# Etat de l'art sur l'application des bandits multi-bras

arXiv: https://arxiv.org/abs/2101.00001 · HTML: https://mlsanity.com/paper/2101.00001/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 44 of its 105 references that are in the map.
- First posted on arXiv 4 Jan 2021.

## Builds on (most cited first, 30 of 44)

- [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016)
- [An ADMM Based Framework for AutoML Pipeline Configuration](https://mlsanity.com/paper/1905.00424/) (2019)
- [A Survey on Practical Applications of Multi-Armed and Contextual Bandits](https://mlsanity.com/paper/1904.10040/) (2019)
- [Scalable Recollections for Continual Lifelong Learning](https://mlsanity.com/paper/1711.06761/) (2017)
- [A Neural Networks Committee for the Contextual Bandit Problem](https://mlsanity.com/paper/1409.8191/) (2014)
- [Beyond Backprop: Online Alternating Minimization with Auxiliary Variables](https://mlsanity.com/paper/1806.09077/) (2018)
- [Multi-armed Bandit Problem with Known Trend](https://mlsanity.com/paper/1508.07091/) (2015)
- [Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing](https://mlsanity.com/paper/1801.10242/) (2018)
- [Incorporating Behavioral Constraints in Online AI Systems](https://mlsanity.com/paper/1809.05720/) (2018)
- [Context Attentive Bandits: Contextual Bandit with Restricted Context](https://mlsanity.com/paper/1705.03821/) (2017)
- [Optimal Exploitation of Clustering and History Information in Multi-Armed Bandit](https://mlsanity.com/paper/1906.03979/) (2019)
- [A Story of Two Streams: Reinforcement Learning Models from Human Behavior and Neuropsychiatry](https://mlsanity.com/paper/1906.11286/) (2019)
- [Unified Models of Human Behavioral Agents in Bandits, Contextual Bandits and RL](https://mlsanity.com/paper/2005.04544/) (2020)
- [Bandit Models of Human Behavior: Reward Processing in Mental Disorders](https://mlsanity.com/paper/1706.02897/) (2017)
- [How can AI Automate End-to-End Data Science?](https://mlsanity.com/paper/1910.14436/) (2019)
- [Building Bridges: Viewing Active Learning from the Multi-Armed Bandit Lens](https://mlsanity.com/paper/1309.6830/) (2013)
- [Customized Nonlinear Bandits for Online Response Selection in Neural Conversation Models](https://mlsanity.com/paper/1711.08493/) (2017)
- [Online Learning in Iterated Prisoner's Dilemma to Mimic Human Behavior](https://mlsanity.com/paper/2006.06580/) (2020)
- [Contextual Bandit with Missing Rewards](https://mlsanity.com/paper/2007.06368/) (2020)
- [Exponentiated Gradient Exploration for Active Learning](https://mlsanity.com/paper/1408.2196/) (2014)
- [Interpretable Multi-Objective Reinforcement Learning through Policy Orchestration](https://mlsanity.com/paper/1809.08343/) (2018)
- [A Bandit Approach to Posterior Dialog Orchestration Under a Budget](https://mlsanity.com/paper/1906.09384/) (2019)
- [Hyper-parameter Tuning for the Contextual Bandit](https://mlsanity.com/paper/2005.02209/) (2020)
- [Online learning with Corrupted context: Corrupted Contextual Bandits](https://mlsanity.com/paper/2006.15194/) (2020)
- [The Impact of Situation Clustering in Contextual-Bandit Algorithm for Context-Aware Recommender Systems](https://mlsanity.com/paper/1304.3845/) (2013)
- [Context-Based Information Retrieval in Risky Environment](https://mlsanity.com/paper/1409.7729/) (2014)
- [Freshness-Aware Thompson Sampling](https://mlsanity.com/paper/1409.8572/) (2014)
- [Solving Constrained CASH Problems with ADMM](https://mlsanity.com/paper/2006.09635/) (2020)
- [Online Semi-Supervised Learning with Bandit Feedback](https://mlsanity.com/paper/2010.12574/) (2020)
- [Role of temporal inference in the recognition of textual inference](https://mlsanity.com/paper/1302.5645/) (2013)

## Abstract (arXiv)

The Multi-armed bandit offer the advantage to learn and exploit the already learnt knowledge at the same time. This capability allows this approach to be applied in different domains, going from clinical trials where the goal is investigating the effects of different experimental treatments while minimizing patient losses, to adaptive routing where the goal is to minimize the delays in a network. This article provides a review of the recent results on applying bandit to real-life scenario and summarize the state of the art for each of these fields. Different techniques has been proposed to solve this problem setting, like epsilon-greedy, Upper confident bound (UCB) and Thompson Sampling (TS). We are showing here how this algorithms were adapted to solve the different problems of exploration exploitation.