arXiv subjects

Yonghui Huang

Publications and source records attributed to Yonghui Huang.

4 recordsLinked to original sources

arXiv2026

Characterizing and Predicting the Longest CI Builds in Machine Learning Projects

Continuous integration (CI) builds in machine learning (ML) projects tend to run longer than in other projects, yet little is known about the longest builds, which consume most CI time. This paper characterizes the longest builds in ML projects, analyzes how CI workflow properties are associated with them, and evaluates whether they can be predicted before execution. We study 269,930 GitHub Actions builds from 107 open-source ML projects. The top 18% longest builds account for 80.30% of total build time and fail in 39.09% of cases. Build duration differs significantly across ML project types. The framework and infrastructure ML projects have the longest upper tail and produce 98% of the longest builds, whereas projects that use existing ML libraries have the lowest median build duration. The longest builds are most strongly associated with the recent history of the same workflow, as the duration of its last build and the median duration of the project's last 20 builds show large effect sizes. The longest builds are more consistently associated with CI workflow structure than with configuration. The number of jobs, control flow complexity, coupling complexity, and maximum steps per job show medium effect sizes, whereas caching, matrix settings, and Docker builds show small or negligible effects. Framework-specific workflows have the largest percentage of builds among long builds. Platform-specific workflows produce the largest number of the longest builds. Finally, 47 features describing build history, workflow structure, and the ML project outperform a traditional baseline with all four classifiers. In a chronological evaluation, the best model (XGBoost) reaches an F1-score of 0.775, compared with 0.682 for the baseline, and an AUC-ROC of 0.955. These results show that build history and CI workflow structure help identify the longest builds in ML projects before they run.

cs.SE↗

arXiv2026

PEAR: Progressive Evidence-Based AutoResearch for Industrial Search Systems

AutoResearch improves systems through iterative experimentation: agents propose candidate modifications, evaluate them, and use the results to guide subsequent exploration. Applying this paradigm to industrial search presents two challenges. (1) Common AutoResearch approaches follow a keep-if-better rule, retaining the highest-scoring candidate for subsequent experiments. Under non-stationary traffic, transient gains may be mistaken for persistent improvements, impairing reliable accumulation of search knowledge. (2) Candidate modifications can be evaluated at multiple fidelity levels, from low-cost proxies to online validation, differing in cost, objective alignment, and statistical reliability. Existing methods rely on individual signals or task-specific procedures, lacking a unified basis for using evidence across levels to guide search. We introduce Progressive Evidence-Based AutoResearch (PEAR) with two complementary components. Evidence-driven AutoResearch maintains an independent, hypothesis-guided research state for each strategy task within a predefined objective and intervention scope. Each state evolves through a Plan-Execute-Evaluate-Update transition that links experimentation to context-aware evidence interpretation and hypothesis revision. Confidence-Gated Verifier Ladder organizes evaluation into four levels of increasing fidelity: Offline Replay, Shadow-Traffic Evaluation, Rapid Online Evaluation, and Decision-Grade Online Evaluation. A unified confidence-based gate promotes candidates only when evidence supports a statistically significant positive effect, enabling broad low-cost exploration while reserving costly online experiments for promoted candidates. In a real-world industrial search system, strategies optimized with PEAR significantly increased Main Order/DAU by 2.7336% and 3.2957% relative to their respective baselines in two A/B experiments.

cs.IR↗

arXiv2022

A high-order tensor completion algorithm based on Fully-Connected Tensor Network weighted optimization

Tensor completion aimes at recovering missing data, and it is one of the popular concerns in deep learning and signal processing. Among the higher-order tensor decomposition algorithms, the recently proposed fully-connected tensor network decomposition (FCTN) algorithm is the most advanced. In this paper, by leveraging the superior expression of the fully-connected tensor network (FCTN) decomposition, we propose a new tensor completion method named the fully connected tensor network weighted optization(FCTN-WOPT). The algorithm performs a composition of the completed tensor by initialising the factors from the FCTN decomposition. We build a loss function with the weight tensor, the completed tensor and the incomplete tensor together, and then update the completed tensor using the lbfgs gradient descent algorithm to reduce the spatial memory occupation and speed up iterations. Finally we test the completion with synthetic data and real data (both image data and video data) and the results show the advanced performance of our FCTN-WOPT when it is applied to higher-order tensor completion.

cs.LG↗

arXiv2018

Monolingual sentence matching for text simplification

This work improves monolingual sentence alignment for text simplification, specifically for text in standard and simple Wikipedia. We introduce a convolutional neural network structure to model similarity between two sentences. Due to the limitation of available parallel corpora, the model is trained in a semi-supervised way, by using the output of a knowledge-based high performance aligning system. We apply the resulting similarity score to rescore the knowledge-based output, and adapt the model by a small hand-aligned dataset. Experiments show that both rescoring and adaptation improve the performance of knowledge-based method.

cs.CL↗