107 citations · 170 across the 26 of their papers we have counts for
26 papers
Unsupervised Learning for Class Distribution Mismatch
Pan Du, Wangbo Zhao, Xinai Lu +8
Class distribution mismatch (CDM) refers to the discrepancy between class distributions in training data and target tasks. Previous methods address this by designing classifiers to…
Personalized Clustering via Targeted Representation Learning
Xiwen Geng, Suyun Zhao, Yixin Yu +5
Clustering traditionally aims to reveal a natural grouping structure within unlabeled data. However, this structure may not always align with users' preferences. In this paper, we…
Is Diffusion Model Safe? Severe Data Leakage via Gradient-Guided Diffusion Model
Jiayang Meng, Tao Huang, Hong Chen +1
Gradient leakage has been identified as a potential source of privacy breaches in modern image processing systems, where the adversary can completely reconstruct the training image…
E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model
Xinmei Huang, Haoyang Li, Jing Zhang +7
Database knob tuning is a significant challenge for database administrators, as it involves tuning a large number of configuration knobs with continuous or discrete values to achie…
Open-World Semi-Supervised Learning for Node Classification
Yanling Wang, Jing Zhang, Lingxi Zhang +5
Open-world semi-supervised learning (Open-world SSL) for node classification, that classifies unlabeled nodes into seen classes or multiple novel classes, is a practical but under-…
Streamlining Redundant Layers to Compress Large Language Models
Xiaodong Chen, Yuxuan Hu, Jing Zhang +3
This paper introduces LLM-Streamline, a pioneer work on layer pruning for large language models (LLMs). It is based on the observation that different layers have varying impacts on…
CodeS: Towards Building Open-source Language Models for Text-to-SQL
Haoyang Li, Jing Zhang, Hanbing Liu +7
Language models have shown promising performance on the task of translating natural language questions into SQL queries (Text-to-SQL). However, most of the state-of-the-art (SOTA)…
FOSS: A Self-Learned Doctor for Query Optimizer
Kai Zhong, Luming Sun, Tao Ji +2
Various works have utilized deep learning to address the query optimization problem in database system. They either learn to construct plans from scratch in a bottom-up manner or s…
Diversifying Question Generation over Knowledge Base via External Natural Questions
Shasha Guo, Jing Zhang, Xirui Ke +2
Previous methods on knowledge base question generation (KBQG) primarily focus on enhancing the quality of a single generated question. Recognizing the remarkable paraphrasing abili…
Semi-Supervised Learning via Weight-aware Distillation under Class Distribution Mismatch
Pan Du, Suyun Zhao, Zisen Sheng +2
Semi-Supervised Learning (SSL) under class distribution mismatch aims to tackle a challenging problem wherein unlabeled data contain lots of unknown categories unseen in the labele…
: Enhancing Structured Pruning via PCA Projection
Yuxuan Hu, Jing Zhang, Zhe Zhao +4
Structured pruning is a widely used technique for reducing the size of pre-trained language models (PLMs), but current methods often overlook the potential of compressing the hidde…
FC-KBQA: A Fine-to-Coarse Composition Framework for Knowledge Base Question Answering
Lingxi Zhang, Jing Zhang, Yanling Wang +5
The generalization problem on KBQA has drawn considerable attention. Existing research suffers from the generalization issue brought by the entanglement in the coarse-grained model…
RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL
Haoyang Li, Jing Zhang, Cuiping Li +1
One of the recent best attempts at Text-to-SQL is the pre-trained language model. Due to the structural property of the SQL queries, the seq2seq model takes the responsibility of p…
Oracle-guided Contrastive Clustering
Mengdie Wang, Liyuan Shang, Suyun Zhao +4
Deep clustering aims to learn a clustering representation through deep architectures. Most of the existing methods usually conduct clustering with the unique goal of maximizing clu…
FLDP: Flexible strategy for local differential privacy
Dan Zhao, Suyun Zhao, Ruixuan Liu +3
Local differential privacy (LDP), a technique applying unbiased statistical estimations instead of real data, is often adopted in data collection. In particular, this technique is…
Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering
Jing Zhang, Xiaokang Zhang, Jifan Yu +4
Recent works on knowledge base question answering (KBQA) retrieve subgraphs for easier reasoning. A desired subgraph is crucial as a small one may exclude the answer but a large on…
An Accelerator for Rule Induction in Fuzzy Rough Theory
Suyun Zhao, Zhigang Dai, Xizhao Wang +4
Rule-based classifier, that extract a subset of induced rules to efficiently learn/mine while preserving the discernibility information, plays a crucial role in human-explainable a…
Injecting Numerical Reasoning Skills into Knowledge Base Question Answering Models
Yu Feng, Jing Zhang, Xiaokang Zhang +3
Embedding-based methods are popular for Knowledge Base Question Answering (KBQA), but few current models have numerical reasoning skills and thus struggle to answer ordinal constra…
A Multi-Strategy based Pre-Training Method for Cold-Start Recommendation
Bowen Hao, Hongzhi Yin, Jing Zhang +2
Cold-start problem is a fundamental challenge for recommendation tasks. The recent self-supervised learning (SSL) on Graph Neural Networks (GNNs) model, PT-GNN, pre-trains the GNN…
Self-supervised Graph Learning for Occasional Group Recommendation
Bowen Hao, Hongzhi Yin, Cuiping Li +1
As an important branch in Recommender System, occasional group recommendation has received more and more attention. In this scenario, each occasional group (cold-start group) has n…
Recommending Courses in MOOCs for Jobs: An Auto Weak Supervision Approach
Bowen Hao, Jing Zhang, Cuiping Li +2
The proliferation of massive open online courses (MOOCs) demands an effective way of course recommendation for jobs posted in recruitment websites, especially for the people who ta…
Pre-Training Graph Neural Networks for Cold-Start Users and Items Representation
Bowen Hao, Jing Zhang, Hongzhi Yin +2
Cold-start problem is a fundamental challenge for recommendation tasks. Despite the recent advances on Graph Neural Networks (GNNs) incorporate the high-order collaborative signal…
CODE: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking
Bo Chen, Jing Zhang, Xiaokang Zhang +6
Expert finding, a popular service provided by many online websites such as Expertise Finder, LinkedIn, and AMiner, is beneficial to seeking candidate qualifications, consultants, a…
JarKA: Modeling Attribute Interactions for Cross-lingual Knowledge Alignment
Bo Chen, Jing Zhang, Xiaobin Tang +2
Abstract. Cross-lingual knowledge alignment is the cornerstone in building a comprehensive knowledge graph (KG), which can benefit various knowledge-driven applications. As the str…