140 citations · 324 across the 56 of their papers we have counts for
66 papers
Temporal-Causal Inference for Reinforcement Learning via Automata Learning
Jan Corazza, Daniil Kaminskyi, Simon Lutz +4
We consider reinforcement learning in environments with dynamics that undergo an irreversible phase transition governed by a hidden temporal pattern. The agent observes the base st…
Making Embodied AI Reliable: A Community Agenda from Testing to Formal Verification
Xi Zheng, Dulanga Weerakoon, Yintong Huo +8
Embodied AI systems are increasingly deployed in open-world environments, yet ensuring their reliability remains a fundamental challenge. Drawing on discussions from the AAAI'26 Br…
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…
Evaluating Counterfactual Explanation Methods on Incomplete Inputs
Francesco Leofante, Daniel Neider, Mustafa Yalçıner
Existing algorithms for generating Counterfactual Explanations (CXs) for Machine Learning (ML) typically assume fully specified inputs. However, real-world data often contains miss…
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…
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…