24 citations · 82 across the 50 of their papers we have counts for
34 papers · 1 filter
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…
Distribution Shift Aware Neural Tabular Learning
Wangyang Ying, Nanxu Gong, Dongjie Wang +5
Tabular learning transforms raw features into optimized spaces for downstream tasks, but its effectiveness deteriorates under distribution shifts between training and testing data.…
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…
Incremental Causal Graph Learning for Online Cyberattack Detection in Cyber-Physical Infrastructures
Arun Vignesh Malarkkan, Dongjie Wang, Haoyue Bai +1
The escalating threat of cyberattacks on real-time critical infrastructures poses serious risks to public safety, demanding detection methods that effectively capture complex syste…
Rethinking Spatio-Temporal Anomaly Detection: A Vision for Causality-Driven Cybersecurity
Arun Vignesh Malarkkan, Haoyue Bai, Xinyuan Wang +3
As cyber-physical systems grow increasingly interconnected and spatially distributed, ensuring their resilience against evolving cyberattacks has become a critical priority. Spatio…
PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models
Xinyuan Wang, Liang Wu, Dongjie Wang +1
Whole-page optimization (WPO) decides how search and recommendation results are surfaced to users, and large language models (LLMs) open a new route to it by treating page generati…
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…
A Survey on Data-Centric AI: Tabular Learning from Reinforcement Learning and Generative AI Perspective
Wangyang Ying, Cong Wei, Nanxu Gong +7
Tabular data is one of the most widely used data formats across various domains such as bioinformatics, healthcare, and marketing. As artificial intelligence moves towards a data-c…
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…
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…
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…
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…
Reinforcement-Enhanced Autoregressive Feature Transformation: Gradient-steered Search in Continuous Space for Postfix Expressions
Dongjie Wang, Meng Xiao, Min Wu +3
Feature transformation aims to generate new pattern-discriminative feature space from original features to improve downstream machine learning (ML) task performances. However, the…
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…
Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting
Wei Fan, Pengyang Wang, Dongkun Wang +3
The distribution shift in Time Series Forecasting (TSF), indicating series distribution changes over time, largely hinders the performance of TSF models. Existing works towards dis…