arXiv subjects

Niansheng Tang

Publications and source records attributed to Niansheng Tang.

12 recordsLinked to original sources

arXiv2026

Efficient Online LLM Watermark Detection via Rao-Blackwellized E-Processes

As large language models (LLMs) are increasingly deployed, reliable and efficient mechanisms for distinguishing AI-generated text from human-written content have become essential. Statistical watermarking has emerged as a promising solution, yet most existing methods are typically fixed-horizon procedures, precluding valid early stopping in streaming generation. In this paper, we develop an efficient online watermark detection framework with anytime-valid inference based on Rao-Blackwellized e-processes, enabling recursive token-level evidence updates without storing the full history. In particular, we instantiate the framework for the Gumbel-max watermark and reduce the original token-level dependence testing problem to a pivot-induced sequential testing problem with an explicit null distribution. Theoretically, we prove anytime-valid Type I error control under arbitrary optional stopping and establish positive asymptotic log-growth under watermarking, implying consistency of the proposed stopping rules. Simulations and experiments on real LLM-generated text demonstrate efficient online detection with rigorous anytime-valid guarantees.

stat.ML↗

arXiv2026

GAttNHP: Group Attention Neural Hawkes Process for Extrapolation Reasoning in Temporal Knowledge Graphs

Temporal Knowledge Graphs (TKGs) record how facts evolve over time, but forecasting future events on a TKG remains difficult for three reasons: (i) long-range temporal dependencies are hard to encode; (ii) events on different chains mutually excite or inhibit one another in ways that snapshot-level models cannot express; and (iii) inter-arrival times are heavy-tailed and statistically sparse, so deterministic time predictors are unreliable. We address these three issues with a single framework, the \textbf{Group Attention Neural Hawkes Process (GAttNHP)}, built around three matched components. First, a self-attention encoder casts each subject--relation chain as a continuous-time point process and captures the lingering excitation of distant history. Second, a semantic soft-grouping module turns globally learnable Hawkes priors into an analytical cross-attention mask, so chains share excitation patterns through their latent group memberships rather than through exhaustive pairwise computation. Third, a Non-Crossing Quantile (NCQ) regression head replaces mean-based time prediction, providing calibrated, monotonically ordered quantile estimates that remain stable under heavy-tailed inter-arrival distributions. On six benchmark TKG datasets, GAttNHP improves over state-of-the-art baselines on both entity prediction and time prediction, and ablations confirm that its largest gains arise on the long-tail event chains where existing models fail most severely.

cs.LG↗

arXiv2026

Robust Sequential Experimental Design for A/B Testing

Experimental design has emerged as a powerful approach for improving the sample efficiency of A/B testing, yet existing designs rely critically on correctly specified models. We study robust sequential experimental design under model misspecification and develop a unified framework that covers both contextual bandit and dynamic settings. Theoretically, we prove that our design bounds the worst-case mean squared error of the estimated treatment effect. Empirically, we demonstrate the effectiveness of the proposed approach using synthetic and real-world datasets from a leading technology company.

stat.ML↗

arXiv2026

Partially Functional Dynamic Backdoor Diffusion-based Causal Model

Causal inference in spatio-temporal settings is critically hindered by unmeasured confounders with complex spatio-temporal dynamics and the prevalence of multi-resolution data. While diffusion models present a promising avenue for estimating structural causal models, existing approaches are limited by assumptions of causal sufficiency or static confounding, failing to capture the region-specific, temporally dependent nature of real-world latent variables or to directly handle functional variables. We bridge this gap by introducing the Partially Functional Dynamic Backdoor Diffusion-based Causal Model (PFD-BDCM), a unified generative framework designed to simultaneously tackle causal inference with dynamic confounding and functional data. Our approach formalizes a novel structural causal model that captures spatio-temporal dependencies in latent confounders through conditional autoregressive processes, represents functional variables via basis expansion coefficients treated as standard graph nodes, and integrates valid backdoor adjustment into a diffusion-based generative process. We provide theoretical guarantees on the preservation of causal effects under basis expansion and derive error bounds for counterfactual estimates. Experiments on synthetic data and a real-world air pollution case study demonstrate that PFD-BDCM outperforms existing methods across observational, interventional, and counterfactual queries. This work provides a rigorous and practical tool for robust causal inference in complex spatio-temporal systems characterized by non-stationarity and multi-resolution data.

stat.ML↗

arXiv2026

Beyond Passive Aggregation: Active Auditing and Topology-Aware Defense in Decentralized Federated Learning

Decentralized Federated Learning (DFL) remains highly vulnerable to adaptive backdoor attacks designed to bypass traditional passive defense metrics. To address this limitation, we shift the defensive paradigm toward a novel active, interventional auditing framework. First, we establish a dynamical model to characterize the spatiotemporal diffusion of adversarial updates across complex graph topologies. Second, we introduce a suite of proactive auditing metrics, stochastic entropy anomaly, randomized smoothing Kullback-Leibler divergence, and activation kurtosis. These metrics utilize private probes to stress-test local models, effectively exposing latent backdoors that remain invisible to conventional static detection. Furthermore, we implement a topology-aware defense placement strategy to maximize global aggregation resilience. We provide theoretical property for the system's convergence under co-evolving attack and defense dynamics. Numeric empirical evaluations across diverse architectures demonstrate that our active framework is highly competitive with state-of-the-art defenses in mitigating stealthy, adaptive backdoors while preserving primary task utility.

cs.LG↗

arXiv2025

Patterns of X-ray and $γ$-ray Flares: from Blazar to Maganetar and Sun

Using Gaussian process methods, we analyzed the light curves of three extreme solar X-ray flares observed by the RHESSI satellite. Their variability characteristics were then compared with those of HXMT-HE X-ray burst (XRB; in SGR 1935+2154) associated with fast radio burst (FRB) 200428 and blazar $γ$-ray giant flares, to investigate the origins of these extreme flaring events. The variability patterns of the solar X-ray flares follow the stochastically driven damped simple harmonic oscillator (SHO) model. The derived timescales $t_{\rm B\underline{} steep}$ and $t_{\rm B\underline{~} flat}$ (corresponding to PSD breaks) are in the range of 4-7 s and 16-53 s, respectively. The FRB-associated HXMT-HE burst has a $Q$ value near 0.3, matching those of the solar flares occurred on 23 July 2002 (flare 1) and 3 November 2003 (flare 2). By contrast, blazar $γ$-ray giant flares show $Q >$ 0.3, similar to the solar flare occurred on 25 February 2014 (flare 3). We proposed that the critically damped state of the system may be the condition triggering the association between the XRB in SGR 1935+2154 and the FRB. In this scenario, the critical damping $Q$ value of the system is around 0.3, not the theoretical 0.5. The similarity in $Q$ values might imply that the FRB-associated HXMT-HE XRB and solar X-ray flares 1 $\&$ 2 share comparable dynamic behavior, while blazar $γ$-ray flares and solar X-ray flare 3 exhibit another distinct but similar dynamic behavior. Like solar X-ray flares, these extreme flares may all be related to the magnetic reconnection process.

astro-ph.HE↗

arXiv2025

Unraveling the Interplay between Carryover Effects and Reward Autocorrelations in Switchback Experiments

A/B testing has become the gold standard for policy evaluation in modern technological industries. Motivated by the widespread use of switchback experiments in A/B testing, this paper conducts a comprehensive comparative analysis of various switchback designs in Markovian environments. Unlike many existing works which derive the optimal design based on specific and relatively simple estimators, our analysis covers a range of state-of-the-art estimators developed in the reinforcement learning (RL) literature. It reveals that the effectiveness of different switchback designs depends crucially on (i) the size of the carryover effect and (ii) the auto-correlations among reward errors over time. Meanwhile, these findings are estimator-agnostic, i.e., they apply to most RL estimators. Based on these insights, we provide a workflow to offer guidelines for practitioners on designing switchback experiments in A/B testing.

stat.ML↗

arXiv2025

Gaussian Process Modeling Coronal X-ray Variability of Active Galactic Nuclei

The corona is an integral component of active galactic nuclei (AGNs) which can produce the X-ray emission. However, many of its physical properties and the mechanisms powering this emission remain a mystery. In this work, we study the coronal X-ray variabilities of 13 AGNs by Gaussian Process. 2-10 keV light curves of 13 AGNs can be successfully described by the damped-random walk (DRW) model. The extracted coronal X-ray timescales range from 3 to 50 days. In the plot of variability timescale versus black hole mass, the coronal X-ray timescales of four sources occupy almost the same region as the optical timescales of the accretion disk, with the latter matching the predicted thermal instability timescale of the disk. In contrast, the X-ray timescales of the remaining sources exhibit a systematic offset toward lower values. We propose that the coronal X-ray variability may be driven by internal processes within the corona itself (such as thermal conduction). On the other hand, it may also be triggered by local thermal instabilities occurring in different regions (close to the central black hole) of the accretion disk, which propagate to the corona via disk-corona coupling.

astro-ph.HE↗

arXiv2025

Pattern and Origin for the Extreme $γ$-ray Flares of 3C 454.3 and 3C 279: An Astrophysical Critical Damper?

We apply a Gaussian process method to the extreme $γ$-ray flares of 3C 454.3 and 3C 279 to discover the variable patterns and then to investigate the physical origins of the giant flares. The kernels of stochastically driven damped simple harmonic oscillator (SHO), the damped random-walk (DRW), and Mat$\acute{\rm e}$rn-3/2 are respectively used to describe the adaptive-binning $γ$-ray light curves of the two flares. Our findings show that both the extreme $γ$-ray flares of 3C 454.3 and 3C 279 clearly prefer the SHO kernel in the over-damped mode and the Mat$\acute{\rm e}$rn-3/2 kernel over the DRW kernel. The resulted SHO and Mat$\acute{\rm e}$rn-3/2 power spectral densities (PSDs) are the same for each object, with the index changing from -4 at high frequencies to 0 at low frequencies. The patterns of the two flares are both approaching the critical damping mode with the quality factor Q $\approx$ 0.4 (i.e., the damping ratio $η\approx$ 1.25), but with slightly different damping timescales. The characteristic timescale (corresponding to the broken frequency in the PSD) for 3C 454.3 is 2-3 days and 3-5 days for 3C 279. The variable patterns found here suggest that once the system responds to the energy injection disturbance, the release of the energy in the system is finished abruptly. The obtained timescale provides a constraint on the size of energy dissipation region for each source.

astro-ph.HE↗

arXiv2025

Variational Bayesian Logistic Tensor Regression with Application to Image Recognition

In recent years, image recognition method has been a research hotspot in various fields such as video surveillance, biometric identification, unmanned vehicles, human-computer interaction, and medical image recognition. Existing recognition methods often ignore structural information of image data or depend heavily on the sample size of image data. To address this issue, we develop a novel variational Bayesian method for image classification in a logistic tensor regression model with image tensor predictors by utilizing tensor decomposition to approximate tensor regression. To handle the sparsity of tensor coefficients, we introduce the multiway shrinkage priors for marginal factor vectors of tensor coefficients. In particular, we obtain a closed-form approximation to the variational posteriors for classification prediction based on the matricization of tensor decomposition. Simulation studies are conducted to investigate the performance of the proposed methodologies in terms of accuracy, precision and F1 score. Flower image data and chest X-ray image data are illustrated by the proposed methodologies.

stat.ME↗

arXiv2025

Evidence for Magneto-gravitational Processes in Supermassive Black Hole Binary PG 1553+113

PG1553+113 has drawn significant attention for its quasi-periodic oscillation (QPO) in gamma-ray variability, though the origin of its variability remains uncertain. In this study, we propose a physical mechanism to explain the observed gamma-ray variability within the framework of a supermassive black hole binary (SMBHB) system, supported by a newly identified component hidden in the light curve. A detailed analysis for its about 16-year light curve obtained from Fermi-LAT observations is performed by Gaussian process (GP). As anticipated, the QPO of 2.1 years is effectively captured by the stochastically-driven damped simple harmonic oscillator (SHO) kernel within the under-damped regime, and the overall stochastic nature of the variability is described by the damped random walk (DRW) kernel albeit with an unconstrained damping timescale. Additionally, our results reveal a previously unrecognized component in active galactic nuclei variability, characterized by the Matérn-3/2 kernel, which is typically associated with systems undergoing abrupt energy release. These findings can be consistently interpreted within the SMBHB framework. The QPO of about 2.1 years is likely attributed to the orbital motion in a SMBHB system. The Matérn-3/2 component is interpreted as resulting from magnetic reconnection events triggered by gravitational perturbations of the magnetic field within the jet, occurring as one black hole approaches the other. Meanwhile, in this case, the damping timescale of the common DRW kernel remains unconstrained due to the influence of new perturbations within the system.

astro-ph.HE↗

arXiv2020

Dynamic Tensor Recommender Systems

Recommender systems have been extensively used by the entertainment industry, business marketing and the biomedical industry. In addition to its capacity of providing preference-based recommendations as an unsupervised learning methodology, it has been also proven useful in sales forecasting, product introduction and other production related businesses. Since some consumers and companies need a recommendation or prediction for future budget, labor and supply chain coordination, dynamic recommender systems for precise forecasting have become extremely necessary. In this article, we propose a new recommendation method, namely the dynamic tensor recommender system (DTRS), which aims particularly at forecasting future recommendation. The proposed method utilizes a tensor-valued function of time to integrate time and contextual information, and creates a time-varying coefficient model for temporal tensor factorization through a polynomial spline approximation. Major advantages of the proposed method include competitive future recommendation predictions and effective prediction interval estimations. In theory, we establish the convergence rate of the proposed tensor factorization and asymptotic normality of the spline coefficient estimator. The proposed method is applied to simulations and IRI marketing data. Numerical studies demonstrate that the proposed method outperforms existing methods in terms of future time forecasting.

stat.ME↗