12 papers
CausalSteward: An Agentic Divide-Conquer-Combine Copilot for Causal Discovery
Nicholas Tagliapietra, Gian Lorenzo Marchioni, Moritz Willig +3
Learning causal models from high-dimensional data is a significant challenge, particularly in real-world settings where violations of core assumptions lead to causal identifiabilit…
Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation
Nadeem Nazer, Hongkuan Zhou, Lavdim Halilaj +2
Recent vision-language models (VLMs) like CLIP have shown impressive anomaly detection performance under significant distribution shift by utilizing high-level semantic information…
GenAU: Language-Grounded Industrial Anomaly Understanding with Vision-Language Models
Hongkuan Zhou, Tristan Rehm, Nadeem Nazer +3
Industrial inspection requires more than binary anomaly detection: a practical system should determine whether an anomaly exists, localize the defective region, identify the defect…
StableRCA: Robust Graph-Agnostic Mechanism-Level Root Cause Analysis
Xiaoyu Lin, Nicholas Tagliapietra, Kehan Li +2
Root-Cause Analysis (RCA) seeks to identify the variables responsible for abnormal system behavior in complex domains such as manufacturing, cloud computing, and healthcare. Existi…
ORCA: An End-to-End Interactive Copilot for Optimized Root Cause Analysis
Phi Nguyen Xuan, Nicholas Tagliapietra, Lavdim Halilaj +2
Causal analysis is a crucial task in many domains, including manufacturing, social science, and medicine. However, despite recent progress, the conceptual and methodological comple…
Seeing and Knowing in the Wild: Open-domain Visual Entity Recognition with Large-scale Knowledge Graphs via Contrastive Learning
Hongkuan Zhou, Lavdim Halilaj, Sebastian Monka +5
Open-domain visual entity recognition aims to identify and link entities depicted in images to a vast and evolving set of real-world concepts, such as those found in Wikidata. Unli…