9 citations · 12 across the 16 of their papers we have counts for
19 papers
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…
LLM as an Algorithmist: Enhancing Anomaly Detectors via Programmatic Synthesis
Hangting Ye, Jinmeng Li, He Zhao +4
Existing anomaly detection (AD) methods for tabular data usually rely on some assumptions about anomaly patterns, leading to inconsistent performance in real-world scenarios. While…
AOT*: Efficient Synthesis Planning via LLM-Empowered AND-OR Tree Search
Xiaozhuang Song, Xuanhao Pan, Xinjian Zhao +4
Retrosynthesis planning enables the discovery of viable synthetic routes for target molecules, playing a crucial role in domains like drug discovery and materials design. Multi-ste…
LLM Empowered Prototype Learning for Zero and Few-Shot Tasks on Tabular Data
Peng Wang, Dongsheng Wang, He Zhao +3
Recent breakthroughs in large language models (LLMs) have opened the door to in-depth investigation of their potential in tabular data modeling. However, effectively utilizing adva…
Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning
Ruxue Shi, Hengrui Gu, Hangting Ye +3
Few-shot tabular learning, in which machine learning models are trained with a limited amount of labeled data, provides a cost-effective approach to addressing real-world challenge…
LLM Meeting Decision Trees on Tabular Data
Hangting Ye, Jinmeng Li, He Zhao +2
Tabular data have been playing a vital role in diverse real-world fields, including healthcare, finance, etc. With the recent success of Large Language Models (LLMs), early explora…
MedGNN: Towards Multi-resolution Spatiotemporal Graph Learning for Medical Time Series Classification
Wei Fan, Jingru Fei, Dingyu Guo +5
Medical time series has been playing a vital role in real-world healthcare systems as valuable information in monitoring health conditions of patients. Accurate classification for…
PTaRL: Prototype-based Tabular Representation Learning via Space Calibration
Hangting Ye, Wei Fan, Xiaozhuang Song +4
Tabular data have been playing a mostly important role in diverse real-world fields, such as healthcare, engineering, finance, etc. With the recent success of deep learning, many t…
Deep Frequency Derivative Learning for Non-stationary Time Series Forecasting
Wei Fan, Kun Yi, Hangting Ye +3
While most time series are non-stationary, it is inevitable for models to face the distribution shift issue in time series forecasting. Existing solutions manipulate statistical me…
UADB: Unsupervised Anomaly Detection Booster
Hangting Ye, Zhining Liu, Xinyi Shen +6
Unsupervised Anomaly Detection (UAD) is a key data mining problem owing to its wide real-world applications. Due to the complete absence of supervision signals, UAD methods rely on…
IMBENS: Ensemble Class-imbalanced Learning in Python
Zhining Liu, Jian Kang, Hanghang Tong +1
imbalanced-ensemble, abbreviated as imbens, is an open-source Python toolbox for leveraging the power of ensemble learning to address the class imbalance problem. It provides stand…