activity
20192026
most citedNode Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding

23 citations · 32 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2026

SERL-SQL: Selective Hindsight Distillation for Text-to-SQL Reinforcement Agentic Learning

Tao Liu, Tao Feng, Xiangheng Li +9

Recent Text-to-SQL systems increasingly rely on multi-turn interaction, execution feedback, and reinforcement learning. However, most existing methods use execution correctness onl…

cs.CL2024

Distinguish Confusion in Legal Judgment Prediction via Revised Relation Knowledge

Nuo Xu, Pinghui Wang, Junzhou Zhao +5

Legal Judgment Prediction (LJP) aims to automatically predict a law case's judgment results based on the text description of its facts. In practice, the confusing law articles (or…

cs.LG2023

Federated Learning over Coupled Graphs

Runze Lei, Pinghui Wang, Junzhou Zhao +6

Graphs are widely used to represent the relations among entities. When one owns the complete data, an entire graph can be easily built, therefore performing analysis on the graph i…

cs.LG2020★ 23 cited

Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding

Lin Lan, Pinghui Wang, Xuefeng Du +3

We study the problem of node classification on graphs with few-shot novel labels, which has two distinctive properties: (1) There are novel labels to emerge in the graph; (2) The n…

cs.LG2019★ 9 cited

Meta Reinforcement Learning with Task Embedding and Shared Policy

Lin Lan, Zhenguo Li, Xiaohong Guan +1

Despite significant progress, deep reinforcement learning (RL) suffers from data-inefficiency and limited generalization. Recent efforts apply meta-learning to learn a meta-learner…