
Langevin Dynamics for Inverse Reinforcement Learning of Stochastic Gradient Algorithms
Inverse reinforcement learning (IRL) aims to estimate the reward functio...
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Variance reduction for distributed stochastic gradient MCMC
Stochastic gradient MCMC methods, such as stochastic gradient Langevin d...
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SVAG: Unified Convergence Results for SAGSAGA Interpolation with Stochastic Variance Adjusted Gradient Descent
We analyze SVAG, a variance reduced stochastic gradient method with SAG ...
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A Universally Optimal Multistage Accelerated Stochastic Gradient Method
We study the problem of minimizing a strongly convex and smooth function...
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Efficient Implementation of SecondOrder Stochastic Approximation Algorithms in HighDimensional Problems
Stochastic approximation (SA) algorithms have been widely applied in min...
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On the rates of convergence of Parallelized Averaged Stochastic Gradient Algorithms
The growing interest for high dimensional and functional data analysis l...
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A fast and recursive algorithm for clustering large datasets with kmedians
Clustering with fast algorithms large samples of high dimensional data i...
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Multikernel Passive Stochastic Gradient Algorithms
This paper develops a novel passive stochastic gradient algorithm. In passive stochastic approximation, the stochastic gradient algorithm does not have control over the location where noisy gradients of the cost function are evaluated. Classical passive stochastic gradient algorithms use a kernel that approximates a Dirac delta to weigh the gradients based on how far they are evaluated from the desired point. In this paper we construct a multikernel passive stochastic gradient algorithm. The algorithm performs substantially better in high dimensional problems and incorporates variance reduction. We analyze the weak convergence of the multikernel algorithm and its rate of convergence. In numerical examples, we study the multikernel version of the LMS algorithm to compare the performance with the classical passive version.
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