# List of research publications using Lilian Besson’s SMPyBandits project¶

I (Lilian Besson) have started my PhD in October 2016, and this project is a part of my on going research since December 2016.

## 1st article, about policy aggregation algorithm (aka model selection)¶

I designed and added the Aggregator policy, in order to test its validity and performance.

It is a “simple” voting algorithm to combine multiple bandit algorithms into one. Basically, it behaves like a simple MAB bandit just based on empirical means (even simpler than UCB), where arms are the child algorithms A_1 .. A_N, each running in “parallel”.

For more details, refer to this file: Aggregation.md and this research article.

## 2nd article, about Multi-players Multi-Armed Bandits¶

There is another point of view: instead of comparing different single-player policies on the same problem, we can make them play against each other, in a multi-player setting. The basic difference is about collisions : at each time t, if two or more user chose to sense the same channel, there is a collision. Collisions can be handled in different way from the base station point of view, and from each player point of view.

For more details, refer to this file: MultiPlayers.md and this research article.

## 3rd article, using Doubling Trick for Multi-Armed Bandits¶

I studied what Doubling Trick can and can’t do to obtain efficient anytime version of non-anytime optimal Multi-Armed Bandits algorithms.

For more details, refer to this file: DoublingTrick.md and this research article.

## 4th article, about Piece-Wise Stationary Multi-Armed Bandits¶

With Emilie Kaufmann, we studied the Generalized Likelihood Ratio Test (GLRT) for sub-Bernoulli distributions, and proposed the B-GLRT algorithm for change-point detection for piece-wise stationary one-armed bandit problems. We combined the B-GLRT with the kl-UCB multi-armed bandit algorithm and proposed the GLR-klUCB algorithm for piece-wise stationary multi-armed bandit problems. We prove finite-time guarantees for the B-GLRT and the GLR-klUCB algorithm, and we illustrate its performance with extensive numerical experiments.

For more details, refer to this file: NonStationaryBandits.md and this research article.