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Adaptive Decision-Making in Audio Classification: A Systematic Review of Reinforcement Learning and Multi-Armed Bandit Frameworks

Machine Learning and Knowledge Extraction
Adaptive decision-making has emerged as an important direction in audio classification. Reinforcement learning (RL) and multi-armed bandit (MAB) methods offer principled frameworks for sequential and localized decision-making.
Geofrey Owino, Timothy Kamanu, John Ndiritu   +2 more
doaj   +1 more source

Multi-Armed Bandits with Network Interference

CoRR
Online experimentation with interference is a common challenge in modern applications such as e-commerce and adaptive clinical trials in medicine. For example, in online marketplaces, the revenue of a good depends on discounts applied to competing goods.
Abhineet Agarwal, Anish Agarwal, Lorenzo Masoero, Justin Whitehouse   +3 more
openaire   +3 more sources

Keeping Your Options Open [PDF]

In standard models of experimentation, the costs of project development consist of (i) the direct cost of running trials as well as (ii) the implicit opportunity cost of leaving alternative projects idle. Another natural type of experimentation cost, the
Jean Guillaume Forand
core  

Optimal Query Selection Using Multi-Armed Bandits. [PDF]

IEEE Signal Process Lett, 2018
Koçanaoğulları A, Marghi YM, Akçakaya M, Erdoğmuş D.   +3 more
europepmc   +1 more source

Minimising the time to a decision [PDF]

, 2011
Suppose we have three independent copies of a regular diffusion on [0,1] with absorbing boundaries. Of these diffusions, either at least two are absorbed at the upper boundary or at least two at the lower boundary.
Warren, Jon, Jacka, Saul D., Windridge, Peter   +2 more
core  

Crowdsourcing complex workflows under budget constraints

, 2015
We consider the problem of task allocation in crowdsourcing systems with multiple complex workflows, each of which consists of a set of interdependent micro-tasks. We propose Budgeteer, an algorithm to solve this problem under a budget constraint.
Huynh, Trung Dong, Ramchurn, Sarvapali D., Tran-Thanh, Long, Rosenfeld, Avi, Jennings, Nicholas R.   +4 more
core  

Transfer Learning for Contextual Multi-armed Bandits [PDF]

Motivated by a range of applications, we study in this paper the problem of transfer learning for nonparametric contextual multi-armed bandits under the covariate shift model, where we have data collected on source bandits before the start of the target ...
Li, Hongzhe, Cai, Changxiao, Cai, T. Tony   +2 more
core   +1 more source

Adversarial Attacks on Combinatorial Multi-Armed Bandits [PDF]

We study reward poisoning attacks on Combinatorial Multi-armed Bandits (CMAB). We first provide a sufficient and necessary condition for the attackability of CMAB, a notion to capture the vulnerability and robustness of CMAB.
Tadepalli, Prasad, Balasubramanian, Rishab, Wang, Huazheng, Wu, Qingyun, Li, Jiawei, Zhao, Haoyu   +5 more
core   +1 more source

Optimizing Infill Drilling Decisions Using Multi-Armed Bandits: Application in a Long-Term, Multi-Element Stockpile. [PDF]

Math Geosci, 2018
Dirkx R, Dimitrakopoulos R.
europepmc   +1 more source

Restless bandits, partial conservation laws and indexability [PDF]

We show that if performance measures in a stochastic scheduling problem satisfy a set of so-called partial conservation laws (PCL), which extend previously studied generalized conservation laws (GCL), then the problem is solved optimally by a priority ...
José Niño-Mora
core