11 citations · 35 across the 20 of their papers we have counts for
5 papers · 1 filter
A Riemannian Optimization Perspective of the Gauss-Newton Method for Feedforward Neural Networks
Semih Cayci
In this work, we establish non-asymptotic convergence bounds for the Gauss-Newton method in training neural networks with smooth activations. In the underparameterized regime, the…
Optimal Rates of Convergence for Entropy Regularization in Discounted Markov Decision Processes
Johannes Müller, Semih Cayci
We study the error introduced by entropy regularization in infinite-horizon discrete discounted Markov decision processes. We show that this error decreases exponentially in the in…
Recurrent Natural Policy Gradient for POMDPs
Semih Cayci, Atilla Eryilmaz
Solving partially observable Markov decision processes (POMDPs) remains a fundamental challenge in reinforcement learning (RL), primarily due to the curse of dimensionality induced…
Fisher-Rao Gradient Flows of Linear Programs and State-Action Natural Policy Gradients
Johannes Müller, Semih Çaycı, Guido Montúfar
Kakade's natural policy gradient method has been studied extensively in recent years, showing linear convergence with and without regularization. We study another natural gradient…
Convergence of Gradient Descent for Recurrent Neural Networks: A Nonasymptotic Analysis
Semih Cayci, Atilla Eryilmaz
We analyze recurrent neural networks with diagonal hidden-to-hidden weight matrices, trained with gradient descent in the supervised learning setting, and prove that gradient desce…