Stochastic Gradient Langevin Dynamics
Consistency and fluctuations for stochastic gradient Langevin dynamics
... with stochastic gradient Langevin dynamics (SGLD), an alter- native approach proposed by Welling and Teh ...the Langevin diffusion (1) with a sufficiently small step-size δ 1, because ...
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Exploration of the (Non-)Asymptotic Bias and Variance of Stochastic Gradient Langevin Dynamics
... proposed stochastic gradient Langevin dynamics (SGLD) method circumvents this problem in three ways: it generates proposed moves using only a subset of the data, it skips the ...
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Nonasymptotic estimates for Stochastic Gradient Langevin Dynamics under local conditions in nonconvex optimization
... Stochastic Processes and their Applications, 129(10): 3638–3663, 2019 [4] N. H. Chau, Ch. Kumar, M. R´ asonyi and S. Sabanis. On fixed gain recursive estimators with discontinuity in the parameters. ESAIM ...
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On stochastic gradient Langevin dynamics with dependent data streams:the fully non-convex case
... May 31, 2019 Abstract We consider the problem of sampling from a target distribution which is not necessarily logconcave. Non- asymptotic analysis results are established in a suitable Wasserstein-type distance of the ...
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Consistency and Fluctuations For Stochastic Gradient Langevin Dynamics
... the stochastic estimates to the gradient above described, this leads to the stochastic gradient Hamiltonian Monte Carlo algorithm of (Chen et ...
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Hypocoercivity properties of adaptive Langevin dynamics
... its gradient are the consequence of substantial calculations and thus entail computational ...the gradient noise is the consequence of incomplete calculation of the log- likelihood function based on ...
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Stochastic Gradient Descent as Approximate Bayesian Inference
... Stochastic Gradient Descent with a constant learning rate (constant SGD) simulates a Markov chain with a stationary ...analyze stochastic-gradient MCMC algorithms. For ...
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Langevin Dynamics for Adaptive Inverse Reinforcement Learning of Stochastic Gradient Algorithms
... adaptive gradient algorithm, the IRL algorithm learns the utility ...a gradient algorithm operating in series with a Langevin dynam- ics ...cascaded Langevin dynamics and ...
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Taming neural networks with TUSLA: Non-convex learning via adaptive stochastic gradient Langevin algorithms
... the stochastic gradient is not globally Lipschitz continuous in θ, hence a new approach is required for learning schemes which rely on the analysis of Langevin dynamics with gradients ...
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Affine Invariant Interacting Langevin Dynamics for Bayesian Inference
... Stochastic differential equations (SDEs) in the N 2 scalar coefficients m ij t can easily be derived from (3.10) using the ansatz (3.11) . In other words, provided that the initial samples u (i) 0 are ...
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Stochastic gradient Markov chain Monte Carlo
... the Langevin dynamics (7) for 10,000 iterations, starting from θ = (0, 0) and with noisy gradients simulated as the true gradient plus noise, ν k ∼ N (0, ...the Langevin algorithm in terms of ...
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Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations
... using stochastic differential equations to study the precise dynamical properties of stochastic gradient descent are the independent works of Li et ...the stochastic algorithms as well as ...
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Stochastic Gradient Method: Applications
... 3 Optimal Control Under Probability Constraint Satellite Model and Optimization Problem Probability and Conditional Expectation Handling Stochastic Arrow-Hurwicz Algorithm.. Numerical Re[r] ...
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COARSE-GRADIENT LANGEVIN ALGORITHMS FOR DYNAMIC DATA INTEGRATION AND UNCERTAINTY QUANTIFICATION
... In Figure 4.2, we compare the acceptance rates of the Algorithms I, II and III with different coarse-scale precision σ c . The acceptance rate is defined as the ratio between the number of accepted permeability samples ...
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Coarse-gradient Langevin algorithms for dynamic data integration and uncertainty quantification
... the Langevin algorithms using the coarse-scale gradient of the target ...direct Langevin algorithm, the new method generates a modified Markov chain by incorporating the coarse-scale information of ...
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Stochastic gradient methods for stochastic model predictive control
... sampled gradient to some of the previ- ously computed ones, hence involving direction of descent of more component functions at once yet mantaining the cheap computational effort of SGD [13], [14], [9], [15], ...
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Overdamped langevin dynamics simulations of grain boundary motion
... overdamped stochastic evolution of particles interacting through inter-atomic ...molecular dynamics shows that our approach reproduces the complex atomic-scale dynamics of grain boundary migration ...
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Boltzmann-Langevin one-body dynamics for fermionic systems
... ing the beginning of the process of fragment formation, characterised by an initial increase of the number of collisions and a successive stabilisation and levelling off due to the separation of the fragments. This figure ...
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Affine invariant interacting Langevin dynamics for Bayesian inference
... Abstract We propose a computational method (with acronym ALDI) for sampling from a given target distribution based on first-order (overdamped) Langevin dynamics which satisfies the property of affine ...
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Stochastic gradient methods for machine learning
... for stochastic composite ...A stochastic gradient method with an exponential convergence rate for strongly-convex optimization with finite training ...
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