1 citations · 7 across the 31 of their papers we have counts for
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Hierarchical and Permutation-Invariant Feature Transformation Learning via Policy-Guided Embedding Search
Rui Liu, Tao Zhe, Yanyong Huang +5
Feature transformation improves predictive performance on tabular data by constructing informative abstractions from raw features. Recent generative approaches encode transformatio…
Adaptive Multi-Agent Feature Selection for Personalized Fall Risk Prevention
Chang Liu, Ladda Thiamwong, Yanjie Fu +1
Falls among older adults represent a major public health challenge driven by complex, time-varying interactions across multiple risk domains. Effective fall risk factor identificat…
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…
Permutation-Invariant Representation Learning for Robust and Privacy-Preserving Feature Selection
Rui Liu, Tao Zhe, Yanjie Fu +3
Feature selection eliminates redundancy among features to improve downstream task performance while reducing computational overhead. Existing methods often struggle to capture intr…
Causal Graph Profiling via Structural Divergence for Robust Anomaly Detection in Cyber-Physical Systems
Arun Vignesh Malarkkan, Haoyue Bai, Dongjie Wang +1
With the growing complexity of cyberattacks targeting critical infrastructures such as water treatment networks, there is a pressing need for robust anomaly detection strategies th…
Biological Pathway Guided Gene Selection Through Collaborative Reinforcement Learning
Ehtesamul Azim, Dongjie Wang, Tae Hyun Hwang +2
Gene selection in high-dimensional genomic data is essential for understanding disease mechanisms and improving therapeutic outcomes. Traditional feature selection methods effectiv…
Continuous Optimization for Feature Selection with Permutation-Invariant Embedding and Policy-Guided Search
Rui Liu, Rui Xie, Zijun Yao +2
Feature selection removes redundant features to enhanc performance and computational efficiency in downstream tasks. Existing works often struggle to capture complex feature intera…
LEKA:LLM-Enhanced Knowledge Augmentation
Xinhao Zhang, Jinghan Zhang, Fengran Mo +3
Humans excel in analogical learning and knowledge transfer and, more importantly, possess a unique understanding of identifying appropriate sources of knowledge. From a model's per…
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…
Reinforcement Feature Transformation for Polymer Property Performance Prediction
Xuanming Hu, Dongjie Wang, Wangyang Ying +1
Polymer property performance prediction aims to forecast specific features or attributes of polymers, which has become an efficient approach to measuring their performance. However…
Revolutionizing Biomarker Discovery: Leveraging Generative AI for Bio-Knowledge-Embedded Continuous Space Exploration
Wangyang Ying, Dongjie Wang, Xuanming Hu +3
Biomarker discovery is vital in advancing personalized medicine, offering insights into disease diagnosis, prognosis, and therapeutic efficacy. Traditionally, the identification an…
Unsupervised Generative Feature Transformation via Graph Contrastive Pre-training and Multi-objective Fine-tuning
Wangyang Ying, Dongjie Wang, Xuanming Hu +3
Feature transformation is to derive a new feature set from original features to augment the AI power of data. In many science domains such as material performance screening, while…
Evolutionary Large Language Model for Automated Feature Transformation
Nanxu Gong, Chandan K. Reddy, Wangyang Ying +2
Feature transformation aims to reconstruct the feature space of raw features to enhance the performance of downstream models. However, the exponential growth in the combinations of…
Neuro-Symbolic Embedding for Short and Effective Feature Selection via Autoregressive Generation
Nanxu Gong, Wangyang Ying, Dongjie Wang +1
Feature selection aims to identify the optimal feature subset for enhancing downstream models. Effective feature selection can remove redundant features, save computational resourc…
Knockoff-Guided Feature Selection via A Single Pre-trained Reinforced Agent
Xinyuan Wang, Dongjie Wang, Wangyang Ying +3
Feature selection prepares the AI-readiness of data by eliminating redundant features. Prior research falls into two primary categories: i) Supervised Feature Selection, which iden…
Feature Selection as Deep Sequential Generative Learning
Wangyang Ying, Dongjie Wang, Haifeng Chen +1
Feature selection aims to identify the most pattern-discriminative feature subset. In prior literature, filter (e.g., backward elimination) and embedded (e.g., Lasso) methods have…
Dual-stage Flows-based Generative Modeling for Traceable Urban Planning
Xuanming Hu, Wei Fan, Dongjie Wang +3
Urban planning, which aims to design feasible land-use configurations for target areas, has become increasingly essential due to the high-speed urbanization process in the modern e…
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…