1 code implementation • 8 Feb 2024 • Zelin Wan, Jin-Hee Cho, Mu Zhu, Ahmed H. Anwar, Charles Kamhoua, Munindar P. Singh
Experimental results demonstrate that the integration of decision theory not only facilitates effective initial guidance for DRL agents but also promotes a more structured and informed exploration strategy, particularly in environments characterized by large and intricate state spaces.
no code implementations • 17 Oct 2023 • Jie Jian, Mu Zhu, Peijun Sang
To model the international trading network, where edge weights represent trading values between countries, we propose an innovative SBM based on a restricted Tweedie distribution.
no code implementations • 7 Feb 2022 • Marius Hofert, Avinash Prasad, Mu Zhu
This map, termed DecoupleNet, is used for dependence model assessment and selection.
no code implementations • 2 Dec 2021 • Marius Hofert, Avinash Prasad, Mu Zhu
A fully nonparametric approach for making probabilistic predictions in multi-response regression problems is introduced.
no code implementations • 21 Jan 2021 • Mu Zhu, Ahmed H. Anwar, Zelin Wan, Jin-Hee Cho, Charles Kamhoua, Munindar P. Singh
Defensive deception is a promising approach for cyber defense.
no code implementations • 15 Dec 2020 • Marius Hofert, Avinash Prasad, Mu Zhu
Generative moment matching networks (GMMNs) are suggested for modeling the cross-sectional dependence between stochastic processes.
no code implementations • 25 Feb 2020 • Marius Hofert, Avinash Prasad, Mu Zhu
Generative moment matching networks (GMMNs) are introduced as dependence models for the joint innovation distribution of multivariate time series (MTS).
1 code implementation • 1 Nov 2018 • Marius Hofert, Avinash Prasad, Mu Zhu
Once trained on pseudo-random samples from a parametric model or on real data, these neural networks only require a multivariate standard uniform randomized QMC point set as input and are thus fast in estimating expectations of interest under dependence with variance reduction.
no code implementations • 26 Apr 2017 • Chunxia Zhang, Yilei Wu, Mu Zhu
In the context of variable selection, ensemble learning has gained increasing interest due to its great potential to improve selection accuracy and to reduce false discovery rate.
no code implementations • 12 Dec 2014 • W. James Murdoch, Mu Zhu
We propose a general technique for improving alternating optimization (AO) of nonconvex functions.
no code implementations • 24 Jul 2013 • Mu Zhu
In his seminal work, Schapire (1990) proved that weak classifiers could be improved to achieve arbitrarily high accuracy, but he never implied that a simple majority-vote mechanism could always do the trick.