arXiv subjects

Zhedong Liu

Publications and source records attributed to Zhedong Liu.

4 recordsLinked to original sources

arXiv2025

Leave-group-out cross-validation for latent Gaussian models

Evaluating the predictive performance of a statistical model is commonly done using cross-validation. Among the various methods, leave-one-out cross-validation (LOOCV) is frequently used. Originally designed for exchangeable observations, LOOCV has since been extended to other cases such as hierarchical models. However, it focuses primarily on short-range prediction and may not fully capture long-range prediction scenarios. For structured hierarchical models, particularly those involving multiple random effects, the concepts of short- and long-range predictions become less clear, which can complicate the interpretation of LOOCV results. In this paper, we propose a complementary cross-validation framework specifcally tailored for longer-range prediction in latent Gaussian models, including those with structured random effects. Our approach differs from LOOCV by excluding a carefully constructed set from the training set, which better emulates longer-range prediction conditions. Furthermore, we achieve computational effciency by adjusting the full joint posterior for this modifed cross-validation, thus eliminating the need for model reftting. This method is implemented in the R-INLA package (www.r-inla.org) and can be adapted to a variety of inferential frameworks.

stat.CO↗

arXiv2025

Variational Learning Induces Adaptive Label Smoothing

We show that variational learning naturally induces an adaptive label smoothing where label noise is specialized for each example. Such label-smoothing is useful to handle examples with labeling errors and distribution shifts, but designing a good adaptivity strategy is not always easy. We propose to skip this step and simply use the natural adaptivity induced during the optimization of a variational objective. We show empirical results where a variational algorithm called IVON outperforms traditional label smoothing and yields adaptivity strategies similar to those of an existing approach. By connecting Bayesian methods to label smoothing, our work provides a new way to handle overconfident predictions.

cs.LG↗

arXiv2019

On Robust Spectrum Sensing Using M-estimators of Covariance Matrix

In this paper, we consider the spectrum sensing in cognitive radio networks when the impulsive noise appears. We propose a class of blind and robust detectors using M-estimators in eigenvalue based spectrum sensing method. The conventional eigenvalue based method uses statistics derived from the eigenvalues of sample covariance matrix(SCM) as testing statistics, which are inefficient and unstable in the impulsive noise environment. Instead of SCM, we can use M-estimators, which have good performance under both impulsive and non-impulsive noise. Among those M-estimators, We recommend the Tyler's M-estimator instead, which requires no knowledge of noise distribution and have the same probability of false alarm under different complex elliptically symmetric distributions. In addition, it performs better than the detector using sample covariance matrix when the noise is highly impulsive. It should be emphasized that this detector does not require knowledge of noise power which is required by the energy detection based methods. Simulations show that it performs better than conventional detector using sample covariance matrix in a highly impulsive noise environment.

eess.SP↗

arXiv2018

Different Power Adaption Methods on Fluctuating Two-Ray Fading Channels

In this letter, we consider a typical scenario where the transmitter employs different power adaption methods, including the optimal rate and power algorithm, optimal rate adaption, channel inversion and truncated channel inversion, to enhance the ergodic capacity (EC) with an average transmit power constraint over fluctuating two-way fading channels. In particular, we derive exact closed-form expressions for the EC under different power adaption methods, as well as corresponding asymptotic formulas for the EC valid in the high signal-to-noise ratio (SNR) region. Finally, we compare the performance of the EC under different power adaption methods, and this also validates the accuracy of our derived expressions for the exact and asymptotic EC.

eess.SP↗