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Researcher profile

Guodong Li

Guodong Li contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
astro-ph.GAMachine LearningMethodologyastro-ph.SRComputer Visionq-fin.STstat.OT

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Published work

8 published item(s)

preprint2026arXiv

ParaRNN: An Interpretable and Parallelizable Recurrent Neural Network for Time-Dependent Data

The proliferation of large-scale and structurally complex data has spurred the integration of machine learning methods into statistical modeling. Recurrent neural networks (RNNs), a foundational class of models for time-dependent data, can be viewed as nonlinear extensions of classical autoregressive moving average models. Despite their flexibility and empirical success in machine learning, RNNs often suffer from limited interpretability and slow training, which hinders their use in statistics. This paper proposes the Parallelized RNN (ParaRNN), a novel model composed of multiple small recurrent units. ParaRNN admits an additive representation that decouples recurrent dynamics into interpretable components, whose behavior can be characterized through recurrence features. This interpretability enables its applications in nonparametric regression for time-dependent data, while the design also allows efficient parallelization. The approximation capacity and non-asymptotic prediction error bounds in a nonparametric regression setting are established for ParaRNN. Empirical results on three sequential modeling tasks further demonstrate that ParaRNN achieves performance comparable to vanilla RNNs while offering improved interpretability and efficiency.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Reduced-Rank Autoregressive Model for High-Dimensional Multivariate Network Time Series

Multivariate network time series are ubiquitous in modern systems, yet existing network autoregressive models typically treat nodes as scalar processes, ignoring cross-variable spillovers. To capture these complex interactions without the curse of dimensionality, we propose the Reduced-Rank Network Autoregressive (RRNAR) model. Our framework introduces a separable bilinear transition structure that couples the known network topology with a learnable low-rank variable subspace. We estimate the model using a novel Scaled Gradient Descent (ScaledGD) algorithm, explicitly designed to bridge the gap between rigid network scalars and flexible factor components. Theoretically, we establish non-asymptotic error bounds under a novel distance metric. A key finding is a network-induced blessing of dimensionality: for sparse networks, the estimation accuracy for network parameters improves as the network size grows. Applications to traffic and server monitoring networks demonstrate that RRNAR significantly outperforms univariate and unstructured benchmarks by identifying latent cross-channel propagation mechanisms.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

A benchmark for extreme conditions of the multiphase interstellar medium in the most luminous hot dust-obscured galaxy at z = 4.6

WISE J224607.6-052634.9 (W2246-0526) is a hot dust-obscured galaxy at $z$ = 4.601, and the most luminous obscured quasar known to date. W2246-0526 harbors a heavily obscured supermassive black hole that is most likely accreting above the Eddington limit. We present observations with the Atacama Large Millimeter/submillimeter Array (ALMA) in seven bands, including band 10, of the brightest far-infrared (FIR) fine-structure emission lines of this galaxy: [OI]$_{63μm}$, [OIII]$_{88μm}$, [NII]$_{122μm}$, [OI]$_{145μm}$, [CII]$_{158μm}$, [NII]$_{205μm}$, [CI]$_{370μm}$, and [CI]$_{609μm}$. A comparison of the data to a large grid of Cloudy radiative transfer models reveals that a high hydrogen density ($n_{H}\sim3\times10^3$ cm$^{-3}$) and extinction ($A_{V}\sim300$ mag), together with extreme ionization ($log(U)=-0.5$) and a high X-ray to UV ratio ($α_{ox}\geq-0.8$) are required to reproduce the observed nuclear line ratios. The values of $α_{ox}$ and $U$ are among the largest found in the literature and imply the existence of an X-ray-dominated region (XDR). In fact, this component explains the a priori very surprising non-detection of the [OIII]$_{88μm}$ emission line, which is actually suppressed, instead of boosted, in XDR environments. Interestingly, the best-fitted model implies higher X-ray emission and lower CO content than what is detected observationally, suggesting the presence of a molecular gas component that should be further obscuring the X-ray emission over larger spatial scales than the central region that is being modeled. These results highlight the need for multiline infrared observations to characterize the multiphase gas in high redshift quasars and, in particular, W2246-0526 serves as an extreme benchmark for comparisons of interstellar medium conditions with other quasar populations at cosmic noon and beyond.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

PGformer: Proxy-Bridged Game Transformer for Multi-Person Highly Interactive Extreme Motion Prediction

Multi-person motion prediction is a challenging task, especially for real-world scenarios of highly interacted persons. Most previous works have been devoted to studying the case of weak interactions (e.g., walking together), in which typically forecasting each human pose in isolation can still achieve good performances. This paper focuses on collaborative motion prediction for multiple persons with extreme motions and attempts to explore the relationships between the highly interactive persons' pose trajectories. Specifically, a novel cross-query attention (XQA) module is proposed to bilaterally learn the cross-dependencies between the two pose sequences tailored for this situation. A proxy unit is additionally introduced to bridge the involved persons, which cooperates with our proposed XQA module and subtly controls the bidirectional spatial information flows. These designs are then integrated into a Transformer-based architecture and the resulting model is called Proxy-bridged Game Transformer (PGformer) for multi-person interactive motion prediction. Its effectiveness has been evaluated on the challenging ExPI dataset, which involves highly interactive actions. Our PGformer consistently outperforms the state-of-the-art methods in both short- and long-term predictions by a large margin. Besides, our approach can also be compatible with the weakly interacted CMU-Mocap and MuPoTS-3D datasets and extended to the case of more than 2 individuals with encouraging results.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

High-Frequency-Based Volatility Model with Network Structure

This paper introduces one new multivariate volatility model that can accommodate an appropriately defined network structure based on low-frequency and high-frequency data. The model reduces the number of unknown parameters and the computational complexity substantially. The model parameterization and iterative multistep-ahead forecasts are discussed and the targeting reparameterization is also presented. Quasi-likelihood functions for parameter estimation are proposed and their asymptotic properties are established. A series of simulation experiments are carried out to assess the performance of the estimation in finite samples. An empirical example is demonstrated that the proposed model outperforms the network GARCH model, with the gains being particularly significant at short forecast horizons.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Nonparametric Quantile Regression for Homogeneity Pursuit in Panel Data Models

Many panel data have the latent subgroup effect on individuals, and it is important to correctly identify these groups since the efficiency of resulting estimators can be improved significantly by pooling the information of individuals within each group. However, the currently assumed parametric and semiparametric relationship between the response and predictors may be misspecified, which leads to a wrong grouping result, and the nonparametric approach hence can be considered to avoid such mistakes. Moreover, the response may depend on predictors in different ways at various quantile levels, and the corresponding grouping structure may also vary. To tackle these problems, this article proposes a nonparametric quantile regression method for homogeneity pursuit in panel data models with individual effects, and a pairwise fused penalty is used to automatically select the number of groups. The asymptotic properties are established, and an ADMM algorithm is also developed. The finite sample performance is evaluated by simulation experiments, and the usefulness of the proposed methodology is further illustrated by an empirical example.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Studying Infall in Infrared Dark Clouds with Multiple HCO+ Transitions

We investigate the infall properties in a sample of 11 infrared dark clouds (IRDCs) showing blue-asymmetry signatures in HCO$^{+}$ J=1--0 line profiles. We used JCMT to conduct mapping observations in HCO$^{+}$ J=4--3 as well as single-pointing observations in HCO$^{+}$ J =3--2, towards 23 clumps in these IRDCs. We applied the HILL model to fit these observations and derived infall velocities in the range of 0.5-2.7 km s$^{-1}$, with a median value of 1.0 km s$^{-1}$, and obtained mass accretion rates of 0.5-14$\times$10$^{-3}$ Msun yr$^{-1}$. These values are comparable to those found in massive star forming clumps in later evolutionary stages. These IRDC clumps are more likely to form star clusters. HCO$^{+}$ J =3--2 and HCO$^{+}$ J =1--0 were shown to trace infall signatures well in these IRDCs with comparable inferred properties. HCO$^{+}$ J=4--3, on the other hand, exhibits infall signatures only in a few very massive clumps, due to smaller opacties. No obvious correlation for these clumps was found between infall velocity and the NH3/CCS ratio.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Do RNN and LSTM have Long Memory?

The LSTM network was proposed to overcome the difficulty in learning long-term dependence, and has made significant advancements in applications. With its success and drawbacks in mind, this paper raises the question - do RNN and LSTM have long memory? We answer it partially by proving that RNN and LSTM do not have long memory from a statistical perspective. A new definition for long memory networks is further introduced, and it requires the model weights to decay at a polynomial rate. To verify our theory, we convert RNN and LSTM into long memory networks by making a minimal modification, and their superiority is illustrated in modeling long-term dependence of various datasets.

0 citations0 reviewsNo code linkedOpen access