56 citations · 65 across the 18 of their papers we have counts for
20 papers
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…
Fixed-Confidence Multiple Change Point Identification under Bandit Feedback
Joseph Lazzaro, Ciara Pike-Burke
Piecewise constant functions describe a variety of real-world phenomena in domains ranging from chemistry to manufacturing. In practice, it is often required to confidently identif…
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…
Fixed-Budget Change Point Identification in Piecewise Constant Bandits
Joseph Lazzaro, Ciara Pike-Burke
We study the piecewise constant bandit problem where the expected reward is a piecewise constant function with one change point (discontinuity) across the action space and…
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…
Trading-Off Payments and Accuracy in Online Classification with Paid Stochastic Experts
Dirk van der Hoeven, Ciara Pike-Burke, Hao Qiu +1
We investigate online classification with paid stochastic experts. Here, before making their prediction, each expert must be paid. The amount that we pay each expert directly influ…
Optimal Convergence Rate for Exact Policy Mirror Descent in Discounted Markov Decision Processes
Emmeran Johnson, Ciara Pike-Burke, Patrick Rebeschini
Policy Mirror Descent (PMD) is a general family of algorithms that covers a wide range of novel and fundamental methods in reinforcement learning. Motivated by the instability of p…
Delayed Feedback in Kernel Bandits
Sattar Vakili, Danyal Ahmed, Alberto Bernacchia +1
Black box optimisation of an unknown function from expensive and noisy evaluations is a ubiquitous problem in machine learning, academic research and industrial production. An abst…
Active Learning for Quantum Mechanical Measurements
Ruidi Zhu, Ciara Pike-Burke, Florian Mintert
The experimental evaluation of many quantum mechanical quantities requires the estimation of several directly measurable observables, such as local observables. Due to the necessit…
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…
Bandit problems with fidelity rewards
Gábor Lugosi, Ciara Pike-Burke, Pierre-André Savalle
The fidelity bandits problem is a variant of the -armed bandit problem in which the reward of each arm is augmented by a fidelity reward that provides the player with an additio…
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…
Recovering Bandits
Ciara Pike-Burke, Steffen Grünewälder
We study the recovering bandits problem, a variant of the stochastic multi-armed bandit problem where the expected reward of each arm varies according to some unknown function of t…
Bandits with Delayed, Aggregated Anonymous Feedback
Ciara Pike-Burke, Shipra Agrawal, Csaba Szepesvari +1
We study a variant of the stochastic -armed bandit problem, which we call "bandits with delayed, aggregated anonymous feedback". In this problem, when the player pulls an arm, a…