activity
20182026
most citedBatch-Size Independent Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms or Independent Arms

6 citations · 29 across the 24 of their papers we have counts for

collaborators
Showing 2024Show all

7 papers · 1 filter

quant-ph2024

Quantum Algorithm for Online Exp-concave Optimization

Jianhao He, Chengchang Liu, Xutong Liu +2

We explore whether quantum advantages can be found for the zeroth-order feedback online exp-concave optimization problem, which is also known as bandit exp-concave optimization wit…

cs.LG2024

Combinatorial Logistic Bandits

Xutong Liu, Xiangxiang Dai, Xuchuang Wang +2

We introduce a novel framework called combinatorial logistic bandits (CLogB), where in each round, a subset of base arms (called the super arm) is selected, with the outcome of eac…

cs.LG2024

Stochastic Bandits Robust to Adversarial Attacks

Xuchuang Wang, Jinhang Zuo, Xutong Liu +2

This paper investigates stochastic multi-armed bandit algorithms that are robust to adversarial attacks, where an attacker can first observe the learner's action and {then} alter t…

cs.MM2024

AxiomVision: Accuracy-Guaranteed Adaptive Visual Model Selection for Perspective-Aware Video Analytics

Xiangxiang Dai, Zeyu Zhang, Peng Yang +3

The rapid evolution of multimedia and computer vision technologies requires adaptive visual model deployment strategies to effectively handle diverse tasks and varying environments…

cs.LG2024★ 1 cited

Combinatorial Multivariant Multi-Armed Bandits with Applications to Episodic Reinforcement Learning and Beyond

Xutong Liu, Siwei Wang, Jinhang Zuo +7

We introduce a novel framework of combinatorial multi-armed bandits (CMAB) with multivariant and probabilistically triggering arms (CMAB-MT), where the outcome of each arm is a …

cs.LG2024★ 1 cited

Cost-Effective Online Multi-LLM Selection with Versatile Reward Models

Xiangxiang Dai, Jin Li, Xutong Liu +2

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…

cs.LG2024

Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users

Hantao Yang, Xutong Liu, Zhiyong Wang +4

We study the problem of federated contextual combinatorial cascading bandits, where agents collaborate under the coordination of a central server to provide tailore…