[图书][B] Bandit algorithms
T Lattimore, C Szepesvári - 2020 - books.google.com
Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …
and the multi-armed bandit model is a commonly used framework to address it. This …
Introduction to multi-armed bandits
A Slivkins - Foundations and Trends® in Machine Learning, 2019 - nowpublishers.com
Multi-armed bandits a simple but very powerful framework for algorithms that make
decisions over time under uncertainty. An enormous body of work has accumulated over the …
decisions over time under uncertainty. An enormous body of work has accumulated over the …
Cascading bandits: Learning to rank in the cascade model
A search engine usually outputs a list of K web pages. The user examines this list, from the
first web page to the last, and chooses the first attractive page. This model of user behavior …
first web page to the last, and chooses the first attractive page. This model of user behavior …
Off-policy evaluation for slate recommendation
A Swaminathan, A Krishnamurthy… - Advances in …, 2017 - proceedings.neurips.cc
This paper studies the evaluation of policies that recommend an ordered set of items (eg, a
ranking) based on some context---a common scenario in web search, ads, and …
ranking) based on some context---a common scenario in web search, ads, and …
Combinatorial multi-armed bandit and its extension to probabilistically triggered arms
In the past few years, differential privacy has become a standard concept in the area of
privacy. One of the most important problems in this field is to answer queries while …
privacy. One of the most important problems in this field is to answer queries while …
Reinforcement learning for non-stationary markov decision processes: The blessing of (more) optimism
WC Cheung, D Simchi-Levi… - … conference on machine …, 2020 - proceedings.mlr.press
We consider un-discounted reinforcement learning (RL) in Markov decision processes
(MDPs) under drifting non-stationarity,\ie, both the reward and state transition distributions …
(MDPs) under drifting non-stationarity,\ie, both the reward and state transition distributions …
Combinatorial bandits revisited
R Combes… - Advances in neural …, 2015 - proceedings.neurips.cc
This paper investigates stochastic and adversarial combinatorial multi-armed bandit
problems. In the stochastic setting under semi-bandit feedback, we derive a problem-specific …
problems. In the stochastic setting under semi-bandit feedback, we derive a problem-specific …
Thompson sampling for combinatorial semi-bandits
S Wang, W Chen - International Conference on Machine …, 2018 - proceedings.mlr.press
We study the application of the Thompson sampling (TS) methodology to the stochastic
combinatorial multi-armed bandit (CMAB) framework. We analyze the standard TS algorithm …
combinatorial multi-armed bandit (CMAB) framework. We analyze the standard TS algorithm …
Hierarchical bayesian bandits
Abstract Meta-, multi-task, and federated learning can be all viewed as solving similar tasks,
drawn from a distribution that reflects task similarities. We provide a unified view of all these …
drawn from a distribution that reflects task similarities. We provide a unified view of all these …
Hedging the drift: Learning to optimize under nonstationarity
We introduce data-driven decision-making algorithms that achieve state-of-the-art dynamic
regret bounds for a collection of nonstationary stochastic bandit settings. These settings …
regret bounds for a collection of nonstationary stochastic bandit settings. These settings …