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Found 11 papers, 2 papers with code
Decision Theory-Guided Deep Reinforcement Learning for Fast Learning
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.
Restricted Tweedie Stochastic Block Models
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.
Dependence model assessment and selection with DecoupleNets
This map, termed DecoupleNet, is used for dependence model assessment and selection.
RafterNet: Probabilistic predictions in multi-response regression
A fully nonparametric approach for making probabilistic predictions in multi-response regression problems is introduced.
Game-Theoretic and Machine Learning-based Approaches for Defensive Deception: A Survey
Defensive deception is a promising approach for cyber defense.
Applications of multivariate quasi-random sampling with neural networks
Generative moment matching networks (GMMNs) are suggested for modeling the cross-sectional dependence between stochastic processes.
Multivariate time-series modeling with generative neural networks
Generative moment matching networks (GMMNs) are introduced as dependence models for the joint innovation distribution of multivariate time series (MTS).
Quasi-random sampling for multivariate distributions via generative neural networks
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.
Pruning variable selection ensembles
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.
Expanded Alternating Optimization of Nonconvex Functions with Applications to Matrix Factorization and Penalized Regression
We propose a general technique for improving alternating optimization (AO) of nonconvex functions.
When is the majority-vote classifier beneficial?
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.
