20 citations · 31 across the 31 of their papers we have counts for
16 papers · 1 filter
Causally-Guided Diffusion for Stable Feature Selection
Arun Vignesh Malarkkan, Xinyuan Wang, Kunpeng Liu +2
Feature selection is fundamental to robust data-centric AI, but most existing methods optimize predictive performance under a single data distribution. This often selects spurious…
Sim2Act: Robust Simulation-to-Decision Learning via Adversarial Calibration and Group-Relative Perturbation
Hongyu Cao, Jinghan Zhang, Kunpeng Liu +5
Simulation-to-decision learning enables safe policy training in digital environments without risking real-world deployment, and has become essential in mission-critical domains suc…
Heterogeneous Multi-Agent Reinforcement Learning with Attention for Cooperative and Scalable Feature Transformation
Tao Zhe, Huazhen Fang, Kunpeng Liu +3
Feature transformation enhances downstream task performance by generating informative features through mathematical feature crossing. Despite the advancements in deep learning, fea…
Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation
Dongjie Wang, Yanyong Huang, Wangyang Ying +11
Tabular data is one of the most widely used formats across industries, driving critical applications in areas such as finance, healthcare, and marketing. In the era of data-centric…
Iterative Feature Space Optimization through Incremental Adaptive Evaluation
Yanping Wu, Yanyong Huang, Zhengzhang Chen +5
Iterative feature space optimization involves systematically evaluating and adjusting the feature space to improve downstream task performance. However, existing works suffer from…
Topology-aware Reinforcement Feature Space Reconstruction for Graph Data
Wangyang Ying, Haoyue Bai, Kunpeng Liu +1
Feature space is an environment where data points are vectorized to represent the original dataset. Reconstructing a good feature space is essential to augment the AI power of data…
A Comprehensive Survey on Data Augmentation
Zaitian Wang, Pengfei Wang, Kunpeng Liu +6
Data augmentation is a series of techniques that generate high-quality artificial data by manipulating existing data samples. By leveraging data augmentation techniques, AI models…
Self-optimizing Feature Generation via Categorical Hashing Representation and Hierarchical Reinforcement Crossing
Wangyang Ying, Dongjie Wang, Kunpeng Liu +2
Feature generation aims to generate new and meaningful features to create a discriminative representation space.A generated feature is meaningful when the generated feature is from…
Feature Interaction Aware Automated Data Representation Transformation
Ehtesamul Azim, Dongjie Wang, Kunpeng Liu +2
Creating an effective representation space is crucial for mitigating the curse of dimensionality, enhancing model generalization, addressing data sparsity, and leveraging classical…
Traceable Group-Wise Self-Optimizing Feature Transformation Learning: A Dual Optimization Perspective
Meng Xiao, Dongjie Wang, Min Wu +4
Feature transformation aims to reconstruct an effective representation space by mathematically refining the existing features. It serves as a pivotal approach to combat the curse o…
Traceable Automatic Feature Transformation via Cascading Actor-Critic Agents
Meng Xiao, Dongjie Wang, Min Wu +5
Feature transformation for AI is an essential task to boost the effectiveness and interpretability of machine learning (ML). Feature transformation aims to transform original data…
Self-Optimizing Feature Transformation
Meng Xiao, Dongjie Wang, Min Wu +4
Feature transformation aims to extract a good representation (feature) space by mathematically transforming existing features. It is crucial to address the curse of dimensionality,…
Group-wise Reinforcement Feature Generation for Optimal and Explainable Representation Space Reconstruction
Dongjie Wang, Yanjie Fu, Kunpeng Liu +2
Representation (feature) space is an environment where data points are vectorized, distances are computed, patterns are characterized, and geometric structures are embedded. Extrac…
Feature and Instance Joint Selection: A Reinforcement Learning Perspective
Wei Fan, Kunpeng Liu, Hao Liu +3
Feature selection and instance selection are two important techniques of data processing. However, such selections have mostly been studied separately, while existing work towards…
Efficient Reinforced Feature Selection via Early Stopping Traverse Strategy
Kunpeng Liu, Pengfei Wang, Dongjie Wang +3
In this paper, we propose a single-agent Monte Carlo based reinforced feature selection (MCRFS) method, as well as two efficiency improvement strategies, i.e., early stopping (ES)…
Automated Feature-Topic Pairing: Aligning Semantic and Embedding Spaces in Spatial Representation Learning
Dongjie Wang, Kunpeng Liu, David Mohaisen +3
Automated characterization of spatial data is a kind of critical geographical intelligence. As an emerging technique for characterization, Spatial Representation Learning (SRL) use…