35 citations · 58 across the 11 of their papers we have counts for
6 papers · 1 filter
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…
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…
Incorporating Prior Knowledge in Deep Learning Models via Pathway Activity Autoencoders
Pedro Henrique da Costa Avelar, Min Wu, Sophia Tsoka
Motivation: Despite advances in the computational analysis of high-throughput molecular profiling assays (e.g. transcriptomics), a dichotomy exists between methods that are simple…
Beyond Discrete Selection: Continuous Embedding Space Optimization for Generative Feature Selection
Meng Xiao, Dongjie Wang, Min Wu +3
The goal of Feature Selection - comprising filter, wrapper, and embedded approaches - is to find the optimal feature subset for designated downstream tasks. Nevertheless, current f…
Traceable Automatic Feature Transformation via Cascading Actor-Critic Agents
Meng Xiao, Dongjie Wang, Min Wu +5
Feature transformation for AI is an essential task to boost the effectiveness and interpretability of machine learning (ML). Feature transformation aims to transform original data…
Self-Optimizing Feature Transformation
Meng Xiao, Dongjie Wang, Min Wu +4
Feature transformation aims to extract a good representation (feature) space by mathematically transforming existing features. It is crucial to address the curse of dimensionality,…