2 citations · 3 across the 9 of their papers we have counts for
12 papers
Thinking in Depth: Retrospective Inference for Tabular Foundation Models
Hao-Run Cai, Si-Yang Liu, Zi-Jian Cheng +8
Tabular foundation models (TFMs) are pretrained across diverse tabular tasks and make predictions on a new table at inference time using its labeled examples as context. Most recen…
TabCausal: Pretraining Across Causal Environments for Tabular Causal Discovery
Zi-Rong Li, Si-Yang Liu, Tian-Zuo Wang +1
Causal discovery aims to recover directed causal relations from observational and interventional data, providing a basis for mechanistic understanding and reliable decision-making.…
Unleashing the Intrinsic Visual Representation Capability of Multimodal Large Language Models
Hengzhuang Li, Xinsong Zhang, Qiming Peng +6
Multimodal Large Language Models (MLLMs) have demonstrated remarkable proficiency in multimodal tasks. Despite their impressive performance, MLLMs suffer from the modality imbalanc…
Feature-aware Modulation for Learning from Temporal Tabular Data
Hao-Run Cai, Han-Jia Ye
While tabular machine learning has achieved remarkable success, temporal distribution shifts pose significant challenges in real-world deployment, as the relationships between feat…
Make Still Further Progress: Chain of Thoughts for Tabular Data Leaderboard
Si-Yang Liu, Qile Zhou, Han-Jia Ye
Tabular data, a fundamental data format in machine learning, is predominantly utilized in competitions and real-world applications. The performance of tabular models--such as gradi…
Representation Learning for Tabular Data: A Comprehensive Survey
Jun-Peng Jiang, Si-Yang Liu, Hao-Run Cai +2
Tabular data, structured as rows and columns, is among the most prevalent data types in machine learning classification and regression applications. Models for learning from tabula…
Efficient Evaluation of Large Language Models via Collaborative Filtering
Xu-Xiang Zhong, Chao Yi, Han-Jia Ye
With the development of Large Language Models (LLMs), numerous benchmarks have been proposed to measure and compare the capabilities of different LLMs. However, evaluating LLMs is…
TabPFN Unleashed: A Scalable and Effective Solution to Tabular Classification Problems
Si-Yang Liu, Han-Jia Ye
TabPFN has emerged as a promising in-context learning model for tabular data, capable of directly predicting the labels of test samples given labeled training examples. It has demo…
FedDyMem: Efficient Federated Learning with Dynamic Memory and Memory-Reduce for Unsupervised Image Anomaly Detection
Silin Chen, Andy Liu, Kangjian Di +4
Unsupervised image anomaly detection (UAD) has become a critical process in industrial and medical applications, but it faces growing challenges due to increasing concerns over dat…
Understanding the Limits of Deep Tabular Methods with Temporal Shift
Hao-Run Cai, Han-Jia Ye
Deep tabular models have demonstrated remarkable success on i.i.d. data, excelling in a variety of structured data tasks. However, their performance often deteriorates under tempor…
A Closer Look at TabPFN v2: Understanding Its Strengths and Extending Its Capabilities
Han-Jia Ye, Si-Yang Liu, Wei-Lun Chao
Tabular datasets are inherently heterogeneous, presenting significant challenges for developing pre-trained foundation models. The recently introduced transformer-based Tabular Pri…
TALENT: A Tabular Analytics and Learning Toolbox
Si-Yang Liu, Hao-Run Cai, Qi-Le Zhou +1
Tabular data is one of the most common data sources in machine learning. Although a wide range of classical methods demonstrate practical utilities in this field, deep learning met…