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Context Aware Recommendation Systems: A review of the state of the art techniques
Recommendation systems are gaining increasing popularity in many application areas like
e-commerce, movie and music recommendations, tourism, news, advertisement, stock …
e-commerce, movie and music recommendations, tourism, news, advertisement, stock …
A review of client selection methods in federated learning
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' …
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 …
Combinatorial slee** bandits with fairness constraints
The multi-armed bandit (MAB) model has been widely adopted for studying many practical
optimization problems (network resource allocation, ad placement, crowdsourcing, etc.) with …
optimization problems (network resource allocation, ad placement, crowdsourcing, etc.) with …
Context-aware online client selection for hierarchical federated learning
Federated Learning (FL) has been considered as an appealing framework to tackle data
privacy issues of mobile devices compared to conventional Machine Learning (ML). Using …
privacy issues of mobile devices compared to conventional Machine Learning (ML). Using …
Model assertions for monitoring and improving ML models
Abstract Machine learning models are increasingly deployed in mission-critical settings such
as vehicles, but unfortunately, these models can fail in complex ways. To prevent errors, ML …
as vehicles, but unfortunately, these models can fail in complex ways. To prevent errors, ML …
Combinatorial neural bandits
We consider a contextual combinatorial bandit problem where in each round a learning
agent selects a subset of arms and receives feedback on the selected arms according to …
agent selects a subset of arms and receives feedback on the selected arms according to …
Contextual client selection for efficient federated learning over edge devices
Federated learning (FL) has emerged as a prominent distributed learning paradigm,
enabling collaborative training of neural network models across local devices with raw data …
enabling collaborative training of neural network models across local devices with raw data …
Power of redundancy: Surplus client scheduling for federated learning against user uncertainties
Federated learning (FL) has reshaped the learning paradigm by overcoming privacy
concerns and siloed data issues. In FL, an aggregator schedules a set of mobile users …
concerns and siloed data issues. In FL, an aggregator schedules a set of mobile users …
Harnessing context for budget-limited crowdsensing with massive uncertain workers
Crowdsensing is an emerging paradigm of ubiquitous sensing, through which a crowd of
workers are recruited to perform sensing tasks collaboratively. Although it has stimulated …
workers are recruited to perform sensing tasks collaboratively. Although it has stimulated …