14 papers · 1 filter
MacrOData: New Benchmarks of Thousands of Datasets for Tabular Outlier Detection
Xueying Ding, Simon Klüttermann, Haomin Wen +2
Quality benchmarks are essential for fairly and accurately tracking scientific progress and enabling practitioners to make informed methodological choices. Outlier detection (OD) o…
From Zero to Hero: Advancing Zero-Shot Foundation Models for Tabular Outlier Detection
Xueying Ding, Haomin Wen, Simon Klüttermann +1
Outlier detection (OD) is widely used in practice; but its effective deployment on new tasks is hindered by lack of labeled outliers, which makes algorithm and hyperparameter selec…
Activation Steering for Synthetic Data Generation: The Role of Diversity in Downstream Safety Detection
Vijeta Deshpande, Tootiya Giyahchi, Veena Padmanabhan +2
Safety detection models require examples of HHH (Helpful, Harmless, Honest)-violating outputs for robust generalization, however such examples are scarce. Activation Steering (AS)…
Toward Privileged Foundation Models:LUPI for Accelerated and Improved Learning
Xueying Ding, Leman Akoglu
Training foundation models is computationally intensive and often slow to converge. We introduce PIQL,Privileged Information for Quick and Quality Learning, the first framework to…
VIP-COP: Context Optimization for Tabular Foundation Models
Yilong Chen, Xueying Ding, Leman Akoglu
Tabular foundation models (TFMs) have emerged as a powerful paradigm for in-context learning on structured data, enabling direct prediction on new tabular tasks without task-specif…
Mitra: Mixed Synthetic Priors for Enhancing Tabular Foundation Models
Xiyuan Zhang, Danielle C. Maddix, Junming Yin +11
Since the seminal work of TabPFN, research on tabular foundation models (TFMs) based on in-context learning (ICL) has challenged long-standing paradigms in machine learning. Withou…