arXiv subjects

Minghao Gu

Publications and source records attributed to Minghao Gu.

4 recordsLinked to original sources

arXiv2026

Predicting Covariate-Driven Spatial Deformation for Nonstationary Gaussian Processes

Nonstationary Gaussian processes (GPs) are essential for modeling complex, locally heterogeneous spatial data. A common modeling approach is the spatial deformation method that warps the domain to recover isotropy. However, this static method does not account for changes in spatial correlation induced by covariates, limiting its ability to predict nonstationary GPs under new covariate conditions. To enable predictive modeling of the deformation method, we propose to model the spatial deformation as a function of covariates. The spaces of diffeomorphic deformations and Euclidean covariate vectors are connected by characterizing deformations as generated by velocity fields living in a Lie algebra. To overcome the estimation instability caused by high-order interactions between multiple covariates in a general Lie algebra, we prove that those interactions can be truncated with a moderate physical assumption. Based on the theoretical results, a concise functional form of deformations driven by multiple covariates can be established, and an efficient estimation-inference algorithm is developed for out-of-sample nonstationary GP prediction with limited covariate-deformation sample pairs. The effectiveness and generalizability of the method are demonstrated on a simulation study and two case studies, in the fields of manufacturing and geostatistics, respectively.

cs.LG↗

arXiv2025

VeFA: Vector-Based Feature Space Adaptation for Robust Model Fine-Tuning

Catastrophic forgetting is a well-documented challenge in model fine-tuning, particularly when the downstream domain has limited labeled data or differs substantially from the pre-training distribution. Existing parameter-efficient fine-tuning methods largely operate in the weight space by modifying or augmenting the parameters of the pre-trained model, which can lead to models that are overly specialized to the observed downstream data. Recent studies suggest that one mechanism underlying such forgetting is the introduction of intruder dimensions into the representation space during fine-tuning. To mitigate the risk of overwriting pre-trained knowledge and to enhance robustness, we propose Vector-based Feature Adaptation (VeFA), a new fine-tuning method that operates directly in the feature space, which naturally avoids generating intruder dimensions. VeFA performs element-wise adaptation on individual features, thereby ensuring that the effective fine-tuned weights always remain within the column space of the pre-trained weight matrix. This feature-space adaptation perspective is inspired by the idea of effect equivalence modeling (EEM) of downstream lurking variables that induce distribution shifts, which posits that the influence of unobserved factors can be represented as an equivalent aggregate effect on observed features. By compensating for the effects of downstream lurking variables via a lightweight feature-level transformation, VeFA preserves the pre-trained representations and improves model generalization under distribution shift. We evaluate VeFA against LoRA on image classification, NLU, and NLG benchmarks, considering both standard fine-tuning performance and robustness; across these tasks, VeFA achieves comparable fine-tuning performance while consistently exhibiting stronger robustness.

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arXiv2024

Multi-task CNN Behavioral Embedding Model For Transaction Fraud Detection

The burgeoning e-Commerce sector requires advanced solutions for the detection of transaction fraud. With an increasing risk of financial information theft and account takeovers, deep learning methods have become integral to the embedding of behavior sequence data in fraud detection. However, these methods often struggle to balance modeling capabilities and efficiency and incorporate domain knowledge. To address these issues, we introduce the multitask CNN behavioral Embedding Model for Transaction Fraud Detection. Our contributions include 1) introducing a single-layer CNN design featuring multirange kernels which outperform LSTM and Transformer models in terms of scalability and domain-focused inductive bias, and 2) the integration of positional encoding with CNN to introduce sequence-order signals enhancing overall performance, and 3) implementing multitask learning with randomly assigned label weights, thus removing the need for manual tuning. Testing on real-world data reveals our model's enhanced performance of downstream transaction models and comparable competitiveness with the Transformer Time Series (TST) model.

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arXiv2019

Variational Langevin Hamiltonian Monte Carlo for Distant Multi-modal Sampling

The Hamiltonian Monte Carlo (HMC) sampling algorithm exploits Hamiltonian dynamics to construct efficient Markov Chain Monte Carlo (MCMC), which has become increasingly popular in machine learning and statistics. Since HMC uses the gradient information of the target distribution, it can explore the state space much more efficiently than the random-walk proposals. However, probabilistic inference involving multi-modal distributions is very difficult for standard HMC method, especially when the modes are far away from each other. Sampling algorithms are then often incapable of traveling across the places of low probability. In this paper, we propose a novel MCMC algorithm which aims to sample from multi-modal distributions effectively. The method improves Hamiltonian dynamics to reduce the autocorrelation of the samples and uses a variational distribution to explore the phase space and find new modes. A formal proof is provided which shows that the proposed method can converge to target distributions. Both synthetic and real datasets are used to evaluate its properties and performance. The experimental results verify the theory and show superior performance in multi-modal sampling.

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