Results 61 to 70 of about 9,337,952 (196)
Control variates for stochastic gradient MCMC [PDF]
, 2019 It is well known that Markov chain Monte Carlo (MCMC) methods scale poorly with dataset size. A popular class of methods for solving this issue is stochastic gradient MCMC (SGMCMC). These methods use a noisy estimate of the gradient of the log-posterior,
Baker, Jack +3 more
core +4 more sources
Variational Intrinsic Control Revisited
CoRR, 2020 In this paper, we revisit variational intrinsic control (VIC), an unsupervised reinforcement learning method for finding the largest set of intrinsic options available to an agent. In the original work by Gregor et al. (2016), two VIC algorithms were proposed: one that represents the options explicitly, and the other that does it implicitly.
openaire +4 more sources
Minimizing control variation in nonlinear optimal control [PDF]
Automatica, 2013 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ryan C. Loxton +2 more
openaire +4 more sources
Control variates for stochastic gradient MCMC [PDF]
Statistics and Computing, 2018 It is well known that Markov chain Monte Carlo (MCMC) methods scale poorly with dataset size. A popular class of methods for solving this issue is stochastic gradient MCMC. These methods use a noisy estimate of the gradient of the log posterior, which reduces the per iteration computational cost of the algorithm.
Jack Baker +3 more
openaire +6 more sources
Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization [PDF]
, 2020 Control variates are a well-established tool to reduce the variance of Monte Carlo estimators. However, for large-scale problems including high-dimensional and large-sample settings, their advantages can be outweighed by a substantial computational cost.
Carin, L +4 more
core +1 more source
Scientific Reports, 2023 To compare spectacle lenses with highly aspherical lenslets (HAL) versus defocus incorporated multiple segments (DIMS) on myopia progression control in 1 year.
Hui Guo +4 more
doaj +1 more source
Genetic Variation and the Control of Transcription
Cold Spring Harbor Symposia on Quantitative Biology, 2003 Identifying and understanding genetic variation is akey driver of agricultural, biotechnological, and biomedical research and commercialization; the major focus ofgenetic variation research has until now been on changesto protein-coding sequences because these are computationally and experimentally accessible.
Cotsapas, Chris +4 more
openaire +2 more sources
Journal of Intelligent Systems and Applied Data ScienceShapley values for feature attribution suffer from high variance requiring thousands of model evaluations. We introduce Orthogonal Permutation Sampling (OPS), achieving provable variance reduction through: (i) exact position stratification, (ii ...
YASH VARSHNEY, RANAV TYAGI, ANURAG SINHA
doaj +1 more source
Genetics and Molecular Biology, 2008 Populations of Ocyurus chrysurus were compared genetically and morphometrically along the West Atlantic coast to test the null hypothesis of population homogeneity in the area.
Anderson V. Vasconcellos +4 more
doaj +1 more source
Prediction of regionalized insurance risks based on control variates
, 2014 We show how regional prediction of ca rinsurance risks can be improved for finer subregions by combining explanatory modeling with phenomenologicalmodels from industrial practice. Motivated by the control-variates technique, wepropose a suitable combined
Christiansen, Marcus C. +3 more
core +1 more source