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Found 47 papers, 10 papers with code
Generative AI in Transportation Planning: A Survey
From the transportation planning perspective, we examine the role of GenAI in automating descriptive, predictive, generative, simulation, and explainable tasks to enhance mobility systems.
A Survey on Data-Centric AI: Tabular Learning from Reinforcement Learning and Generative AI Perspective
This survey focuses on data-driven tabular data optimization, specifically exploring reinforcement learning (RL) and generative approaches for feature selection and feature generation as fundamental techniques for refining data spaces.
LEKA:LLM-Enhanced Knowledge Augmentation
Therefore, we design a knowledge augmentation method LEKA for knowledge transfer that actively searches for suitable knowledge sources that can enrich the target domain's knowledge.
Iterative Feature Space Optimization through Incremental Adaptive Evaluation
We propose a weighted-sharing multi-head attention mechanism to encode key characteristics of the feature space into an embedding vector for evaluation.
Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation
This survey examines the key aspects of tabular data-centric AI, emphasizing feature selection and feature generation as essential techniques for data space refinement.
CONDEN-FI: Consistency and Diversity Learning-based Multi-View Unsupervised Feature and In-stance Co-Selection
The objective of multi-view unsupervised feature and instance co-selection is to simultaneously iden-tify the most representative features and samples from multi-view unlabeled data, which aids in mit-igating the curse of dimensionality and reducing instance size to improve the performance of down-stream tasks.
Causally-Aware Unsupervised Feature Selection Learning
Additionally, previous graph-based methods fail to account for the differing impacts of non-causal and causal features in constructing the similarity graph, which leads to false links in the generated graph.
Reinforcement Feature Transformation for Polymer Property Performance Prediction
This study focuses on improving polymer property performance prediction tasks by reconstructing an optimal and explainable descriptor representation space.
Revolutionizing Biomarker Discovery: Leveraging Generative AI for Bio-Knowledge-Embedded Continuous Space Exploration
Inspired by the success of generative AI, we think that the intricate knowledge of biomarker identification can be compressed into a continuous embedding space, thus enhancing the search for better biomarkers.
GUME: Graphs and User Modalities Enhancement for Long-Tail Multimodal Recommendation
By using the user modality enhancement strategy to maximize mutual information between these two features, we improve the generalization ability of user modality representations.
Adaptive Collaborative Correlation Learning-based Semi-Supervised Multi-Label Feature Selection
Then, the instance correlation and label correlation are integrated into the proposed regression model to adaptively learn both the sample similarity graph and the label similarity graph, which mutually enhance feature selection performance.
Enhanced Gene Selection in Single-Cell Genomics: Pre-Filtering Synergy and Reinforced Optimization
In this study, we introduce an iterative gene panel selection strategy that is applicable to clustering tasks in single-cell genomics.
Enhancing Tabular Data Optimization with a Flexible Graph-based Reinforced Exploration Strategy
Tabular data optimization methods aim to automatically find an optimal feature transformation process that generates high-value features and improves the performance of downstream machine learning tasks.
LEMMA-RCA: A Large Multi-modal Multi-domain Dataset for Root Cause Analysis
Root cause analysis (RCA) is crucial for enhancing the reliability and performance of complex systems.
RATT: A Thought Structure for Coherent and Correct LLM Reasoning
To address these limitations, we introduce the Retrieval Augmented Thought Tree (RATT), a novel thought structure that considers both overall logical soundness and factual correctness at each step of the thinking process.
Unsupervised Generative Feature Transformation via Graph Contrastive Pre-training and Multi-objective Fine-tuning
For unsupervised feature set representation pretraining, we regard a feature set as a feature-feature interaction graph, and develop an unsupervised graph contrastive learning encoder to embed feature sets into vectors.
Towards Graph Contrastive Learning: A Survey and Beyond
In recent years, deep learning on graphs has achieved remarkable success in various domains.
Neuro-Symbolic Embedding for Short and Effective Feature Selection via Autoregressive Generation
Within the learned embedding space, we leverage a multi-gradient search algorithm to find more robust and generalized embeddings with the objective of improving model performance and reducing feature subset redundancy.
Knockoff-Guided Feature Selection via A Single Pre-trained Reinforced Agent
A deep Q-network, pre-trained with the original features and their corresponding pseudo labels, is employed to improve the efficacy of the exploration process in feature selection.
Feature Selection as Deep Sequential Generative Learning
(2) We leverage the trained feature subset utility evaluator as a gradient provider to guide the identification of the optimal feature subset embedding;(3) We decode the optimal feature subset embedding to autoregressively generate the best feature selection decision sequence with autostop.
Dual-stage Flows-based Generative Modeling for Traceable Urban Planning
We design several experiments to indicate that our framework can outperform compared to other generative models for the urban planning task.
Feature Interaction Aware Automated Data Representation Transformation
Creating an effective representation space is crucial for mitigating the curse of dimensionality, enhancing model generalization, addressing data sparsity, and leveraging classical models more effectively.
Self-optimizing Feature Generation via Categorical Hashing Representation and Hierarchical Reinforcement Crossing
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 a feature pair with inherent feature interaction.
Traceable Group-Wise Self-Optimizing Feature Transformation Learning: A Dual Optimization Perspective
Feature transformation aims to reconstruct an effective representation space by mathematically refining the existing features.
Disentangled Causal Graph Learning for Online Unsupervised Root Cause Analysis
In this paper, we propose CORAL, a novel online RCA framework that can automatically trigger the RCA process and incrementally update the RCA model.
Towards Automated Urban Planning: When Generative and ChatGPT-like AI Meets Urban Planning
The two fields of urban planning and artificial intelligence (AI) arose and developed separately.
Beyond Discrete Selection: Continuous Embedding Space Optimization for Generative Feature Selection
Furthermore, we reconstruct feature selection solutions using these embeddings and select the feature subset with the highest performance for downstream tasks as the optimal subset.
Deep Graph Stream SVDD: Anomaly Detection in Cyber-Physical Systems
The temporal embeddings are mapped to the new graph as node attributes to form weighted attributed graph.
Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting
The distribution shift in Time Series Forecasting (TSF), indicating series distribution changes over time, largely hinders the performance of TSF models.
Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localization
REASON consists of Topological Causal Discovery and Individual Causal Discovery.
Traceable Automatic Feature Transformation via Cascading Actor-Critic Agents
Feature transformation for AI is an essential task to boost the effectiveness and interpretability of machine learning (ML).
Boosting Urban Traffic Speed Prediction via Integrating Implicit Spatial Correlations
To tackle the challenge, we propose a generic model for enabling the current traffic speed prediction methods to preserve implicit spatial correlations.
Human-instructed Deep Hierarchical Generative Learning for Automated Urban Planning
The third stage is to leverage multi-attentions to model the zone-zone peer dependencies of the functionality projections to generate grid-level land-use configurations.
Automated Urban Planning aware Spatial Hierarchies and Human Instructions
While automated urban planners have been examined, they are constrained because of the following: 1) neglecting human requirements in urban planning; 2) omitting spatial hierarchies in urban planning, and 3) lacking numerous urban plan data samples.
Self-Optimizing Feature Transformation
Feature transformation aims to extract a good representation (feature) space by mathematically transforming existing features.
C$^{2}$IMUFS: Complementary and Consensus Learning-based Incomplete Multi-view Unsupervised Feature Selection
Besides, learning the complete similarity graph, as an important promising technology in existing MUFS methods, cannot achieve due to the missing views.
Group-wise Reinforcement Feature Generation for Optimal and Explainable Representation Space Reconstruction
We reformulate representation space reconstruction into an interactive process of nested feature generation and selection, where feature generation is to generate new meaningful and explicit features, and feature selection is to eliminate redundant features to control feature sizes.
Reinforced Imitative Graph Learning for Mobile User Profiling
The profiling framework is formulated into a reinforcement learning task, where an agent is a next-visit planner, an action is a POI that a user will visit next, and the state of the environment is a fused representation of a user and spatial entities.
Online POI Recommendation: Learning Dynamic Geo-Human Interactions in Streams
An event that a user visits a POI in stream updates the states of both users and geospatial contexts; the agent perceives the updated environment state to make online recommendations.
Automated Urban Planning for Reimagining City Configuration via Adversarial Learning: Quantification, Generation, and Evaluation
However, three major challenges arise: 1) how to define a quantitative land-use configuration?
Deep Human-guided Conditional Variational Generative Modeling for Automated Urban Planning
Existing studies usually ignore the need of personalized human guidance in planning, and spatial hierarchical structure in planning generation.
Efficient Reinforced Feature Selection via Early Stopping Traverse Strategy
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) strategy and reward-level interactive (RI) strategy.
Automated Feature-Topic Pairing: Aligning Semantic and Embedding Spaces in Spatial Representation Learning
Texts of spatial entities, on the other hand, provide semantic understanding of latent feature labels, but is insensible to deep SRL models.
Reinforced Imitative Graph Representation Learning for Mobile User Profiling: An Adversarial Training Perspective
To solve the problem, we propose an adversarial training strategy to guarantee the robustness of the representation module.
Defending Water Treatment Networks: Exploiting Spatio-temporal Effects for Cyber Attack Detection
To this end, we propose a structured anomaly detection framework to defend WTNs by modeling the spatio-temporal characteristics of cyber attacks in WTNs.
DeepSTCL: A Deep Spatio-temporal ConvLSTM for Travel Demand Prediction
Urban resource scheduling is an important part of the development of a smart city, and transportation resources are the main components of urban resources.
Reimagining City Configuration: Automated Urban Planning via Adversarial Learning
The objective is then to propose an adversarial learning framework that can automatically generate such tensor for an unplanned area.
