1 citations · 2 across the 4 of their papers we have counts for
7 papers
From Score to Diagnosis: Search-Independent Post-Diagnosis for Symbolic Regression
Qian Li, Yuxiao Hu, Juncheng Liu +2
Symbolic regression (SR) solvers employ diverse search mechanisms, yet most rely on the same low-information scalar fitness. Such scores provide little guidance on why a skeleton f…
Learning to Design SR Solvers: Harness Optimization for Symbolic Regression
Qian Li, Yuxiao Hu, Qihao Duan +2
Symbolic regression (SR) seeks explicit equations that explain observed data, and recent methods increasingly employ large language models (LLMs) and agents for equation discovery.…
DASS: A Solver-Adaptable Dynamic Auxiliary Search Strategy for Symbolic Regression
Yuxiao Hu, Qian Li, Ding Wang +3
Symbolic regression (SR) aims to discover interpretable mathematical expressions from data and plays a key role in scientific discovery. However, existing methods face a common bot…
GENSR: Symbolic Regression Based in Equation Generative Space
Qian Li, Yuxiao Hu, Juncheng Liu +1
Symbolic Regression (SR) tries to reveal the hidden equations behind observed data. However, most methods search within a discrete equation space, where the structural modification…
Context-Alignment: Activating and Enhancing LLM Capabilities in Time Series
Yuxiao Hu, Qian Li, Dongxiao Zhang +2
Recently, leveraging pre-trained Large Language Models (LLMs) for time series (TS) tasks has gained increasing attention, which involves activating and enhancing LLMs' capabilities…
Multi-spatial Multi-temporal Air Quality Forecasting with Integrated Monitoring and Reanalysis Data
Yuxiao Hu, Qian Li, Xiaodan Shi +2
Accurate air quality forecasting is crucial for public health, environmental monitoring and protection, and urban planning. However, existing methods fail to effectively utilize mu…
Focus on Hiders: Exploring Hidden Threats for Enhancing Adversarial Training
Qian Li, Yuxiao Hu, Yinpeng Dong +2
Adversarial training is often formulated as a min-max problem, however, concentrating only on the worst adversarial examples causes alternating repetitive confusion of the model, i…