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Found 10 papers, 2 papers with code
Stochastic Rising Bandits
This paper is in the field of stochastic Multi-Armed Bandits (MABs), i. e., those sequential selection techniques able to learn online using only the feedback given by the chosen option (a. k. a.
Dynamic Pricing with Volume Discounts in Online Settings
We ran a real-world 4-month-long A/B testing experiment in collaboration with an Italian e-commerce company, in which our algorithm PVD-B-corresponding to A configuration-has been compared with human pricing specialists-corresponding to B configuration.
Multi-Armed Bandit Problem with Temporally-Partitioned Rewards: When Partial Feedback Counts
We provide two algorithms to address TP-MAB problems, namely, TP-UCB-FR and TP-UCB-EW, which exploit the partial information disclosed by the reward collected over time.
ARLO: A Framework for Automated Reinforcement Learning
In this work, we propose a general and flexible framework, namely ARLO: Automated Reinforcement Learning Optimizer, to construct automated pipelines for AutoRL.
Safe Online Bid Optimization with Return-On-Investment and Budget Constraints subject to Uncertainty
More interestingly, we provide an algorithm, namely GCB_{safe}(\psi,\phi), guaranteeing both sublinear pseudo-regret and safety w. h. p.
Adapting Bandit Algorithms for Settings with Sequentially Available Arms
Instead, in such applications, a set of options is presented sequentially to the learner within a time span, and this process is repeated throughout a time horizon.
Online Joint Bid/Daily Budget Optimization of Internet Advertising Campaigns
We experimentally evaluate our algorithms with synthetic settings generated from real data from Yahoo!, and we present the results of the adoption of our algorithms in a real-world application with a daily average spent of 1, 000 Euros for more than one year.
Learning Probably Approximately Correct Maximin Strategies in Simulation-Based Games with Infinite Strategy Spaces
As a result, solving these games begets the challenge of designing learning algorithms that can find (approximate) equilibria with high confidence, using as few simulator queries as possible.
Unimodal Thompson Sampling for Graph-Structured Arms
We study, to the best of our knowledge, the first Bayesian algorithm for unimodal Multi-Armed Bandit (MAB) problems with graph structure.
Machine Learning Techniques for Stackelberg Security Games: a Survey
The present survey aims at presenting the current machine learning techniques employed in security games domains.
