Wavelet DPP kernels deliver improved continuous variance reduction and a discretization procedure that preserves decay rates for discrete ML subsampling tasks including rough objectives.
Importance Sampling: Intrinsic Dimension and Computational Cost
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
abstract
The basic idea of importance sampling is to use independent samples from a proposal measure in order to approximate expectations with respect to a target measure. It is key to understand how many samples are required in order to guarantee accurate approximations. Intuitively, some notion of distance between the target and the proposal should determine the computational cost of the method. A major challenge is to quantify this distance in terms of parameters or statistics that are pertinent for the practitioner. The subject has attracted substantial interest from within a variety of communities. The objective of this paper is to overview and unify the resulting literature by creating an overarching framework. A general theory is presented, with a focus on the use of importance sampling in Bayesian inverse problems and filtering.
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
Recasts sampling-based nonconvex optimization as smoothed gradient descent to obtain non-asymptotic convergence guarantees and introduces the DIDA annealed algorithm that converges to the global optimum.
citing papers explorer
-
State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives
Wavelet DPP kernels deliver improved continuous variance reduction and a discretization procedure that preserves decay rates for discrete ML subsampling tasks including rough objectives.
-
Global Convergence of Sampling-Based Nonconvex Optimization through Diffusion-Style Smoothing
Recasts sampling-based nonconvex optimization as smoothed gradient descent to obtain non-asymptotic convergence guarantees and introduces the DIDA annealed algorithm that converges to the global optimum.