Cover Image

Falcon: Fair Active Learning using Multi-armed Bandits

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Tae, Ki Hyun, Zhang, Hantian, Park, Jaeyoung, Rong, Kexin, Whang, Steven Euijong
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Fair Class-Incremental Learning using Sample Weighting
by: Park, Jaeyoung, et al.
Published: (2024)

T-CIL: Temperature Scaling using Adversarial Perturbation for Calibration in Class-Incremental Learning
by: Hwang, Seong-Hyeon, et al.
Published: (2025)

PFGuard: A Generative Framework with Privacy and Fairness Safeguards
by: Kim, Soyeon, et al.
Published: (2024)

MIDAS: Misalignment-based Data Augmentation Strategy for Imbalanced Multimodal Learning
by: Hwang, Seong-Hyeon, et al.
Published: (2025)

RC-Mixup: A Data Augmentation Strategy against Noisy Data for Regression Tasks
by: Hwang, Seong-Hyeon, et al.
Published: (2024)

GradMix: Gradient-based Selective Mixup for Robust Data Augmentation in Class-Incremental Learning
by: Kim, Minsu, et al.
Published: (2025)

Fairness of Exposure in Online Restless Multi-armed Bandits
by: Sood, Archit, et al.
Published: (2024)

Multi-agent Multi-armed Bandits with Minimum Reward Guarantee Fairness
by: Manupriya, Piyushi, et al.
Published: (2025)

Explanation Multiplicity in SHAP: Characterization and Assessment
by: Hwang, Hyunseung, et al.
Published: (2026)

Offline Learning for Combinatorial Multi-armed Bandits
by: Liu, Xutong, et al.
Published: (2025)

Transfer Learning for Contextual Multi-armed Bandits
by: Cai, Changxiao, et al.
Published: (2022)

Multi-armed Bandits with Missing Outcome
by: Mahrooghi, Ilia, et al.
Published: (2024)

Learning with Limited Shared Information in Multi-agent Multi-armed Bandit
by: Shao, Junning, et al.
Published: (2025)

SHAP-based Explanations are Sensitive to Feature Representation
by: Hwang, Hyunseung, et al.
Published: (2025)

Meritocratic Fairness in Budgeted Combinatorial Multi-armed Bandits via Shapley Values
by: Sharma, Shradha, et al.
Published: (2026)

Replicability is Asymptotically Free in Multi-armed Bandits
by: Komiyama, Junpei, et al.
Published: (2024)

Maximal Objectives in the Multi-armed Bandit with Applications
by: Ozbay, Eren, et al.
Published: (2020)

Causally Abstracted Multi-armed Bandits
by: Zennaro, Fabio Massimo, et al.
Published: (2024)

Deceptive Exploration in Multi-armed Bandits
by: Vurankaya, I. Arda, et al.
Published: (2025)

Hybrid Combinatorial Multi-armed Bandits with Probabilistically Triggered Arms
by: Zhou, Kongchang, et al.
Published: (2025)

Kullback-Leibler Maillard Sampling for Multi-armed Bandits with Bounded Rewards
by: Qin, Hao, et al.
Published: (2023)

Locally Private Nonparametric Contextual Multi-armed Bandits
by: Ma, Yuheng, et al.
Published: (2025)

Multi-agent Multi-armed Bandit with Fully Heavy-tailed Dynamics
by: Wang, Xingyu, et al.
Published: (2025)

Heterogeneous Multi-agent Multi-armed Bandits on Stochastic Block Models
by: Xu, Mengfan, et al.
Published: (2025)

Transfer in Sequential Multi-armed Bandits via Reward Samples
by: R, Rahul N, et al.
Published: (2024)

Recommenadation aided Caching using Combinatorial Multi-armed Bandits
by: J, Pavamana K, et al.
Published: (2024)

GINO-Q: Learning an Asymptotically Optimal Index Policy for Restless Multi-armed Bandits
by: Chen, Gongpu, et al.
Published: (2024)

Optimal Control of Fluid Restless Multi-armed Bandits: A Machine Learning Approach
by: Bertsimas, Dimitris, et al.
Published: (2025)

Design Experiments to Compare Multi-armed Bandit Algorithms
by: Meng, Huiling, et al.
Published: (2026)

Empirical Bayesian Multi-Bandit Learning
by: Jiang, Xia, et al.
Published: (2025)

ERBench: An Entity-Relationship based Automatically Verifiable Hallucination Benchmark for Large Language Models
by: Oh, Jio, et al.
Published: (2024)

Efficient and Optimal Policy Gradient Algorithm for Corrupted Multi-armed Bandits
by: Liu, Jiayuan, et al.
Published: (2025)

On the Low-Complexity of Fair Learning for Combinatorial Multi-Armed Bandit
by: Wu, Xiaoyi, et al.
Published: (2025)

Adaptive Endpointing with Deep Contextual Multi-armed Bandits
by: Min, Do June, et al.
Published: (2023)

Extended UCB Policies for Multi-armed Bandit Problems
by: Liu, Keqin, et al.
Published: (2011)

Meta-Learning Multi-armed Bandits for Beam Tracking in 5G and 6G Networks
by: Mattick, Alexander, et al.
Published: (2025)

Decentralized Blockchain-based Robust Multi-agent Multi-armed Bandit
by: Xu, Mengfan, et al.
Published: (2024)

Multi-Objective Multi-Agent Bandits: From Learning Efficiency to Fairness Optimization
by: Wang, John, et al.
Published: (2026)

On the Regularity and Fairness of Combinatorial Multi-Armed Bandit
by: Wu, Xiaoyi, et al.
Published: (2025)

Rethinking Reinforcement fine-tuning of LLMs: A Multi-armed Bandit Learning Perspective
by: Hu, Xiao, et al.
Published: (2026)