Optimism in Active Learning

Collet, Timothé; Pietquin, Olivier

doi:10.1155/2015/973696

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…The use of MAB in Active Learning is not new and relatively well studied. A realtively common way to solve active learning with MAB is to cluster the instances on the pool and consider that each cluster is an arm [3,7]. In this case, the payoff distribution for each arm is non-stationary since the probability of finding relevant instances in a cluster decreases as the cluster is exploited.…”

Section: Related Workmentioning

confidence: 99%

Active Search for High Recall: A Non-stationary Extension of Thompson Sampling

Renders¹

2018

Lecture Notes in Computer Science

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We consider the problem of Active Search, where a maximum of relevant objects -ideally all relevant objects -should be retrieved with the minimum effort or minimum time. Solving this kind of problem is crucial in applications such as fraud detection, e-discovery, prior art search in patent databases, etc. Typically, there are two main challenges to face when tackling this problem: first, the class of relevant objects has often a very low prevalence and, secondly, this class can be multi-faceted or multi-modal: objects could be relevant for completely different reasons. To solve this problem and its associated issues, we propose an approach based on a non-stationary (aka restless) extension of Thompson Sampling, a well-known strategy for the Multi-Armed bandit problems. The collection is first soft-clustered into a finite set of components and a posterior distribution of getting a relevant object inside each cluster (or component) is updated after receiving the user feedback about the proposed instances. The"next instance" selection strategy is a mixed, two-level decision process, where the algorithm first selects a cluster through "optimistic Thompson sampling" and then chooses ,inside the cluster, the instance with maximal relevance probability, as computed by an incremental on-line classifier. In some way, this method should be considered as an insurance, where the cost of the insurance is an extra exploration effort in the short run (i.e. the early stage of the search process), for achieving a nearly "total" recall with less efforts in the long run.

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Section: Related Workmentioning

confidence: 99%

Active Search for High Recall: A Non-stationary Extension of Thompson Sampling

Renders¹

2018

Lecture Notes in Computer Science

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Agent-based model calibration using machine learning surrogates

Lamperti

Roventini

Sani

2018

Journal of Economic Dynamics and Control

182

117

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Taking agent-based models (ABM) closer to the data is an open challenge. This paper explicitly tackles parameter space exploration and calibration of ABMs combining supervised machine-learning and intelligent sampling to build a surrogate meta-model. The proposed approach provides a fast and accurate approximation of model behaviour, dramatically reducing computation time. In that, our machine-learning surrogate facilitates large scale explorations of the parameter-space, while providing a powerful filter to gain insights into the complex functioning of agent-based models. The algorithm introduced in this paper merges model simulation and output analysis into a surrogate meta-model, which substantially ease ABM calibration. We successfully apply our approach to the Brock and Hommes (1998) asset pricing model and to the "Island" endogenous growth model (Fagiolo and Dosi, 2003). Performance is evaluated against a relatively large outof-sample set of parameter combinations, while employing different user-defined statistical tests for output analysis. The results demonstrate the capacity of machine learning surrogates to facilitate fast and precise exploration of agent-based models' behaviour over their often rugged parameter spaces. KEY WORDSAgent based model; calibration; machine learning; surrogate; meta-model. JELC15, C52, C63.

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