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ALORS: An algorithm recommender system

ARTIFICIAL INTELLIGENCE 2017年 244卷 291-314页

作者： Misir, Mustafa Sebag, Michele Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol 29 Jiangiun Ave Nanjing 211106 Jiangsu Peoples R China Univ Paris Sud Univ Paris Saclay CNRS INRIA LRI TAO Shannon Bldg 660 F-91405 Orsay France

algorithm selection (AS), selecting the algorithm best suited for a particular problem instance, is acknowledged to be a key issue to make the best out of algorithm portfolios. This paper presents a collaborative filtering approach to AS. Collaborative filtering, popularized by the Netflix challenge, aims to recommend the items that a user will most probably like, based on the previous items she liked, and the items that have been liked by other users. As first noted by Stern et al. [47], algorithm selection can be formalized as a collaborative filtering problem, by considering that a problem instance "likes better" the algorithms that achieve better performance on this particular instance. Two merits of collaborative filtering (CF) compared to the mainstream algorithm selection (AS) approaches are the following. Firstly, mainstream AS requires extensive and computationally expensive experiments to learn a performance model, with all algorithms launched on all problem instances, whereas CF can exploit a sparse matrix, with a few algorithms launched on each problem instance. Secondly, AS learns a performance model as a function of the initial instance representation, whereas CF builds latent factors to describe algorithms and instances, and uses the associated latent metrics to recommend algorithms for a specific problem instance. A main contribution of the proposed algorithm recommender ALORS system is to handle the cold start problem emitting recommendations for a new problem instance - through the non-linear modeling of the latent factors based on the initial instance representation, extending the linear approach proposed by Stern et al. [47]. The merits and generality of ALORS are empirically demonstrated on the ASLib [6] and OpenML [53] benchmarks. (C) 2016 Elsevier B.V. All rights reserved.

关键词： algorithm selection Constraint satisfaction Constraint programming Collaborative filtering Meta-learning

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Development of quantitative structure activity relationship (QSAR) model for disinfection byproduct (DBP) research: A review of methods and resources

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JOURNAL OF HAZARDOUS MATERIALS 2015年 299卷 260-279页

作者： Chen, Baiyang Zhang, Tian Bond, Tom Gan, Yiqun Harbin Inst Technol Shenzhen Grad Sch Shenzhen Key Lab Water Resource Utilizat & Enviro Shenzhen 518055 Peoples R China Univ London Imperial Coll Sci Technol & Med Dept Civil & Environm Engn London SW7 2AZ England

Quantitative structure-activity relationship (QSAR) models are tools for linking chemical activities with molecular structures and compositions. Due to the concern about the proliferating number of disinfection byproducts (DBPs) in water and the associated financial and technical burden, researchers have recently begun to develop QSAR models to investigate the toxicity, formation, property, and removal of DBPs. However, there are no standard procedures or best practices regarding how to develop QSAR models, which potentially limit their wide acceptance. In order to facilitate more frequent use of QSAR models in future DBP research, this article reviews the processes required for QSAR model development, summarizes recent trends in QSAR-DBP studies, and shares some important resources for QSAR development (e.g., free databases and QSAR programs). The paper follows the four steps of QSAR model development, i.e., data collection, descriptor filtration, algorithm selection, and model validation;and finishes by highlighting several research needs. Because QSAR models may have an important role in progressing our understanding of DBP issues, it is hoped that this paper will encourage their future use for this application. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Disinfection byproduct QSAR/QSPR/QSTR/LFER Descriptor filtration algorithm selection Model validation

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Eight years of AutoML: categorisation, review and trends

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KNOWLEDGE AND INFORMATION SYSTEMS 2023年第12期65卷 5097-5149页

作者： Barbudo, Rafael Ventura, Sebastian Romero, Jose Raul Univ Cordoba Dept Comp Sci & Numer Anal Cordoba 14071 Spain Andalusian Res Inst Data Sci & Computat Intelligen Cordoba Spain

Knowledge extraction through machine learning techniques has been successfully applied in a large number of application domains. However, apart from the required technical knowledge and background in the application domain, it usually involves a number of time-consuming and repetitive steps. Automated machine learning (AutoML) emerged in 2014 as an attempt to mitigate these issues, making machine learning methods more practicable to both data scientists and domain experts. AutoML is a broad area encompassing a wide range of approaches aimed at addressing a diversity of tasks over the different phases of the knowledge discovery process being automated with specific techniques. To provide a big picture of the whole area, we have conducted a systematic literature review based on a proposed taxonomy that permits categorising 447 primary studies selected from a search of 31,048 papers. This review performs an extensive and rigorous analysis of the AutoML field, scrutinising how the primary studies have addressed the dimensions of the taxonomy, and identifying any gaps that remain unexplored as well as potential future trends. The analysis of these studies has yielded some intriguing findings. For instance, we have observed a significant growth in the number of publications since 2018. Additionally, it is noteworthy that the algorithm selection problem has gradually been superseded by the challenge of workflow composition, which automates more than one phase of the knowledge discovery process simultaneously. Of all the tasks in AutoML, the growth of neural architecture search is particularly noticeable.

关键词： AutoML algorithm selection Hyper-parameter optimisation Neural architecture search Workflow composition Systematic literature review

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Combining Uncertainty Sampling methods for supporting the generation of Meta-examples

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INFORMATION SCIENCES 2012年 196卷 1-14页

作者： Prudencio, Ricardo B. C. Ludermir, Teresa B. Univ Fed Pernambuco Ctr Informat Recife PE Brazil

Meta-Learning aims to automatically acquire knowledge relating features of learning problems to the performance of learning algorithms. Each training example in Meta-Learning (i.e. each meta-example) stores features of a learning problem plus the performance obtained by a set of algorithms when evaluated on the problem. Based on a set of meta-examples, a Meta-Learner will be used to predict algorithm performance for new problems. The generation of a good set of meta-examples can be a costly process, since for each problem it is necessary to perform an empirical evaluation of the algorithms. In a previous work, we proposed the Active Meta-Learning, in which Active Learning was used to reduce the set of meta-examples by selecting only the most relevant problems for meta-example generation. In the current work, we extend our previous research by combining different Uncertainty Sampling methods for Active Meta-Learning, considering that each individual method will provide useful information to select relevant problems. We also investigated the use of Outlier Detection to remove a priori those problems considered as outliers, aiming to improve the performance of the sampling methods. In our experiments, we observed a gain in Meta-Learning performance when the proposed combining method was compared to the individual active methods being combined and also when outliers were removed from the set of problems available for meta-example generation. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Meta-Learning algorithm selection Active Learning Uncertainty Sampling Outlier Detection

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Statistical models for empirical search-based performance tuning

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INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2004年第1期18卷 65-94页

作者： Vuduc, R Demmel, JW Bilmes, JA Univ Calif Berkeley Dept Elect Engn & Comp Sci Div Comp Sci Berkeley CA 94720 USA Univ Calif Berkeley Dept Math Berkeley CA 94720 USA Univ Washington Dept Elect Engn Seattle WA 98195 USA

Achieving peak performance from the computational kernels that dominate application performance often requires extensive machine-dependent tuning by hand. Automatic tuning systems have emerged in response, and they typically operate by (1) generating a large number of possible, reasonable implementations of a kernel, and (2) selecting the fastest implementation by a combination of heuristic modeling, heuristic pruning, and empirical search (i.e. actually running the code). This paper presents quantitative data that motivate the development of such a search-based system, using dense matrix multiply as a case study. The statistical distributions of performance within spaces of reasonable implementations, when observed on a variety of hardware platforms, lead us to pose and address two general problems which arise during the search process. First, we develop a heuristic for stopping an exhaustive compile-time search early if a near-optimal implementation is found. Secondly, we show how to construct run-time decision rules, based on run-time inputs, for selecting from among a subset of the best implementations when the space of inputs can be described by continuously varying features. We address both problems by using statistical modeling techniques that exploit the large amount of performance data collected during the search. We demonstrate these methods on actual performance data collected by the PHiPAC tuning system for dense matrix multiply. We close with a survey of recent projects that use or otherwise advocate an empirical search-based approach to code generation and algorithm selection, whether at the level of computational kernels, compiler and run-time systems, or problem-solving environments. Collectively, these efforts suggest a number of possible software architectures for constructing platform-adapted libraries and applications.

关键词： algorithm selection automatic performance tuning early stopping feedback-directed optimization matrix multiplication performance distribution performance optimization software engineering support vector method

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ML-Plan: Automated machine learning via hierarchical planning

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MACHINE LEARNING 2018年第8-10期107卷 1495-1515页

作者： Mohr, Felix Wever, Marcel Huellermeier, Eyke Paderborn Univ Warburger Str 100 D-33098 Paderborn Germany

Automated machine learning (AutoML) seeks to automatically select, compose, and parametrize machine learning algorithms, so as to achieve optimal performance on a given task (dataset). Although current approaches to AutoML have already produced impressive results, the field is still far from mature, and new techniques are still being developed. In this paper, we present ML-Plan, a new approach to AutoML based on hierarchical planning. To highlight the potential of this approach, we compare ML-Plan to the state-of-the-art frameworks Auto-WEKA, auto-sklearn, and TPOT. In an extensive series of experiments, we show that ML-Plan is highly competitive and often outperforms existing approaches.

关键词： Automated machine learning Automated planning algorithm selection algorithm configuration Heuristic search

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DASH: Dynamic Approach for Switching Heuristics

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2016年第3期248卷 943-953页

作者： Di Liberto, Giovanni Kadioglu, Serdar Leo, Kevin Malitsky, Yuri Univ Coll Cork Cork Constraint Computat Ctr Cork Ireland Oracle Corp Burlington MA 01803 USA Natl ICT Australia NICTA Victoria Lab West Melbourne Vic Australia

Complete tree search is a highly effective method for tackling Mixed-Integer Programming (MIP) problems, and over the years, a plethora of branching heuristics have been introduced to further refine the technique for varying problems. Yet while each new approach continued to push the state-of-the-art, parallel research began to repeatedly demonstrate that there is no single method that would perform the best on all problem instances. Tackling this issue, portfolio algorithms took the process a step further, by trying to predict the best heuristic for each instance at hand. However, the motivation behind algorithm selection can be taken further still, and used to dynamically choose the most appropriate algorithm for each encountered sub-problem. In this paper we identify a feature space that captures both the evolution of the problem in the branching tree and the similarity among sub-problems of instances from the same MIP models. We show how to exploit these features on-the-fly in order to decide the best time to switch the branching variable selection heuristic and then show how such a system can be trained efficiently. Experiments on a highly heterogeneous collection of hard MIP instances show significant gains over the standard pure approach which commits to a single heuristic throughout the search. (C) 2015 Elsevier B.V. and Association of European Operational Research Societies (EURO) within the International Federation of Operational Research Societies (IFORS). All rights reserved.

关键词： Mixed-Integer Programming algorithm selection Dynamic search heuristics

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The ELAPS framework: Experimental Linear Algebra Performance Studies

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INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2019年第2期33卷 353-365页

作者： Peise, Elmar Bientinesi, Paolo Rhein Westfal TH Aachen AICES Schinkelstr 2 D-52062 Aachen Germany

In scientific computing, optimal use of computing resources comes at the cost of extensive coding, tuning, and benchmarking. While the classic approach of "features first, performance later" is supported by a variety of tools such as Tau, Vampir, and Scalasca, the emerging performance-centric approach, in which both features and performance are primary objectives, is still lacking suitable development tools. For dense linear algebra applications, we fill this gap with the Experimental Linear Algebra Performance Studies (ELAPS) framework, a multi-platform open-source environment for easy, fast, and yet powerful performance experimentation and prototyping. In contrast to many existing tools, ELAPS targets the beginning of the development process, assisting application developers in both algorithmic and optimization decisions. With ELAPS, users construct experiments to investigate how performance and efficiency depend on factors such as caching, algorithmic parameters, problem size, and parallelism. Experiments are designed either through Python scripts or a specialized Graphical User Interface (GUI), and run on a spectrum of architectures, ranging from laptops to accelerators and clusters. The resulting reports provide various metrics and statistics that can be analyzed both numerically and visually. In this article, we introduce ELAPS and illustrate its practical value in guiding critical performance decisions already in early development stages.

关键词： Performance dense linear algebra experimentation algorithm selection performance prediction

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A sequential algorithm portfolio approach for black box optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2019年 44卷 559-570页

作者： He, Yaodong Yuen, Shiu Yin Lou, Yang Zhang, Xin City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Tianjin Normal Univ Coll Elect & Commun Engn Tianjin Peoples R China

A large number of optimization algorithms have been proposed. However, the no free lunch (NFL) theorems inform us that no algorithm can solve all types of optimization problems. An approach, which can suggest the most suitable algorithm for different types of problems, is valuable. In this paper, we propose an approach called sequential algorithm portfolio (SAP) which belongs to the inter-disciplinary fields of algorithm portfolio and algorithm selection. It uses a pre-trained predictor to predict the most suitable algorithm and a termination mechanism to automatically stop the optimization algorithms. The SAP is easy to implement and can incorporate any optimization algorithm. We experimentally compare SAP with two state-of-the-art algorithm portfolio approaches and single optimization algorithms. The result shows that SAP is a well-performing algorithm portfolio approach.

关键词： algorithm portfolio algorithm selection Heuristic algorithms Optimization problems Performance prediction

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Monte Carlo Search algorithm Discovery for Single-Player Games

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IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES 2013年第3期5卷 201-213页

作者： Maes, Francis St-Pierre, David Lupien Ernst, Damien Univ Liege Syst & Modeling Res Unit B-4000 Liege Belgium Univ Liege Dept Elect Engn & Comp Sci B-4000 Liege Belgium

Much current research in AI and games is being devoted to Monte Carlo search (MCS) algorithms. While the quest for a single unified MCS algorithm that would perform well on all problems is of major interest for AI, practitioners often know in advance the problem they want to solve, and spend plenty of time exploiting this knowledge to customize their MCS algorithm in a problem-driven way. We propose an MCS algorithm discovery scheme to perform this in an automatic and reproducible way. First, we introduce a grammar over MCS algorithms that enables inducing a rich space of candidate algorithms. Afterwards, we search in this space for the algorithm that performs best on average for a given distribution of training problems. We rely on multiarmed bandits to approximately solve this optimization problem. The experiments, generated on three different domains, show that our approach enables discovering algorithms that outperform several well-known MCS algorithms such as upper confidence bounds applied to trees and nested Monte Carlo search. We also show that the discovered algorithms are generally quite robust with respect to changes in the distribution over the training problems.

关键词： algorithm selection grammar of algorithms Monte Carlo search (MCS)

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