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Bozhi Wu

3 accepted papers

2025

Logic-Q: Improving Deep Reinforcement Learning-based Quantitative Trading via Program Sketch-based Tuning

AAAI 2025technical

Deep reinforcement learning (DRL) has revolutionized quantitative trading (Q-trading) by achieving decent performance without significant human expert knowledge. Despite its achievements, we observe that the current state-of-the-art DRL models are still ineffective in identifying the market trends,…

Cited by 0SourcePDFScholar
2024

Improving Neural Logic Machines via Failure Reflection

ICML 2024poster

Reasoning is a fundamental ability towards artificial general intelligence (AGI). Fueled by the success of deep learning, the neural logic machines models (NLMs) have introduced novel neural-symbolic structures and demonstrate great performance and generalization on reasoning and decision-making tas…

Cited by 3SourcePDFScholar
2024

Unveiling Project-Specific Bias in Neural Code Models

COLING 2024main

Deep learning has introduced significant improvements in many software analysis tasks. Although the Large Language Models (LLMs) based neural code models demonstrate commendable performance when trained and tested within the intra-project independent and identically distributed (IID) setting, they o…