4 citations · 8 across the 16 of their papers we have counts for
7 papers · 1 filter
When Correlations Mislead: Confounder-Aware Multi-View Urban Region Representation Learning
Sean Bin Yang, Ying Sun, Zongyi Xu +6
Urban region representation learning commonly combines heterogeneous data sources, such as mobility flows, points of interest, and land-use information, to support tasks including…
Robustness Analysis of Agentic AI to Inconsistent and Incomplete Tool Responses
Jiachen Xu, Torben Bach Pedersen, Zhongming Yao +2
Tool-using agents increasingly rely on external tools to complete multi-step tasks, but tool returns can fail in different ways and require different recovery actions. Existing rob…
Using Lower-Bound Representations for Trajectory Similarity Learning
Liwei Deng, Haotian Meng, Yupu Zhang +4
Trajectory similarity learning is fundamental to efficient trajectory retrieval under complex distance measures. Existing learning-based methods typically rely on embeddings traine…
Quantum Compression for Distributed Entanglement
Jan Østergaard, Shashi Raj Pandey, Christophe Biscio +2
We study compression strategies for multipartite entanglement distribution under uncertainty in the partitioning of the quantum state. When the partition is not known at the time o…
Reliable AI Needs to Externalize Implicit Knowledge: A Human-AI Collaboration Perspective
Hengyu Liu, Tianyi Li, Zhihong Cui +5
This position paper argues that reliable AI requires infrastructure for human validation of implicit knowledge. AI learns from both explicit knowledge (papers, documentation, struc…
CLEAR: A Knowledge-Centric Vessel Trajectory Analysis Platform
Hengyu Liu, Tianyi Li, Haoyu Wang +5
Vessel trajectory data from the Automatic Identification System (AIS) is used widely in maritime analytics. Yet, analysis is difficult for non-expert users due to the incompletenes…
msData: A Millisecond-Resolution Network Dataset for Advancing Time Series Foundation Models
Subina Khanal, Seshu Tirupathi, Merim Dzaferagic +2
Time series foundation models (TSFMs) require diverse, real-world datasets to adapt across varying domains and temporal frequencies. However, current large-scale datasets predomina…