[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 …
An efficiency-boosting client selection scheme for federated learning with fairness guarantee
The issue of potential privacy leakage during centralized AI's model training has drawn
intensive concern from the public. A Parallel and Distributed Computing (or PDC) scheme …
intensive concern from the public. A Parallel and Distributed Computing (or PDC) scheme …
Multi-armed bandits in recommendation systems: A survey of the state-of-the-art and future directions
Abstract Recommender Systems (RSs) have assumed a crucial role in several digital
companies by directly affecting their key performance indicators. Nowadays, in this era of big …
companies by directly affecting their key performance indicators. Nowadays, in this era of big …
A review of client selection methods in federated learning
S Mayhoub, T M. Shami - Archives of Computational Methods in …, 2024 - Springer
Federated learning (FL) is a promising new technology that allows machine learning (ML)
models to be trained locally on edge devices while preserving the privacy of the devices' …
models to be trained locally on edge devices while preserving the privacy of the devices' …
Reinforcement learning to rank in e-commerce search engine: Formalization, analysis, and application
In E-commerce platforms such as Amazon and TaoBao, ranking items in a search session is
a typical multi-step decision-making problem. Learning to rank (LTR) methods have been …
a typical multi-step decision-making problem. Learning to rank (LTR) methods have been …
Spatio–temporal edge service placement: A bandit learning approach
Shared edge computing platforms deployed at the radio access network are expected to
significantly improve the quality-of-service delivered by application service providers (ASPs) …
significantly improve the quality-of-service delivered by application service providers (ASPs) …
Online learning to rank in stochastic click models
M Zoghi, T Tunys, M Ghavamzadeh… - International …, 2017 - proceedings.mlr.press
Online learning to rank is a core problem in information retrieval and machine learning.
Many provably efficient algorithms have been recently proposed for this problem in specific …
Many provably efficient algorithms have been recently proposed for this problem in specific …
Contextual combinatorial bandits with probabilistically triggered arms
We study contextual combinatorial bandits with probabilistically triggered arms (C $^ 2$
MAB-T) under a variety of smoothness conditions that capture a wide range of applications …
MAB-T) under a variety of smoothness conditions that capture a wide range of applications …
Residential HVAC aggregation based on risk-averse multi-armed bandit learning for secondary frequency regulation
As the penetration of renewable energy continues to increase, stochastic and intermittent
generation resources gradually replace the conventional generators, bringing significant …
generation resources gradually replace the conventional generators, bringing significant …
Batch-size independent regret bounds for combinatorial semi-bandits with probabilistically triggered arms or independent arms
In this paper, we study the combinatorial semi-bandits (CMAB) and focus on reducing the
dependency of the batch-size $ K $ in the regret bound, where $ K $ is the total number of …
dependency of the batch-size $ K $ in the regret bound, where $ K $ is the total number of …