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Found 21 papers, 5 papers with code
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.
T-SaS: Toward Shift-aware Dynamic Adaptation for Streaming Data
Discovering the shifted behaviors and the evolving patterns underlying the streaming data are important to understand the dynamic system.
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.
A Counterfactual Collaborative Session-based Recommender System
Next, COCO-SBRS adopts counterfactual inference to recommend items based on the outputs of the pre-trained recommendation model considering the causalities to alleviate the data sparsity problem.
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).
Mitigating Popularity Bias in Recommendation with Unbalanced Interactions: A Gradient Perspective
We present a gradient perspective to understand two negative impacts of popularity bias in recommendation model optimization: (i) the gradient direction of popular item embeddings is closer to that of positive interactions, and (ii) the magnitude of positive gradient for popular items are much greater than that of unpopular items.
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.
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.
Feature and Instance Joint Selection: A Reinforcement Learning Perspective
Feature selection and instance selection are two important techniques of data processing.
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.
Interactive Reinforcement Learning for Feature Selection with Decision Tree in the Loop
In this journal version, we propose a novel interactive and closed-loop architecture to simultaneously model interactive reinforcement learning (IRL) and decision tree feedback (DTF).
Simplifying Reinforced Feature Selection via Restructured Choice Strategy of Single Agent
To address the question, we develop a single-agent reinforced feature selection approach integrated with restructured choice strategy.
AutoFS: Automated Feature Selection via Diversity-aware Interactive Reinforcement Learning
Motivated by such a computational dilemma, this study is to develop a novel feature space navigation method.
