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 …
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 …
Learning Context-Aware Probabilistic Maximum Coverage Bandits: A Variance-Adaptive Approach
Probabilistic maximum coverage (PMC) is an important framework that can model many
network applications, including mobile crowdsensing, content delivery, and task repli¬ …
network applications, including mobile crowdsensing, content delivery, and task repli¬ …
Variance-adaptive algorithm for probabilistic maximum coverage bandits with general feedback
Probabilistic maximum coverage (PMC) is an important problem that can model many
network applications, including mobile crowdsensing, network content delivery, and …
network applications, including mobile crowdsensing, network content delivery, and …
Linkselfie: Link selection and fidelity estimation in quantum networks
Reliable transmission of fragile quantum information requires one to efficiently select and
utilize high-fidelity links among multiple noisy quantum links. However, the fidelity, a quality …
utilize high-fidelity links among multiple noisy quantum links. However, the fidelity, a quality …
Online competitive influence maximization
Online influence maximization has attracted much attention as a way to maximize influence
spread through a social network while learning the values of unknown network parameters …
spread through a social network while learning the values of unknown network parameters …
Variance-Aware Bandit Framework for Dynamic Probabilistic Maximum Coverage Problem With Triggered or Self-Reliant Arms
The Probabilistic Maximum Coverage (PMC) problem plays a pivotal role in modeling
various network applications, such as mobile crowdsensing, which involves selecting nodes …
various network applications, such as mobile crowdsensing, which involves selecting nodes …
Combinatorial Multivariant Multi-Armed Bandits with Applications to Episodic Reinforcement Learning and Beyond
We introduce a novel framework of combinatorial multi-armed bandits (CMAB) with
multivariant and probabilistically triggering arms (CMAB-MT), where the outcome of each …
multivariant and probabilistically triggering arms (CMAB-MT), where the outcome of each …
Cost-Effective Online Multi-LLM Selection with Versatile Reward Models
With the rapid advancement of large language models (LLMs), the diversity of multi-LLM
tasks and the variability in their pricing structures have become increasingly important, as …
tasks and the variability in their pricing structures have become increasingly important, as …
Fixed confidence community mode estimation
Our aim is to estimate the largest community (aka, mode) in a population composed of
multiple disjoint communities. This estimation is performed in a fixed confidence setting via …
multiple disjoint communities. This estimation is performed in a fixed confidence setting via …