13 papers · 1 filter
Detecting Diffusion-Generated Time Series Under Generator Shift
Zhi Wen Soi, Aditya Shankar, Gert Lek +4
The boundary between real and diffusion-generated time series is becoming increasingly difficult to draw, yet detection in this domain remains underexplored, especially when the ge…
Reinforcement Learning with Symbolic Reward Machines
Thomas Krug, Daniel Neider
Reward Machines (RMs) are an established mechanism in Reinforcement Learning (RL) to represent and learn sparse, temporally extended tasks with non-Markovian rewards. RMs rely on h…
VeriFlow: Modeling Distributions for Neural Network Verification
Faried Abu Zaid, Daniel Neider, Mustafa Yalçıner
Formal verification has emerged as a promising method to ensure the safety and reliability of neural networks. However, many relevant properties, such as fairness or global robustn…
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…