
Recent large language models achieve strong reasoning performance by generating detailed chain-of-thought traces, but this often leads to excessive token use and high inference lat…
Formally Exploring Time-Series Anomaly Detection Evaluation Metrics
Dennis Wagner, Arjun Nair, Billy Joe Franks +24
Undetected anomalies in time series can trigger catastrophic failures in safety-critical systems, such as chemical plant explosions or power grid outages. Although many detection m…
Expediting Reinforcement Learning by Incorporating Knowledge About Temporal Causality in the Environment
Jan Corazza, Hadi Partovi Aria, Daniel Neider +1
Reinforcement learning (RL) algorithms struggle with learning optimal policies for tasks where reward feedback is sparse and depends on a complex sequence of events in the environm…
Reinforcement Learning with Stochastic Reward Machines
Jan Corazza, Ivan Gavran, Daniel Neider
Reward machines are an established tool for dealing with reinforcement learning problems in which rewards are sparse and depend on complex sequences of actions. However, existing a…
On Uniformly Scaling Flows: A Density-Aligned Approach to Deep One-Class Classification
Faried Abu Zaid, Tim Katzke, Emmanuel Müller +1
Unsupervised anomaly detection is often framed around two widely studied paradigms. Deep one-class classification, exemplified by Deep SVDD, learns compact latent representations o…