:: Library Catalog

Ata uhi

I tiakina i:

Ngā taipitopito rārangi puna kōrero
Ngā kaituhi matua:	Lazzaro, Joseph, Pike-Burke, Ciara
Hōputu:	Preprint
I whakaputaina:	2025
Ngā marau:	Machine Learning
Urunga tuihono:	https://arxiv.org/abs/2507.08994
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!

Ngā tūemi rite

Fixed-Budget Change Point Identification in Piecewise Constant Bandits
mā: Lazzaro, Joseph, me ētahi atu.
I whakaputaina: (2025)

Locally Differentially Private Thresholding Bandits
mā: Barbara, Annalisa, me ētahi atu.
I whakaputaina: (2025)

QuACK: A Multipurpose Queuing Algorithm for Cooperative $k$-Armed Bandits
mā: Howson, Benjamin, me ētahi atu.
I whakaputaina: (2024)

The Sample Complexity of Multiple Change Point Identification under Bandit Feedback
mā: Graf, Maximilian, me ētahi atu.
I whakaputaina: (2026)

Learning Fair And Effective Points-Based Rewards Programs
mā: Hssaine, Chamsi, me ētahi atu.
I whakaputaina: (2025)

When and why randomised exploration works (in linear bandits)
mā: Abeille, Marc, me ētahi atu.
I whakaputaina: (2025)

Sample-Efficiency in Multi-Batch Reinforcement Learning: The Need for Dimension-Dependent Adaptivity
mā: Johnson, Emmeran, me ētahi atu.
I whakaputaina: (2023)

Optimal Best Arm Identification with Fixed Confidence in Restless Bandits
mā: Karthik, P. N., me ētahi atu.
I whakaputaina: (2023)

Stochastic Shortest Path with Sparse Adversarial Costs
mā: Johnson, Emmeran, me ētahi atu.
I whakaputaina: (2025)

Optimal Best-Arm Identification under Fixed Confidence with Multiple Optima
mā: Truong, Lan V.
I whakaputaina: (2025)

On the necessity of adaptive regularisation:Optimal anytime online learning on $\boldsymbol{\ell_p}$-balls
mā: Johnson, Emmeran, me ētahi atu.
I whakaputaina: (2025)

Bandit Pareto Set Identification: the Fixed Budget Setting
mā: Kone, Cyrille, me ētahi atu.
I whakaputaina: (2023)

Constrained Pareto Set Identification with Bandit Feedback
mā: Kone, Cyrille, me ētahi atu.
I whakaputaina: (2025)

Fair Best Arm Identification with Fixed Confidence
mā: Russo, Alessio, me ētahi atu.
I whakaputaina: (2024)

Fixed Confidence Best Arm Identification in the Bayesian Setting
mā: Jang, Kyoungseok, me ētahi atu.
I whakaputaina: (2024)

Risk-Averse Best Arm Set Identification with Fixed Budget and Fixed Confidence
mā: Nonaga, Shunta, me ētahi atu.
I whakaputaina: (2025)

A Finite Time Analysis of Thompson Sampling for Bayesian Optimization with Preferential Feedback
mā: Lazzaro, Joseph, me ētahi atu.
I whakaputaina: (2026)

Fixed-Budget Constrained Best Arm Identification in Grouped Bandits
mā: Mukherjee, Raunak, me ētahi atu.
I whakaputaina: (2026)

Multi-thresholding Good Arm Identification with Bandit Feedback
mā: Jiang, Xuanke, me ētahi atu.
I whakaputaina: (2025)

The Cost of Learning under Multiple Change Points
mā: Gafni, Tomer, me ētahi atu.
I whakaputaina: (2026)

Multiclass Online Learnability under Bandit Feedback
mā: Raman, Ananth, me ētahi atu.
I whakaputaina: (2023)

Fixed-Confidence Best Arm Identification with Decreasing Variance
mā: Roychowdhury, Tamojeet, me ētahi atu.
I whakaputaina: (2025)

Fixed Budget is No Harder Than Fixed Confidence in Best-Arm Identification up to Logarithmic Factors
mā: Balagopalan, Kapilan, me ētahi atu.
I whakaputaina: (2026)

Robust Pareto Set Identification with Contaminated Bandit Feedback
mā: Korkmaz, İlter Onat, me ētahi atu.
I whakaputaina: (2022)

Optimal Multi-Objective Best Arm Identification with Fixed Confidence
mā: Chen, Zhirui, me ētahi atu.
I whakaputaina: (2025)

Prior-Dependent Allocations for Bayesian Fixed-Budget Best-Arm Identification in Structured Bandits
mā: Nguyen, Nicolas, me ētahi atu.
I whakaputaina: (2024)

Learning Markov Decision Processes under Fully Bandit Feedback
mā: Zhuo, Zhengjia, me ētahi atu.
I whakaputaina: (2026)

Nearest Neighbour with Bandit Feedback
mā: Pasteris, Stephen, me ētahi atu.
I whakaputaina: (2023)

Optimal High-Probability Regret for Online Convex Optimization with Two-Point Bandit Feedback
mā: Ye, Haishan
I whakaputaina: (2026)

Efficient Algorithms for Logistic Contextual Slate Bandits with Bandit Feedback
mā: Goyal, Tanmay, me ētahi atu.
I whakaputaina: (2025)

Improved Online Confidence Bounds for Multinomial Logistic Bandits
mā: Lee, Joongkyu, me ētahi atu.
I whakaputaina: (2025)

Lipschitz Bandits with Stochastic Delayed Feedback
mā: Liu, Zhongxuan, me ētahi atu.
I whakaputaina: (2025)

Queueing Matching Bandits with Preference Feedback
mā: Kim, Jung-hun, me ētahi atu.
I whakaputaina: (2024)

Graph Feedback Bandits with Similar Arms
mā: Qi, Han, me ētahi atu.
I whakaputaina: (2024)

Nonparametric Kernel Clustering with Bandit Feedback
mā: Thuot, Victor, me ētahi atu.
I whakaputaina: (2026)

Cascading Bandits With Feedback
mā: Prakash, R Sri, me ētahi atu.
I whakaputaina: (2025)

Optimal Clustering with Bandit Feedback
mā: Yang, Junwen, me ētahi atu.
I whakaputaina: (2022)

Stochastic $k$-Submodular Bandits with Full Bandit Feedback
mā: Nie, Guanyu, me ētahi atu.
I whakaputaina: (2024)

Fixed Point Neural Acceleration and Inverse Surrogate Model for Battery Parameter Identification
mā: Cheon, Hojin, me ētahi atu.
I whakaputaina: (2025)

Multi-Agent Combinatorial-Multi-Armed-Bandit framework for the Submodular Welfare Problem under Bandit Feedback
mā: Pokhriyal, Subham, me ētahi atu.
I whakaputaina: (2026)