arXiv subjects

Zimu Chen

Publications and source records attributed to Zimu Chen.

6 recordsLinked to original sources

arXiv2026

DGA$_2$D: Directed Graph-Guided Automated Algorithm Design with Large Language Models

The rapid development of Large Language Models (LLMs) has opened new avenues for Automated Heuristic Design (AHD) for solving NP-hard combinatorial optimization problems (COPs). However, existing LLM-driven AHD methods are largely confined to rigid solver templates, relegating the search process to isolated module tuning. Transitioning to fully autonomous, system-level algorithm design is essential but fraught with low reliability of generated operators, extremely large search spaces, and ineffective credit assignment. To overcome these drawbacks, this paper proposes a Directed Graph-Guided Automated Algorithm Design framework, termed DGA$_2$D. It structures the open-ended program space as a directed graph, where each node represents a functional operator that can be instantiated using one of multiple candidate code implementations, while directed walks constitute complete algorithmic pipelines. A first-order path-dependent credit assignment mechanism is introduced to evaluate code variations strictly based on their topological context. Extensive experiments across 12 distinct COPs, ranging from complex scheduling to routing, demonstrate the consistent empirical advantages of DGA$_2$D. It reduces the average normalized gap by up to 10.96 percentage points compared to state-of-the-art LLM baselines.

cs.AI↗

arXiv2025

PathFuzzing: Worst Case Analysis by Fuzzing Symbolic-Execution Paths

Estimating worst-case resource consumption is a critical task in software development. The worst-case analysis (WCA) problem is an optimization-based abstraction of this task. Fuzzing and symbolic execution are widely used techniques for addressing the WCA problem. However, improving code coverage in fuzzing or managing path explosion in symbolic execution within the context of WCA poses significant challenges. In this paper, we propose PathFuzzing, aiming to combine the strengths of both techniques to design a WCA method. The key idea is to transform a program into a symbolic one that takes an execution path (encoded as a binary string) and interprets the bits as branch decisions. PathFuzzing then applies evolutionary fuzzing techniques to the transformed program to search for binary strings that represent satisfiable path conditions and lead to high resource consumption. We evaluate the performance of PathFuzzing experimentally on a benchmark suite that consists of prior work's benchmarks and some added by us. Results show that PathFuzzing generally outperforms a fuzzing and a symbolic-execution baseline.

cs.SE↗

arXiv2019

Sequential estimation for GEE with adaptive variables and subject selection

Modeling correlated or highly stratified multiple-response data becomes a common data analysis task due to modern data monitoring facilities and methods. Generalized estimating equations (GEE) is one of the popular statistical methods for analyzing this kind of data. In this paper, we present a sequential estimation procedure for obtaining GEE-based estimates. In addition to the conventional random sampling, the proposed method features adaptive subject recruiting and variable selection. Moreover, we equip our method with an adaptive shrinkage property so that it can decide the effective variables during the estimation procedure and build a confidence set with a pre-specified precision for the corresponding parameters. In addition to the statistical properties of the proposed procedure, we assess our method using both simulated data and real data sets.

stat.ME↗

arXiv2018

Robust functional ANOVA model with t-process

Robust estimation approaches are of fundamental importance for statistical modelling. To reduce susceptibility to outliers, we propose a robust estimation procedure with t-process under functional ANOVA model. Besides common mean structure of the studied subjects, their personal characters are also informative, especially for prediction. We develop a prediction method to predict the individual effect. Statistical properties, such as robustness and information consistency, are studied. Numerical studies including simulation and real data examples show that the proposed method performs well.

stat.ME↗

arXiv2017

H-relative error estimation approach for multiplicative regression model with random effect

Relative error approaches are more of concern compared to absolute error ones such as the least square and least absolute deviation, when it needs scale invariant of output variable, for example with analyzing stock and survival data. An h-relative error estimation method via the h-likelihood is developed to avoid heavy and intractable integration for a multiplicative regression model with random effect. Statistical properties of the parameters and random effect in the model are studied. To estimate the parameters, we propose an h-relative error computation procedure. Numerical studies including simulation and real examples show the proposed method performs well.

stat.ME↗

arXiv2016

A relative error estimation approach for single index model

A product relative error estimation method for single index regression model is proposed as an alternative to absolute error methods, such as the least square estimation and the least absolute deviation estimation. It is scale invariant for outcome and covariates in the model. Regression coefficients are estimated via a two-stage procedure and their statistical properties such as consistency and normality are studied. Numerical studies including simulation and a body fat example show that the proposed method performs well.

stat.ME↗