44 citations · 88 across the 13 of their papers we have counts for
13 papers
ProSPy: A Profiling-Driven SQL-Python Agentic Framework for Enterprise Text-to-SQL
Zhaorui Yang, Huawei Zheng, Sen Yang +14
Large language models have substantially advanced Text-to-SQL systems, yet applying them to enterprise-scale databases remains challenging. Real-world databases often contain large…
EviLink: Multi-Path Schema Linking with Uncertainty-Guided Evidence Acquisition for Large-Scale Text-to-SQL
Huawei Zheng, Sen Yang, Zhaorui Yang +12
Schema linking is a difficult and important step in large-scale Text-to-SQL, where systems must identify a compact yet sufficient schema context from large and ambiguous databases.…
DataLab: A Unified Platform for LLM-Powered Business Intelligence
Luoxuan Weng, Yinghao Tang, Yingchaojie Feng +18
Business intelligence (BI) transforms large volumes of data within modern organizations into actionable insights for informed decision-making. Recently, large language model (LLM)-…
InsightLens: Augmenting LLM-Powered Data Analysis with Interactive Insight Management and Navigation
Luoxuan Weng, Xingbo Wang, Junyu Lu +5
The proliferation of large language models (LLMs) has revolutionized the capabilities of natural language interfaces (NLIs) for data analysis. LLMs can perform multi-step and compl…
Self-Distillation Bridges Distribution Gap in Language Model Fine-Tuning
Zhaorui Yang, Tianyu Pang, Haozhe Feng +4
The surge in Large Language Models (LLMs) has revolutionized natural language processing, but fine-tuning them for specific tasks often encounters challenges in balancing performan…
CoSDA: Continual Source-Free Domain Adaptation
Haozhe Feng, Zhaorui Yang, Hesun Chen +5
Without access to the source data, source-free domain adaptation (SFDA) transfers knowledge from a source-domain trained model to target domains. Recently, SFDA has gained populari…
BAFFLE: A Baseline of Backpropagation-Free Federated Learning
Haozhe Feng, Tianyu Pang, Chao Du +3
Federated learning (FL) is a general principle for decentralized clients to train a server model collectively without sharing local data. FL is a promising framework with practical…
DPVisCreator: Incorporating Pattern Constraints to Privacy-preserving Visualizations via Differential Privacy
Jiehui Zhou, Xumeng Wang, Jason K. Wong +8
Data privacy is an essential issue in publishing data visualizations. However, it is challenging to represent multiple data patterns in privacy-preserving visualizations. The prior…
Prior-Enhanced Few-Shot Segmentation with Meta-Prototypes
Jian-Wei Zhang, Lei Lv, Yawei Luo +3
Few-shot segmentation~(FSS) performance has been extensively promoted by introducing episodic training and class-wise prototypes. However, the FSS problem remains challenging due t…
SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations
Hao-Zhe Feng, Kezhi Kong, Minghao Chen +3
Semi-supervised variational autoencoders (VAEs) have obtained strong results, but have also encountered the challenge that good ELBO values do not always imply accurate inference r…
KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation
Hao-Zhe Feng, Zhaoyang You, Minghao Chen +5
Conventional unsupervised multi-source domain adaptation (UMDA) methods assume all source domains can be accessed directly. This neglects the privacy-preserving policy, that is, al…
GraphFederator: Federated Visual Analysis for Multi-party Graphs
Dongming Han, Wei Chen, Rusheng Pan +8
This paper presents GraphFederator, a novel approach to construct joint representations of multi-party graphs and supports privacy-preserving visual analysis of graphs. Inspired by…
An Interactive Insight Identification and Annotation Framework for Power Grid Pixel Maps using DenseU-Hierarchical VAE
Tianye Zhang, Haozhe Feng, Zexian Chen +4
Insights in power grid pixel maps (PGPMs) refer to important facility operating states and unexpected changes in the power grid. Identifying insights helps analysts understand the…