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Found 14 papers, 1 papers with code
Federated Linear Contextual Bandits with Heterogeneous Clients
The demand for collaborative and private bandit learning across multiple agents is surging due to the growing quantity of data generated from distributed systems.
Incentivized Truthful Communication for Federated Bandits
To enhance the efficiency and practicality of federated bandit learning, recent advances have introduced incentives to motivate communication among clients, where a client participates only when the incentive offered by the server outweighs its participation cost.
Communication-Efficient Federated Non-Linear Bandit Optimization
Federated optimization studies the problem of collaborative function optimization among multiple clients (e. g. mobile devices or organizations) under the coordination of a central server.
Pure Exploration in Asynchronous Federated Bandits
We study the federated pure exploration problem of multi-armed bandits and linear bandits, where $M$ agents cooperatively identify the best arm via communicating with the central server.
How Bad is Top-$K$ Recommendation under Competing Content Creators?
Content creators compete for exposure on recommendation platforms, and such strategic behavior leads to a dynamic shift over the content distribution.
Exploring the Effect of Primitives for Compositional Generalization in Vision-and-Language
Compositional generalization is critical to simulate the compositional capability of humans, and has received much attention in the vision-and-language (V&L) community.
Communication Efficient Distributed Learning for Kernelized Contextual Bandits
We tackle the communication efficiency challenge of learning kernelized contextual bandits in a distributed setting.
Learning from a Learning User for Optimal Recommendations
In real-world recommendation problems, especially those with a formidably large item space, users have to gradually learn to estimate the utility of any fresh recommendations from their experience about previously consumed items.
Communication Efficient Federated Learning for Generalized Linear Bandits
Contextual bandit algorithms have been recently studied under the federated learning setting to satisfy the demand of keeping data decentralized and pushing the learning of bandit models to the client side.
Learning the Optimal Recommendation from Explorative Users
We propose a new problem setting to study the sequential interactions between a recommender system and a user.
Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits
In this paper, we study linear contextual bandit in a federated learning setting.
When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution
However, all existing collaborative bandit learning solutions impose a stationary assumption about the environment, i. e., both user preferences and the dependency among users are assumed static over time.
Incentivizing Exploration in Linear Bandits under Information Gap
We study the problem of incentivizing exploration for myopic users in linear bandits, where the users tend to exploit arm with the highest predicted reward instead of exploring.
Unifying Clustered and Non-stationary Bandits
Non-stationary bandits and online clustering of bandits lift the restrictive assumptions in contextual bandits and provide solutions to many important real-world scenarios.
