Search Results for author:
Found 4 papers, 1 papers with code
Optimizing Optimizers: Regret-optimal gradient descent algorithms
The need for fast and robust optimization algorithms are of critical importance in all areas of machine learning.
A Latent Variational Framework for Stochastic Optimization
This paper provides a unifying theoretical framework for stochastic optimization algorithms by means of a latent stochastic variational problem.
Deep Q-Learning for Nash Equilibria: Nash-DQN
Model-free learning for multi-agent stochastic games is an active area of research.
Trading algorithms with learning in latent alpha models
Under fairly general assumptions, we demonstrate how the trader can learn the posterior distribution over the latent states, and explicitly solve the latent optimal trading problem.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
