Pages that link to "Item:Q2029096"
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The following pages link to Convergence rates for optimised adaptive importance samplers (Q2029096):
Displaying 11 items.
- Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space (Q825596) (← links)
- Convergence of adaptive mixtures of importance sampling schemes (Q997389) (← links)
- Implicitly adaptive importance sampling (Q2058716) (← links)
- MCMC-driven importance samplers (Q2110113) (← links)
- Optimal convergence rates of high order Parzen windows with unbounded sampling (Q2251679) (← links)
- Daisee: Adaptive importance sampling by balancing exploration and exploitation (Q6049796) (← links)
- Gradient-based adaptive importance samplers (Q6078205) (← links)
- Efficient Bayes inference in neural networks through adaptive importance sampling (Q6082841) (← links)
- Context-Aware Surrogate Modeling for Balancing Approximation and Sampling Costs in Multifidelity Importance Sampling and Bayesian Inverse Problems (Q6109165) (← links)
- Accelerated gradient methods for sparse statistical learning with nonconvex penalties (Q6494394) (← links)
- On variational inference and maximum likelihood estimation with the \(\lambda\)-exponential family (Q6620121) (← links)
