Adaptive Anomaly Detection in Evolving Network Environments

Distribution shift, a change in the statistical properties of data over time, poses a critical challenge for deep learning anomaly detection systems. Existing anomaly detection systems often struggle to adapt to these shifts. Specifically, systems based on supervised learning require costly manua...

Revisiting Data Auditing in Large Vision-Language Models

With the surge of large language models LLMs, Large Vision-Language Models VLMs--which integrate vision encoders with LLMs for accurate visual grounding--have shown great potential in tasks like generalist agents and robotic control. However, VLMs are typically trained on massive web-scraped...

Avoiding Leakage Poisoning: Concept Interventions under Distribution Shifts

In this paper, we investigate how concept-based models CMs respond to out-of-distribution OOD inputs. CMs are interpretable neural architectures that first predict a set of high-level concepts e.g., stripes, black and then predict a task label from those concepts. In particular, we study the impa...