Optimistic Planning for the Stochastic Knapsack Problem. - Ciara Pike-Burke; Steffen Grünewälder (AISTATS, 2017)

Bandits with Delayed Anonymous Feedback. - Ciara Pike-Burke; Shipra Agrawal 001; Csaba Szepesvári; Steffen Grünewälder (CoRR, 2017)

Bandits with Delayed, Aggregated Anonymous Feedback. - Ciara Pike-Burke; Shipra Agrawal 001; Csaba Szepesvári; Steffen Grünewälder (ICML, 2018)

Recovering Bandits. - Ciara Pike-Burke; Steffen Grünewälder (NeurIPS, 2019)

A Unifying View of Optimism in Episodic Reinforcement Learning. - Gergely Neu; Ciara Pike-Burke (NeurIPS, 2020)

Local Differentially Private Regret Minimization in Reinforcement Learning. - Evrard Garcelon; Vianney Perchet; Ciara Pike-Burke; Matteo Pirotta (CoRR, 2020)

Local Differential Privacy for Regret Minimization in Reinforcement Learning. - Evrard Garcelon; Vianney Perchet; Ciara Pike-Burke; Matteo Pirotta (NeurIPS, 2021)

Delayed Feedback in Episodic Reinforcement Learning. - Benjamin Howson; Ciara Pike-Burke; Sarah Filippi (CoRR, 2021)

Exact algorithms for the 0-1 Time-Bomb Knapsack Problem. - Michele Monaci; Ciara Pike-Burke; Alberto Santini (Comput. Oper. Res., 2022)

Optimism and Delays in Episodic Reinforcement Learning. - Benjamin Howson; Ciara Pike-Burke; Sarah Filippi (AISTATS, 2023)

Delayed Feedback in Generalised Linear Bandits Revisited. - Benjamin Howson; Ciara Pike-Burke; Sarah Filippi (AISTATS, 2023)

Trading-Off Payments and Accuracy in Online Classification with Paid Stochastic Experts. - Dirk van der Hoeven; Ciara Pike-Burke; Hao Qiu; Nicolò Cesa-Bianchi (ICML, 2023)

Delayed Feedback in Kernel Bandits. - Sattar Vakili; Danyal Ahmed; Alberto Bernacchia; Ciara Pike-Burke (ICML, 2023)

Optimal Convergence Rate for Exact Policy Mirror Descent in Discounted Markov Decision Processes. - Emmeran Johnson; Ciara Pike-Burke; Patrick Rebeschini (NeurIPS, 2023)

Sample Complexity of Goal-Conditioned Hierarchical Reinforcement Learning. - Arnaud Robert; Ciara Pike-Burke; Aldo A. Faisal (NeurIPS, 2023)

Bandit problems with fidelity rewards. - Gábor Lugosi; Ciara Pike-Burke; Pierre-André Savalle (J. Mach. Learn. Res., 2023)

Sample-Efficiency in Multi-Batch Reinforcement Learning: The Need for Dimension-Dependent Adaptivity. - Emmeran Johnson; Ciara Pike-Burke; Patrick Rebeschini (ICLR, 2024)

QuACK: A Multipurpose Queuing Algorithm for Cooperative k-Armed Bandits. - Benjamin Howson; Sarah Filippi; Ciara Pike-Burke (CoRR, 2024)

Efficient Exploitation of Hierarchical Structure in Sparse Reward Reinforcement Learning. - Gianluca Drappo; Arnaud Robert; Marcello Restelli; Aldo A. Faisal; Alberto Maria Metelli; Ciara Pike-Burke (AISTATS, 2025)

QuACK: A Multipurpose Queuing Algorithm for Cooperative k-Armed Bandits. - Benjamin Howson; Sarah Filippi; Ciara Pike-Burke (AISTATS, 2025)

Fixed-Budget Change Point Identification in Piecewise Constant Bandits. - Joseph Lazzaro; Ciara Pike-Burke (AISTATS, 2025)

When and why randomised exploration works (in linear bandits). - Marc Abeille; David Janz; Ciara Pike-Burke (ALT, 2025)

Learning Fair And Effective Points-Based Rewards Programs. - Chamsi Hssaine; Yichun Hu; Ciara Pike-Burke (EC, 2025)

Posterior Sampling for Reinforcement Learning on Graphs. - Arnaud Robert; Aldo A. Faisal; Ciara Pike-Burke (Trans. Mach. Learn. Res., 2025)

On the necessity of adaptive regularisation:Optimal anytime online learning on ℓp-balls. - Emmeran Johnson; David Martínez-Rubio; Ciara Pike-Burke; Patrick Rebeschini (CoRR, 2025)

Fixed-Confidence Multiple Change Point Identification under Bandit Feedback. - Joseph Lazzaro; Ciara Pike-Burke (CoRR, 2025)

Locally Differentially Private Thresholding Bandits. - Annalisa Barbara; Joseph Lazzaro; Ciara Pike-Burke (CoRR, 2025)