1 citations · 7 across the 31 of their papers we have counts for
31 papers
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…
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…
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…
Mitigating Shortcut Reasoning in Language Models: A Gradient-Aware Training Approach
Hongyu Cao, Kunpeng Liu, Dongjie Wang +1
Large language models exhibit strong reasoning capabilities, yet often rely on shortcuts such as surface pattern matching and answer memorization rather than genuine logical infere…
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…
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…
QUIETT: Query-Independent Table Transformation for Robust Reasoning
Gaurav Najpande, Tampu Ravi Kumar, Manan Roy Choudhury +3
Real-world tables often contain schema inconsistencies, heterogeneous value formats, and implicit relational structures that degrade table reasoning and question answering. Existin…
City Editing: Hierarchical Agentic Execution for Dependency-Aware Urban Geospatial Modification
Rui Liu, Steven Jige Quan, Zhong-Ren Peng +6
Urban renewal requires incremental modifications to existing geospatial plans, yet manually updating complex layouts under spatial constraints is labor-intensive and error-prone. T…
Autonomous Data Agents: A New Opportunity for Smart Data
Yanjie Fu, Dongjie Wang, Wangyang Ying +4
As data continues to grow in scale and complexity, preparing, transforming, and analyzing it remains labor-intensive, repetitive, and difficult to scale. Since data contains knowle…
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…
Towards Urban Planing AI Agent in the Age of Agentic AI
Rui Liu, Tao Zhe, Zhong-Ren Peng +4
Generative AI, large language models, and agentic AI have emerged separately of urban planning. However, the convergence between AI and urban planning presents an interesting oppor…
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…