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20212026
most citedA Comprehensive Survey on Data Augmentation

20 citations · 31 across the 31 of their papers we have counts for

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8 papers · 1 filter

cs.AI2026

Flat-Consensus Diffusion for Robust Data Reshaping under Noisy Evaluator

Hongyu Cao, Kunpeng Liu, Fei Xie +1

Data shape determines how features are structured, how patterns are separated, and how distributions cover the underlying domain. Poor data shape can make models learn noise rather…

cs.AI2026

StaRPO: Stability-Augmented Reinforcement Policy Optimization

Jinghan Zhang, Fengran Mo, Tharindu Cyril Weerasooriya +5

Reinforcement learning (RL) is effective in enhancing the accuracy of large language models in complex reasoning tasks. Existing RL policy optimization frameworks rely on final-ans…

cs.AI2026

AgentOS: From Application Silos to a Natural Language-Driven Data Ecosystem

Rui Liu, Tao Zhe, Dongjie Wang +5

The rapid emergence of open-source, locally hosted intelligent agents marks a critical inflection point in human-computer interaction. Systems such as OpenClaw demonstrate that Lar…

cs.AI2025

Dataforge: Agentic Platform for Autonomous Data Engineering

Xinyuan Wang, Hongyu Cao, Kunpeng Liu +1

The growing demand for artificial intelligence (AI) applications in materials discovery, molecular modeling, and climate science has made data preparation a critical but labor-inte…

cs.AI2024★ 1 cited

Enhanced Gene Selection in Single-Cell Genomics: Pre-Filtering Synergy and Reinforced Optimization

Weiliang Zhang, Zhen Meng, Dongjie Wang +4

Recent advancements in single-cell genomics necessitate precision in gene panel selection to interpret complex biological data effectively. Those methods aim to streamline the anal…

cs.AI2022

Automated Urban Planning aware Spatial Hierarchies and Human Instructions

Dongjie Wang, Kunpeng Liu, Yanyong Huang +3

Traditional urban planning demands urban experts to spend considerable time and effort producing an optimal urban plan under many architectural constraints. The remarkable imaginat…

cs.AI2022

Reinforced Imitative Graph Learning for Mobile User Profiling

Dongjie Wang, Pengyang Wang, Yanjie Fu +3

Mobile user profiling refers to the efforts of extracting users' characteristics from mobile activities. In order to capture the dynamic varying of user characteristics for generat…

cs.AI2021★ 2 cited

Reinforced Imitative Graph Representation Learning for Mobile User Profiling: An Adversarial Training Perspective

Dongjie Wang, Pengyang Wang, Kunpeng Liu +3

In this paper, we study the problem of mobile user profiling, which is a critical component for quantifying users' characteristics in the human mobility modeling pipeline. Human mo…