5 citations · 5 across the 5 of their papers we have counts for
6 papers
EVOM: Agentic Meta-Evolution of Actor-Critic Architectures for Reinforcement Learning
Boyun Zhang, Chao Wang, Kai Wu
In actor-critic reinforcement learning, network architectures are typically manually designed. Automating this design is challenging because each candidate must be trained before e…
Synthetic Series-Symbol Data Generation for Time Series Foundation Models
Wenxuan Wang, Kai Wu, Yujian Betterest Li +2
Foundation models for time series analysis (TSA) have attracted significant attention. However, challenges such as training data scarcity and imbalance continue to hinder their dev…
Mitigating Data Scarcity in Time Series Analysis: A Foundation Model with Series-Symbol Data Generation
Wenxuan Wang, Kai Wu, Yujian Betterest Li +3
Foundation models for time series analysis (TSA) have attracted significant attention. However, challenges such as data scarcity and data imbalance continue to hinder their develop…
Pretrained Optimization Model for Zero-Shot Black Box Optimization
Xiaobin Li, Kai Wu, Yujian Betterest Li +3
Zero-shot optimization involves optimizing a target task that was not seen during training, aiming to provide the optimal solution without or with minimal adjustments to the optimi…
EMOFM: Ensemble MLP mOdel with Feature-based Mixers for Click-Through Rate Prediction
Yujian Betterest Li, Kai Wu
Track one of CTI competition is on click-through rate (CTR) prediction. The dataset contains millions of records and each field-wise feature in a record consists of hashed integers…
SPELL: Semantic Prompt Evolution based on a LLM
Yujian Betterest Li, Kai Wu
Prompt engineering is a new paradigm for enhancing the performance of trained neural network models. For optimizing text-style prompts, existing methods usually individually operat…