9 citations · 12 across the 16 of their papers we have counts for
9 papers · 1 filter
Typed Decision Models on Tabular Data
Baowen Zhang, Hangting Ye, Hongyuan Zha
Typed decision models are language models that answer a question with a fixed set of possible answers by returning a probability for each answer instead of generating text. We stud…
Understanding Errors in LLM-Based Question Answering over Imperfect Tables
Baowen Zhang, Wei Fan, Ruman Wang +1
Answering questions over imperfect tables requires handling errors that can affect the answer. We investigate two challenges for large language models (LLMs): whether error discove…
Beyond Distribution Matching: Semantics-Consistent Tabular Diffusion with Weak Semantic Priors
Yili Wang, Ruxue Shi, Mengnan Du +3
Synthetic tabular data can match real data distributions while still violating the semantic constraints that govern valid tabular rows. This reveals a key limitation of existing ta…
Creation begins with understanding: LLMs as strategy designers for privacy-preserving tabular data synthesis
Jinmeng Li, Quan Zhang, Hangting Ye +4
Sharing tabular data in high-stakes domains is constrained by privacy regulations. Synthetic data offer a promising alternative, but deep generative models are costly to train and…
ScaFE: Data-Efficient Scar Classification with LLM-Generated Clinical Feature Programs
Ruman Wang, Hangting Ye
Classifying pathological scars from clinical photographs requires distinguishing keloids from hypertrophic scars despite limited expert-labeled data and substantial acquisition var…
When LLMs Analyze Scars: From Images to Clinically-Meaningful Features
Ruman Wang, Hangting Ye
Medical image classification faces a fundamental dilemma: while deep learning models achieve remarkable performance at scale, real-world clinical scenarios often suffer from severe…
TAROT: Task-Adaptive Refinement of LLM-prior Graphs for Few-shot Tabular Learning
Ruxue Shi, Yili Wang, Mengnan Du +3
Few-shot tabular learning provides a cost-effective approach for real-world applications where annotation is costly and collecting sufficient samples for new tasks is difficult. Ex…
Deep Tabular Representation Corrector
Hangting Ye, Peng Wang, Wei Fan +4
Tabular data have been playing a mostly important role in diverse real-world fields, such as healthcare, engineering, finance, etc. The recent success of deep learning has fostered…
Calibrating Tabular Anomaly Detection via Optimal Transport
Hangting Ye, He Zhao, Wei Fan +4
Tabular anomaly detection (TAD) remains challenging due to the heterogeneity of tabular data: features lack natural relationships, vary widely in distribution and scale, and exhibi…