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Found 37 papers, 9 papers with code
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.
Scoring with Large Language Models: A Study on Measuring Empathy of Responses in Dialogues
We develop a novel and comprehensive framework for investigating how effective LLMs are at measuring and scoring empathy of responses in dialogues, and what methods can be employed to deepen our understanding of LLM scoring.
Topology-aware Reinforcement Feature Space Reconstruction for Graph Data
Feature space is an environment where data points are vectorized to represent the original dataset.
Enhancing Risk Assessment in Transformers with Loss-at-Risk Functions
In the financial field, precise risk assessment tools are essential for decision-making.
Dynamic Weight Adjusting Deep Q-Networks for Real-Time Environmental Adaptation
Deep Reinforcement Learning has shown excellent performance in generating efficient solutions for complex tasks.
Thought Space Explorer: Navigating and Expanding Thought Space for Large Language Model Reasoning
Recent advances in large language models (LLMs) have demonstrated their potential in handling complex reasoning tasks, which are usually achieved by constructing a thought chain to guide the model to solve the problem with multi-step thinking.
Retrieval-Augmented Feature Generation for Domain-Specific Classification
Feature generation can significantly enhance learning outcomes, particularly for tasks with limited data.
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.
Prototypical Reward Network for Data-Efficient RLHF
The reward model for Reinforcement Learning from Human Feedback (RLHF) has proven effective in fine-tuning Large Language Models (LLMs).
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.
Dynamic and Adaptive Feature Generation with LLM
The representation of feature space is a crucial environment where data points get vectorized and embedded for upcoming modeling.
A Comprehensive Survey on Data Augmentation
Existing literature surveys only focus on a certain type of specific modality data, and categorize these methods from modality-specific and operation-centric perspectives, which lacks a consistent summary of data augmentation methods across multiple modalities and limits the comprehension of how existing data samples serve the data augmentation process.
TFWT: Tabular Feature Weighting with Transformer
In this paper, we propose a novel feature weighting method to address the limitation of existing feature processing methods for tabular data.
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.
MEGA-DAgger: Imitation Learning with Multiple Imperfect Experts
In this paper, we propose MEGA-DAgger, a new DAgger variant that is suitable for interactive learning with multiple imperfect experts.
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.
