3 citations · 4 across the 14 of their papers we have counts for
14 papers
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…
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…
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…
Deep Neural Network Calibration by Reducing Classifier Shift with Stochastic Masking
Jiani Ni, He Zhao, Yibo Yang +1
In recent years, deep neural networks (DNNs) have shown competitive results in many fields. Despite this success, they often suffer from poor calibration, especially in safety-crit…
Merging Smarter, Generalizing Better: Enhancing Model Merging on OOD Data
Bingjie Zhang, Hongkang Li, Changlong Shi +5
Multi-task learning (MTL) concurrently trains a model on diverse task datasets to exploit common features, thereby improving overall performance across the tasks. Recent studies ha…
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…
Beyond Words: Augmenting Discriminative Richness via Diffusions in Unsupervised Prompt Learning
Hairui Ren, Fan Tang, He Zhao +3
Fine-tuning vision-language models (VLMs) with large amounts of unlabeled data has recently garnered significant interest. However, a key challenge remains the lack of high-quality…
Balancing Two Classifiers via A Simplex ETF Structure for Model Calibration
Jiani Ni, He Zhao, Jintong Gao +2
In recent years, deep neural networks (DNNs) have demonstrated state-of-the-art performance across various domains. However, despite their success, they often face calibration issu…
FedLWS: Federated Learning with Adaptive Layer-wise Weight Shrinking
Changlong Shi, Jinmeng Li, He Zhao +2
In Federated Learning (FL), weighted aggregation of local models is conducted to generate a new global model, and the aggregation weights are typically normalized to 1. A recent st…
FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors
Changlong Shi, He Zhao, Bingjie Zhang +3
Federated Learning (FL) has emerged as a promising framework for distributed machine learning, enabling collaborative model training without sharing local data, thereby preserving…
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…
Extracting Clean and Balanced Subset for Noisy Long-tailed Classification
Zhuo Li, He Zhao, Zhen Li +3
Real-world datasets usually are class-imbalanced and corrupted by label noise. To solve the joint issue of long-tailed distribution and label noise, most previous works usually aim…