Search Results for author:
Found 7 papers, 1 papers with code
Practical Policy Optimization with Personalized Experimentation
Many organizations measure treatment effects via an experimentation platform to evaluate the casual effect of product variations prior to full-scale deployment.
Multi-armed Bandits with Cost Subsidy
We then establish a fundamental lower bound on the performance of any online learning algorithm for this problem, highlighting the hardness of our problem in comparison to the classical MAB problem.
Active Learning for Skewed Data Sets
Furthermore, there is usually only a small amount of initial training data available when building machine-learned models to solve such problems.
Best Arm Identification in Generalized Linear Bandits
Motivated by drug design, we consider the best-arm identification problem in generalized linear bandits.
Learning to Price with Reference Effects
As a firm varies the price of a product, consumers exhibit reference effects, making purchase decisions based not only on the prevailing price but also the product's price history.
A Tutorial on Thompson Sampling
Thompson sampling is an algorithm for online decision problems where actions are taken sequentially in a manner that must balance between exploiting what is known to maximize immediate performance and investing to accumulate new information that may improve future performance.
Conservative Contextual Linear Bandits
We prove an upper-bound on the regret of CLUCB and show that it can be decomposed into two terms: 1) an upper-bound for the regret of the standard linear UCB algorithm that grows with the time horizon and 2) a constant (does not grow with the time horizon) term that accounts for the loss of being conservative in order to satisfy the safety constraint.
