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Found 9 papers, 2 papers with code
Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need
We leverage multilevel Monte Carlo (MLMC) to improve the performance of multi-step look-ahead Bayesian optimization (BO) methods that involve nested expectations and maximizations.
Exponential Concentration in Stochastic Approximation
We apply our results to several different Stochastic Approximation algorithms, specifically Projected Stochastic Gradient Descent, Kiefer-Wolfowitz and Stochastic Frank-Wolfe algorithms.
Learning and Information in Stochastic Networks and Queues
We review the role of information and learning in the stability and optimization of queueing systems.
Reinforcement Learning for Traffic Signal Control: Comparison with Commercial Systems
Recently, Intelligent Transportation Systems are leveraging the power of increased sensory coverage and computing power to deliver data-intensive solutions achieving higher levels of performance than traditional systems.
An Adiabatic Theorem for Policy Tracking with TD-learning
We evaluate the ability of temporal difference learning to track the reward function of a policy as it changes over time.
Fast Approximate Bayesian Contextual Cold Start Learning (FAB-COST)
Cold-start is a notoriously difficult problem which can occur in recommendation systems, and arises when there is insufficient information to draw inferences for users or items.
Regret Analysis of a Markov Policy Gradient Algorithm for Multi-arm Bandits
We consider a policy gradient algorithm applied to a finite-arm bandit problem with Bernoulli rewards.
A Short Note on Soft-max and Policy Gradients in Bandits Problems
This is a short communication on a Lyapunov function argument for softmax in bandit problems.
Adaptive Pricing in Insurance: Generalized Linear Models and Gaussian Process Regression Approaches
We develop two pricing models: an adaptive Generalized Linear Model (GLM) and an adaptive Gaussian Process (GP) regression model.
