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Found 9 papers, 3 papers with code
Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users
We study the problem of federated contextual combinatorial cascading bandits, where $|\mathcal{U}|$ agents collaborate under the coordination of a central server to provide tailored recommendations to the $|\mathcal{U}|$ corresponding users.
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, such as contextual cascading bandits and contextual influence maximization bandits.
Efficient Explorative Key-term Selection Strategies for Conversational Contextual Bandits
To tackle these issues, we first propose ``ConLinUCB", a general framework for conversational bandits with better information incorporation, combining arm-level and key-term-level feedback to estimate user preference in one step at each time.
On-Demand Communication for Asynchronous Multi-Agent Bandits
We propose ODC, an on-demand communication protocol that tailors the communication of each pair of agents based on their empirical pull times.
Batch-Size Independent Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms or Independent Arms
Under this new condition, we propose a BCUCB-T algorithm with variance-aware confidence intervals and conduct regret analysis which reduces the $O(K)$ factor to $O(\log K)$ or $O(\log^2 K)$ in the regret bound, significantly improving the regret bounds for the above applications.
Federated Online Clustering of Bandits
Contextual multi-armed bandit (MAB) is an important sequential decision-making problem in recommendation systems.
Multi-layered Network Exploration via Random Walks: From Offline Optimization to Online Learning
For the online learning setting, neither the network structure nor the node weights are known initially.
Online Competitive Influence Maximization
In this paper, we introduce a new Online Competitive Influence Maximization (OCIM) problem, where two competing items (e. g., products, news stories) propagate in the same network and influence probabilities on edges are unknown.
Graphlet Count Estimation via Convolutional Neural Networks
The number of each graphlet, called graphlet count, is a signature which characterizes the local network structure of a given graph.
