arXiv subjects

Foo Hui-Mean

Publications and source records attributed to Foo Hui-Mean.

5 recordsLinked to original sources

arXiv2026

Small-Area Precipitation Forecasting and Drought--Flood Early Warning with Reverse-Martingale Regularized Recurrent Networks

Small-area precipitation forecasts support real-time decisions for reservoir operation, irrigation planning, drought monitoring, and flash-flood response. Operational value depends not only on point accuracy, but also on calibrated exceedance probabilities and warning rules that remain stable when local weather regimes depart from the training climatology. We evaluate a reverse-martingale regularized recurrent neural network (\RMRNN) for probabilistic precipitation forecasting and sequential early warning. A backward-coherence penalty is added to the recurrent hidden state; the resulting residual process drives a Shiryaev--Roberts (SR) detector, so the same latent trajectory that produces the forecast also supplies a continuously updated drought or flood-regime indicator. The framework is tested on the Taiwan CWA dense rain-gauge network, CHIRPS v2 daily gridded precipitation over Taiwan and the Horn of Africa, and NOAA GHCN-Daily stations over the Texas Hill Country. Across 1{,}000 replications, \RMRNN{} matches or slightly improves the GRU baseline in RMSE, MAE, and CRPS at 1~h--72~h lead while substantially improving alarm characteristics. The SR detector reduces false-alarm ratios by a factor of three to five at matched detection power. In the 2020--2021 Taiwan drought, onset is flagged 8--12 days earlier than SPI-3 thresholding; in the 2023 Typhoon Haikui flood, flash-flood risk is signalled 4~h before the CWA operational alert.

stat.AP↗

arXiv2026

ALMAB-DC: Active Learning, Multi-Armed Bandits, and Distributed Computing for Sequential Experimental Design and Black-Box Optimization

Sequential experimental design under expensive, gradient-free objectives is a central challenge in computational statistics: evaluation budgets are tightly constrained and information must be extracted efficiently from each observation. We propose \textbf{ALMAB-DC}, a GP-based sequential design framework combining active learning, multi-armed bandits (MAB), and distributed asynchronous computing for expensive black-box experimentation. A Gaussian process surrogate with uncertainty-aware acquisition identifies informative query points; a UCB or Thompson-sampling bandit controller allocates evaluations across parallel workers; and an asynchronous scheduler handles heterogeneous runtimes. We present cumulative regret bounds for the bandit components and characterize parallel scalability via Amdahl's Law. We validate ALMAB-DC on five benchmarks. On the two statistical experimental-design tasks, ALMAB-DC achieves lower simple regret than Equal Spacing, Random, and D-optimal designs in dose--response optimization, and in adaptive spatial field estimation matches the Greedy Max-Variance benchmark while outperforming Latin Hypercube Sampling; at $K=4$ the distributed setting reaches target performance in one-quarter of sequential wall-clock rounds. On three ML/engineering tasks (CIFAR-10 HPO, CFD drag minimization, MuJoCo RL), ALMAB-DC achieves 93.4\% CIFAR-10 accuracy (outperforming BOHB by 1.7\,pp and Optuna by 1.1\,pp), reduces airfoil drag to $C_D = 0.059$ (36.9\% below Grid Search), and improves RL return by 50\% over Grid Search. All advantages over non-ALMAB baselines are statistically significant under Bonferroni-corrected Mann--Whitney $U$ tests. Distributed execution achieves $7.5\times$ speedup at $K = 16$ agents, consistent with Amdahl's Law.

cs.LG↗

arXiv2026

Efficient Data Reduction Via PCA-Guided Quantile Based Sampling

In large-scale statistical modeling, reducing data size through subsampling is essential for balancing computational efficiency and statistical accuracy. We propose a new method, Principal Component Analysis guided Quantile Sampling (PCA-QS), which projects data onto principal components and applies quantile-based sampling to retain representative and diverse subsets. Compared with uniform random sampling, leverage score sampling, and coreset methods, PCA-QS consistently achieves lower mean squared error and better preservation of key data characteristics, while also being computationally efficient. This approach is adaptable to a variety of data scenarios and shows strong potential for broad applications in statistical computing.

stat.CO↗

arXiv2026

Integrating Multi-Armed Bandit, Active Learning, and Distributed Computing for Scalable Optimization

Modern optimization problems in scientific and engineering domains often rely on expensive black-box evaluations, such as those arising in physical simulations or deep learning pipelines, where gradient information is unavailable or unreliable. In these settings, conventional optimization methods quickly become impractical due to prohibitive computational costs and poor scalability. We propose ALMAB-DC, a unified and modular framework for scalable black-box optimization that integrates active learning, multi-armed bandits, and distributed computing, with optional GPU acceleration. The framework leverages surrogate modeling and information-theoretic acquisition functions to guide informative sample selection, while bandit-based controllers dynamically allocate computational resources across candidate evaluations in a statistically principled manner. These decisions are executed asynchronously within a distributed multi-agent system, enabling high-throughput parallel evaluation. We establish theoretical regret bounds for both UCB-based and Thompson-sampling-based variants and develop a scalability analysis grounded in Amdahl's and Gustafson's laws. Empirical results across synthetic benchmarks, reinforcement learning tasks, and scientific simulation problems demonstrate that ALMAB-DC consistently outperforms state-of-the-art black-box optimizers. By design, ALMAB-DC is modular, uncertainty-aware, and extensible, making it particularly well suited for high-dimensional, resource-intensive optimization challenges.

stat.CO↗

arXiv2025

PCA-Guided Quantile Sampling: Preserving Data Structure in Large-Scale Subsampling

We introduce Principal Component Analysis guided Quantile Sampling (PCA QS), a novel sampling framework designed to preserve both the statistical and geometric structure of large scale datasets. Unlike conventional PCA, which reduces dimensionality at the cost of interpretability, PCA QS retains the original feature space while using leading principal components solely to guide a quantile based stratification scheme. This principled design ensures that sampling remains representative without distorting the underlying data semantics. We establish rigorous theoretical guarantees, deriving convergence rates for empirical quantiles, Kullback Leibler divergence, and Wasserstein distance, thus quantifying the distributional fidelity of PCA QS samples. Practical guidelines for selecting the number of principal components, quantile bins, and sampling rates are provided based on these results. Extensive empirical studies on both synthetic and real-world datasets show that PCA QS consistently outperforms simple random sampling, yielding better structure preservation and improved downstream model performance. Together, these contributions position PCA QS as a scalable, interpretable, and theoretically grounded solution for efficient data summarization in modern machine learning workflows.

stat.ME↗