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Found 8 papers, 2 papers with code
Policy Iteration for Multiplicative Noise Output Feedback Control
We propose a policy iteration algorithm for solving the multiplicative noise linear quadratic output feedback design problem.
Data-Driven Optimal Control of Affine Systems: A Linear Programming Perspective
In this letter, we discuss the problem of optimal control for affine systems in the context of data-driven linear programming.
PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation
A numerical evaluation confirms the competitive performance of our method on classical control tasks.
Convergence Analysis of Homotopy-SGD for non-convex optimization
In this work, we present a first-order stochastic algorithm based on a combination of homotopy methods and SGD, called Homotopy-Stochastic Gradient Descent (H-SGD), which finds interesting connections with some proposed heuristics in the literature, e. g. optimization by Gaussian continuation, training by diffusion, mollifying networks.
On the Promise of the Stochastic Generalized Gauss-Newton Method for Training DNNs
This enables researchers to further study and improve this promising optimization technique and hopefully reconsider stochastic second-order methods as competitive optimization techniques for training DNNs; we also hope that the promise of SGN may lead to forward automatic differentiation being added to Tensorflow or Pytorch.
Transferring Optimality Across Data Distributions via Homotopy Methods
Homotopy methods, also known as continuation methods, are a powerful mathematical tool to efficiently solve various problems in numerical analysis, including complex non-convex optimization problems where no or only little prior knowledge regarding the localization of the solutions is available.
Probabilistic Rollouts for Learning Curve Extrapolation Across Hyperparameter Settings
We propose probabilistic models that can extrapolate learning curves of iterative machine learning algorithms, such as stochastic gradient descent for training deep networks, based on training data with variable-length learning curves.
A Distributed Second-Order Algorithm You Can Trust
Due to the rapid growth of data and computational resources, distributed optimization has become an active research area in recent years.
