56 citations · 65 across the 18 of their papers we have counts for
11 papers · 1 filter
Stochastic Shortest Path with Sparse Adversarial Costs
Emmeran Johnson, Alberto Rumi, Ciara Pike-Burke +1
We study the adversarial Stochastic Shortest Path (SSP) problem with sparse costs under full-information feedback. In the known transition setting, existing bounds based on Online…
Locally Differentially Private Thresholding Bandits
Annalisa Barbara, Joseph Lazzaro, Ciara Pike-Burke
This work investigates the impact of ensuring local differential privacy in the thresholding bandit problem. We consider both the fixed budget and fixed confidence settings. We pro…
On The Limited Cost of Consistency In Points-Based Rewards Programs
Chamsi Hssaine, Yichun Hu, Ciara Pike-Burke
Points-based rewards programs are a prevalent way to incentivize customer loyalty. In theory, the effective design of these programs is shaped by two important considerations. Cust…
On the necessity of adaptive regularisation:Optimal anytime online learning on -balls
Emmeran Johnson, David Martínez-Rubio, Ciara Pike-Burke +1
We study online convex optimization on -balls in for . While always sub-linear, the optimal regret exhibits a shift between the high-dimensional setti…
When and why randomised exploration works (in linear bandits)
Marc Abeille, David Janz, Ciara Pike-Burke
We provide an approach for the analysis of randomised exploration algorithms like Thompson sampling that does not rely on forced optimism or posterior inflation. With this, we demo…
QuACK: A Multipurpose Queuing Algorithm for Cooperative -Armed Bandits
Benjamin Howson, Sarah Filippi, Ciara Pike-Burke
We study the cooperative stochastic -armed bandit problem, where a network of agents collaborate to find the optimal action. In contrast to most prior work on this problem,…
Sample-Efficiency in Multi-Batch Reinforcement Learning: The Need for Dimension-Dependent Adaptivity
Emmeran Johnson, Ciara Pike-Burke, Patrick Rebeschini
We theoretically explore the relationship between sample-efficiency and adaptivity in reinforcement learning. An algorithm is sample-efficient if it uses a number of queries to…
Delayed Feedback in Generalised Linear Bandits Revisited
Benjamin Howson, Ciara Pike-Burke, Sarah Filippi
The stochastic generalised linear bandit is a well-understood model for sequential decision-making problems, with many algorithms achieving near-optimal regret guarantees under imm…
Optimism and Delays in Episodic Reinforcement Learning
Benjamin Howson, Ciara Pike-Burke, Sarah Filippi
There are many algorithms for regret minimisation in episodic reinforcement learning. This problem is well-understood from a theoretical perspective, providing that the sequences o…
Local Differential Privacy for Regret Minimization in Reinforcement Learning
Evrard Garcelon, Vianney Perchet, Ciara Pike-Burke +1
Reinforcement learning algorithms are widely used in domains where it is desirable to provide a personalized service. In these domains it is common that user data contains sensitiv…
A Unifying View of Optimism in Episodic Reinforcement Learning
Gergely Neu, Ciara Pike-Burke
The principle of optimism in the face of uncertainty underpins many theoretically successful reinforcement learning algorithms. In this paper we provide a general framework for des…