In this paper we propose a novel and practical variance reduction approach for additive functionals of dependent sequences. Our combines the use control variates with minimization o...

Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years. Most of the existing methods are based on semi-definite programming (SDP), which is generally time-consuming and degrades scalability, especially confronting large-scale data. To overcome this challenge, we propose a stochastic algorithm SVRG-SBB, has following f...

Asynchronous Q-learning aims to learn the optimal action-value function (or Q-function) of a Markov decision process (MDP), based on single trajectory Markovian samples induced by behavior policy. Focusing $\gamma $ -discounted MDP wi...

The feature hashing algorithm introduced by Weinberger et al. (2009) is a popular dimensionality reduction that compresses high dimensional data points into low closely approximate the pairwise inner product. This has been used in many fundamental machine learning applications such as model compression (Chen 2015), spam classification (Weinberger 2009), compressing text classifiers (Joulin 2016...

Nowadays, there is a problem of lack computer power to conduct high-precision reactor analysis. There are several factors that increase the excessive computational load and make it difficult calculate nuclear full-scale models using Monte Carlo method. Among them large neutron-flux attenuation, which observed in deep penetration problems. Various reduction techniques used efficiency calculation...