Multiple-Play Bandits in the Position-Based Model

Sequentially learning to place items in multi-position displays or lists is a task that can be cast into the multiple-play semi-bandit setting. However, a major concern in this context is when the system cannot decide whether the user feedback for each item is actually exploitable. Indeed, much of the content may have been simply ignored by the user. The present work proposes to exploit available information regarding the display position bias under the so-called Position-based click model (PBM). We first discuss how this model differs from the Cascade model and its variants considered in several recent works on multiple-play bandits. We then provide a novel regret lower bound for this model as well as computationally efficient algorithms that display good empirical and theoretical performance

Poisson process bandits:Sequential models and algorithms for maximising the detection of point data

In numerous settings in areas as diverse as security, ecology, astronomy, and logistics, it is desirable to optimally deploy a limited resource to observe events, which may be modelled as point data arising according to a Non-homogeneous Poisson process. Increasingly, thanks to developments in mobile and adaptive technologies, it is possible to update a deployment of such resource and gather feedback on the quality of multiple actions. Such a capability presents the opportunity to learn, and with it a classic problem in operations research and machine learning - the explorationexploitation dilemma. To perform optimally, how should investigative choices which explore the value of poorly understood actions and optimising choices which choose actions known to be of a high value be balanced? Effective techniques exist to resolve this dilemma in simpler settings, but the Poisson process data brings new challenges. In this thesis, effective solution methods for the problem of sequentially deploying resource are developed, via a combination of efficient inference schemes, bespoke optimisation approaches, and advanced sequential decision-making strategies. Furthermore, extensive theoretical work provides strong guarantees on the performance of the proposed solution methods and an understanding of the challenges of this problem and more complex extensions. In particular, Upper Confidence Bound and Thompson Sampling (TS) approaches are derived for combinatorial and continuum-armed bandit versions of the problem, with accompanying analysis displaying that the regret of the approaches is of optimal order. A broader understanding of the performance of TS based on non-parametric models for smooth reward functions is developed, and new posterior contraction results for the Gaussian Cox Process, a popular Bayesian non-parametric model of point data, are derived. These results point to effective strategies for more challenging variants of the event detection problem, and more generally advance the understanding of bandit decision-making with complex data structures

Finding the bandit in a graph: Sequential search-and-stop

We consider the problem where an agent wants to find a hidden object that is randomly located in some vertex of a directed acyclic graph (DAG) according to a fixed but possibly unknown distribution. The agent can only examine vertices whose in-neighbors have already been examined. In this paper, we address a learning setting where we allow the agent to stop before having found the object and restart searching on a new independent instance of the same problem. Our goal is to maximize the total number of hidden objects found given a time budget. The agent can thus skip an instance after realizing that it would spend too much time on it. Our contributions are both to the search theory and multi-armed bandits. If the distribution is known, we provide a quasi-optimal and efficient stationary strategy. If the distribution is unknown, we additionally show how to sequentially approximate it and, at the same time, act near-optimally in order to collect as many hidden objects as possible.Comment: in International Conference on Artificial Intelligence and Statistics (AISTATS 2019), April 2019, Naha, Okinawa, Japa

Adaptive policies for perimeter surveillance problems

We consider the problem of sequentially choosing observation regions along a line, with an aim of maximising the detection of events of interest. Such a problem may arise when monitoring the movements of endangered or migratory species, detecting crossings of a border, policing activities at sea, and in many other settings. In each case, the key operational challenge is to learn an allocation of surveillance resources which maximises successful detection of events of interest. We present a combinatorial multi-armed bandit model with Poisson rewards and a novel filtered feedback mechanism - arising from the failure to detect certain intrusions - where reward distributions are dependent on the actions selected. Our solution method is an upper confidence bound approach and we derive upper and lower bounds on its expected performance. We prove that the gap between these bounds is of constant order, and demonstrate empirically that our approach is more reliable in simulated problems than competing algorithms

Reinforcement Learning and Bandits for Speech and Language Processing: Tutorial, Review and Outlook

In recent years, reinforcement learning and bandits have transformed a wide range of real-world applications including healthcare, finance, recommendation systems, robotics, and last but not least, the speech and natural language processing. While most speech and language applications of reinforcement learning algorithms are centered around improving the training of deep neural networks with its flexible optimization properties, there are still many grounds to explore to utilize the benefits of reinforcement learning, such as its reward-driven adaptability, state representations, temporal structures and generalizability. In this survey, we present an overview of recent advancements of reinforcement learning and bandits, and discuss how they can be effectively employed to solve speech and natural language processing problems with models that are adaptive, interactive and scalable.Comment: To appear in Expert Systems with Applications. Accompanying INTERSPEECH 2022 Tutorial on the same topic. Including latest advancements in large language models (LLMs