10 papers
TabCausal: Pretraining Across Causal Environments for Tabular Causal Discovery
Zi-Rong Li, Si-Yang Liu, Tian-Zuo Wang +1
Causal discovery aims to recover directed causal relations from observational and interventional data, providing a basis for mechanistic understanding and reliable decision-making.…
FedDyMem: Efficient Federated Learning with Dynamic Memory and Memory-Reduce for Unsupervised Image Anomaly Detection
Silin Chen, Andy Liu, Kangjian Di +4
Unsupervised image anomaly detection (UAD) has become a critical process in industrial and medical applications, but it faces growing challenges due to increasing concerns over dat…
Unleashing the Intrinsic Visual Representation Capability of Multimodal Large Language Models
Hengzhuang Li, Xinsong Zhang, Qiming Peng +6
Multimodal Large Language Models (MLLMs) have demonstrated remarkable proficiency in multimodal tasks. Despite their impressive performance, MLLMs suffer from the modality imbalanc…
Feature-aware Modulation for Learning from Temporal Tabular Data
Hao-Run Cai, Han-Jia Ye
While tabular machine learning has achieved remarkable success, temporal distribution shifts pose significant challenges in real-world deployment, as the relationships between feat…
Understanding the Limits of Deep Tabular Methods with Temporal Shift
Hao-Run Cai, Han-Jia Ye
Deep tabular models have demonstrated remarkable success on i.i.d. data, excelling in a variety of structured data tasks. However, their performance often deteriorates under tempor…
A Closer Look at TabPFN v2: Understanding Its Strengths and Extending Its Capabilities
Han-Jia Ye, Si-Yang Liu, Wei-Lun Chao
Tabular datasets are inherently heterogeneous, presenting significant challenges for developing pre-trained foundation models. The recently introduced transformer-based Tabular Pri…