657 research outputs found
A Neural Networks Committee for the Contextual Bandit Problem
This paper presents a new contextual bandit algorithm, NeuralBandit, which
does not need hypothesis on stationarity of contexts and rewards. Several
neural networks are trained to modelize the value of rewards knowing the
context. Two variants, based on multi-experts approach, are proposed to choose
online the parameters of multi-layer perceptrons. The proposed algorithms are
successfully tested on a large dataset with and without stationarity of
rewards.Comment: 21st International Conference on Neural Information Processin
Context Attentive Bandits: Contextual Bandit with Restricted Context
We consider a novel formulation of the multi-armed bandit model, which we
call the contextual bandit with restricted context, where only a limited number
of features can be accessed by the learner at every iteration. This novel
formulation is motivated by different online problems arising in clinical
trials, recommender systems and attention modeling. Herein, we adapt the
standard multi-armed bandit algorithm known as Thompson Sampling to take
advantage of our restricted context setting, and propose two novel algorithms,
called the Thompson Sampling with Restricted Context(TSRC) and the Windows
Thompson Sampling with Restricted Context(WTSRC), for handling stationary and
nonstationary environments, respectively. Our empirical results demonstrate
advantages of the proposed approaches on several real-life datasetsComment: IJCAI 201
- …