3 citations · 4 across the 14 of their papers we have counts for
8 papers · 2 filters
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…
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…