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Found 8 papers, 1 papers with code
Backward error analysis and the qualitative behaviour of stochastic optimization algorithms: Application to stochastic coordinate descent
Stochastic optimization methods have been hugely successful in making large-scale optimization problems feasible when computing the full gradient is computationally prohibitive.
On the connections between optimization algorithms, Lyapunov functions, and differential equations: theory and insights
As a result we are able to prove for Polyak's ordinary differential equations and for a two-parameter family of Nesterov algorithms rates of convergence that improve on those available in the literature.
Introduction To Gaussian Process Regression In Bayesian Inverse Problems, With New ResultsOn Experimental Design For Weighted Error Measures
Bayesian posterior distributions arising in modern applications, including inverse problems in partial differential equation models in tomography and subsurface flow, are often computationally intractable due to the large computational cost of evaluating the data likelihood.
Batch Bayesian Optimization via Particle Gradient Flows
In this work we reformulate batch BO as an optimisation problem over the space of probability measures.
Limitations of Implicit Bias in Matrix Sensing: Initialization Rank Matters
In matrix sensing, we first numerically identify the sensitivity to the initialization rank as a new limitation of the implicit bias of gradient flow.
Information Theory Information Theory Optimization and Control
Probabilistic Linear Multistep Methods
We present a derivation and theoretical investigation of the Adams-Bashforth and Adams-Moulton family of linear multistep methods for solving ordinary differential equations, starting from a Gaussian process (GP) framework.
Multilevel Monte Carlo for Scalable Bayesian Computations
In contrast to MCMC methods, Stochastic Gradient MCMC (SGMCMC) algorithms such as the Stochastic Gradient Langevin Dynamics (SGLD) only require access to a batch of the data set at every step.
Multilevel Monte Carlo methods for the approximation of invariant measures of stochastic differential equations
We show that this is the first stochastic gradient MCMC method with complexity $\mathcal{O}(\varepsilon^{-2}|\log {\varepsilon}|^{3})$, in contrast to the complexity $\mathcal{O}(\varepsilon^{-3})$ of currently available methods.
