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Annals of Mathematics and Artificial Intelligence最新文献

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Integrating optimized item selection with active learning for continuous exploration in recommender systems 将优化项目选择与主动学习相结合，在推荐系统中进行持续探索
IF 1.2 4区 计算机科学
Annals of Mathematics and Artificial Intelligence Pub Date : 2024-04-05 DOI: 10.1007/s10472-024-09941-x
Serdar Kadıoğlu, Bernard Kleynhans, Xin Wang
{"title":"Integrating optimized item selection with active learning for continuous exploration in recommender systems","authors":"Serdar Kadıoğlu,&nbsp;Bernard Kleynhans,&nbsp;Xin Wang","doi":"10.1007/s10472-024-09941-x","DOIUrl":"10.1007/s10472-024-09941-x","url":null,"abstract":"<div><p>Recommender Systems have become the backbone of personalized services that provide tailored experiences to individual users, yet designing new recommendation applications with limited or no available training data remains a challenge. To address this issue, we focus on selecting the universe of items for experimentation in recommender systems by leveraging a recently introduced combinatorial problem. On the one hand, selecting a large set of items is desirable to increase the diversity of items. On the other hand, a smaller set of items enables rapid experimentation and minimizes the time and the amount of data required to train machine learning models. We first present how to optimize for such conflicting criteria using a multi-level optimization framework. Then, we shift our focus to the operational setting of a recommender system. In practice, to work effectively in a dynamic environment where new items are introduced to the system, we need to explore users’ behaviors and interests continuously. To that end, we show how to integrate the item selection approach with active learning to guide randomized exploration in an ongoing fashion. Our hybrid approach combines techniques from discrete optimization, unsupervised clustering, and latent text embeddings. Experimental results on well-known movie and book recommendation benchmarks demonstrate the benefits of optimized item selection and efficient exploration.</p></div>","PeriodicalId":7971,"journal":{"name":"Annals of Mathematics and Artificial Intelligence","volume":"92 6","pages":"1585 - 1607"},"PeriodicalIF":1.2,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140582298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Multi-resolution continuous normalizing flows 多分辨率连续归一化流程
IF 1.2 4区 计算机科学
Annals of Mathematics and Artificial Intelligence Pub Date : 2024-03-21 DOI: 10.1007/s10472-024-09939-5
Vikram Voleti, Chris Finlay, Adam Oberman, Christopher Pal
{"title":"Multi-resolution continuous normalizing flows","authors":"Vikram Voleti,&nbsp;Chris Finlay,&nbsp;Adam Oberman,&nbsp;Christopher Pal","doi":"10.1007/s10472-024-09939-5","DOIUrl":"10.1007/s10472-024-09939-5","url":null,"abstract":"<div><p>Recent work has shown that Neural Ordinary Differential Equations (ODEs) can serve as generative models of images using the perspective of Continuous Normalizing Flows (CNFs). Such models offer exact likelihood calculation, and invertible generation/density estimation. In this work we introduce a Multi-Resolution variant of such models (MRCNF), by characterizing the conditional distribution over the additional information required to generate a fine image that is consistent with the coarse image. We introduce a transformation between resolutions that allows for no change in the log likelihood. We show that this approach yields comparable likelihood values for various image datasets, with improved performance at higher resolutions, with fewer parameters, using only one GPU. Further, we examine the out-of-distribution properties of MRCNFs, and find that they are similar to those of other likelihood-based generative models.</p></div>","PeriodicalId":7971,"journal":{"name":"Annals of Mathematics and Artificial Intelligence","volume":"92 5","pages":"1295 - 1317"},"PeriodicalIF":1.2,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140203649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Clustering, coding, and the concept of similarity 聚类、编码和相似性概念
IF 1.2 4区 计算机科学
Annals of Mathematics and Artificial Intelligence Pub Date : 2024-03-19 DOI: 10.1007/s10472-024-09929-7
L. Thorne McCarty
{"title":"Clustering, coding, and the concept of similarity","authors":"L. Thorne McCarty","doi":"10.1007/s10472-024-09929-7","DOIUrl":"10.1007/s10472-024-09929-7","url":null,"abstract":"<div><p>This paper develops a theory of <i>clustering</i> and <i>coding</i> that combines a geometric model with a probabilistic model in a principled way. The geometric model is a Riemannian manifold with a Riemannian metric, <span>({g}_{ij}(textbf{x}))</span>, which we interpret as a measure of <i>dissimilarity</i>. The probabilistic model consists of a stochastic process with an invariant probability measure that matches the density of the sample input data. The link between the two models is a potential function, <span>(U(textbf{x}))</span>, and its gradient, <span>(nabla U(textbf{x}))</span>. We use the gradient to define the dissimilarity metric, which guarantees that our measure of dissimilarity will depend on the probability measure. Finally, we use the dissimilarity metric to define a coordinate system on the embedded Riemannian manifold, which gives us a low-dimensional encoding of our original data.</p></div>","PeriodicalId":7971,"journal":{"name":"Annals of Mathematics and Artificial Intelligence","volume":"92 5","pages":"1197 - 1248"},"PeriodicalIF":1.2,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140172695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Coalition formation games – preface 联盟组建游戏--序言
IF 1.2 4区 计算机科学
Annals of Mathematics and Artificial Intelligence Pub Date : 2024-03-07 DOI: 10.1007/s10472-024-09937-7
Judy Goldsmith, Jörg Rothe
{"title":"Coalition formation games – preface","authors":"Judy Goldsmith,&nbsp;Jörg Rothe","doi":"10.1007/s10472-024-09937-7","DOIUrl":"10.1007/s10472-024-09937-7","url":null,"abstract":"","PeriodicalId":7971,"journal":{"name":"Annals of Mathematics and Artificial Intelligence","volume":"92 3","pages":"535 - 537"},"PeriodicalIF":1.2,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10472-024-09937-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140076852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A new definition for feature selection stability analysis 特征选择稳定性分析的新定义
IF 1.2 4区 计算机科学
Annals of Mathematics and Artificial Intelligence Pub Date : 2024-03-01 DOI: 10.1007/s10472-024-09936-8
Teddy Lazebnik, Avi Rosenfeld
{"title":"A new definition for feature selection stability analysis","authors":"Teddy Lazebnik,&nbsp;Avi Rosenfeld","doi":"10.1007/s10472-024-09936-8","DOIUrl":"10.1007/s10472-024-09936-8","url":null,"abstract":"<div><p>Feature selection (FS) stability is an important topic of recent interest. Finding stable features is important for creating reliable, non-overfitted feature sets, which in turn can be used to generate machine learning models with better accuracy and explanations and are less prone to adversarial attacks. There are currently several definitions of FS stability that are widely used. In this paper, we demonstrate that existing stability metrics fail to quantify certain key elements of many datasets such as resilience to data drift or non-uniformly distributed missing values. To address this shortcoming, we propose a new definition for FS stability inspired by Lyapunov stability in dynamic systems. We show the proposed definition is statistically different from the classical <i>record-stability</i> on (<span>(n=90)</span>) datasets. We present the advantages and disadvantages of using Lyapunov and other stability definitions and demonstrate three scenarios in which each one of the three proposed stability metrics is best suited.</p></div>","PeriodicalId":7971,"journal":{"name":"Annals of Mathematics and Artificial Intelligence","volume":"92 3","pages":"753 - 770"},"PeriodicalIF":1.2,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10472-024-09936-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140017398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Correction: Personalized choice prediction with less user information 更正：利用较少的用户信息进行个性化选择预测
IF 1.2 4区 计算机科学
Annals of Mathematics and Artificial Intelligence Pub Date : 2024-02-20 DOI: 10.1007/s10472-024-09934-w
Francine Chen, Yanxia Zhang, Minh Nguyen, Matt Klenk, Charlene Wu
{"title":"Correction: Personalized choice prediction with less user information","authors":"Francine Chen,&nbsp;Yanxia Zhang,&nbsp;Minh Nguyen,&nbsp;Matt Klenk,&nbsp;Charlene Wu","doi":"10.1007/s10472-024-09934-w","DOIUrl":"10.1007/s10472-024-09934-w","url":null,"abstract":"","PeriodicalId":7971,"journal":{"name":"Annals of Mathematics and Artificial Intelligence","volume":"92 3","pages":"771 - 771"},"PeriodicalIF":1.2,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10472-024-09934-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142412597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Sequential composition of propositional logic programs 命题逻辑程序的顺序组合
IF 1.2 4区 计算机科学
Annals of Mathematics and Artificial Intelligence Pub Date : 2024-02-15 DOI: 10.1007/s10472-024-09925-x
Christian Antić
{"title":"Sequential composition of propositional logic programs","authors":"Christian Antić","doi":"10.1007/s10472-024-09925-x","DOIUrl":"10.1007/s10472-024-09925-x","url":null,"abstract":"<div><p>This paper introduces and studies the sequential composition and decomposition of propositional logic programs. We show that acyclic programs can be decomposed into single-rule programs and provide a general decomposition result for arbitrary programs. We show that the immediate consequence operator of a program can be represented via composition which allows us to compute its least model without any explicit reference to operators. This bridges the conceptual gap between the syntax and semantics of a propositional logic program in a mathematically satisfactory way.</p></div>","PeriodicalId":7971,"journal":{"name":"Annals of Mathematics and Artificial Intelligence","volume":"92 2","pages":"505 - 533"},"PeriodicalIF":1.2,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10472-024-09925-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139773591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Analogical proportions in monounary algebras 单名代数中的类比比例
IF 1.2 4区 计算机科学
Annals of Mathematics and Artificial Intelligence Pub Date : 2024-02-10 DOI: 10.1007/s10472-023-09921-7
Christian Antić
{"title":"Analogical proportions in monounary algebras","authors":"Christian Antić","doi":"10.1007/s10472-023-09921-7","DOIUrl":"10.1007/s10472-023-09921-7","url":null,"abstract":"<div><p>This paper studies analogical proportions in monounary algebras consisting only of a universe and a single unary function, where we analyze the role of congruences, and we show that the analogical proportion relation is characterized in the infinite monounary algebra formed by the natural numbers together with the successor function via difference proportions.</p></div>","PeriodicalId":7971,"journal":{"name":"Annals of Mathematics and Artificial Intelligence","volume":"92 6","pages":"1663 - 1677"},"PeriodicalIF":1.2,"publicationDate":"2024-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10472-023-09921-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139767183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A category theory approach to the semiotics of machine learning 机器学习符号学的范畴理论方法
IF 1.2 4区 计算机科学
Annals of Mathematics and Artificial Intelligence Pub Date : 2024-02-09 DOI: 10.1007/s10472-024-09932-y
Fernando Tohmé, Rocco Gangle, Gianluca Caterina
{"title":"A category theory approach to the semiotics of machine learning","authors":"Fernando Tohmé,&nbsp;Rocco Gangle,&nbsp;Gianluca Caterina","doi":"10.1007/s10472-024-09932-y","DOIUrl":"10.1007/s10472-024-09932-y","url":null,"abstract":"<div><p>The successes of Machine Learning, and in particular of Deep Learning systems, have led to a reformulation of the Artificial Intelligence agenda. One of the pressing issues in the field is the extraction of knowledge out of the behavior of those systems. In this paper we propose a semiotic analysis of that behavior, based on the formal model of <i>learners</i>. We analyze the topos-theoretic properties that ensure the logical expressivity of the knowledge embodied by learners. Furthermore, we show that there exists an ideal <i>universal learner</i>, able to interpret the knowledge gained about any possible function as well as about itself, which can be monotonically approximated by networks of increasing size.</p></div>","PeriodicalId":7971,"journal":{"name":"Annals of Mathematics and Artificial Intelligence","volume":"92 3","pages":"733 - 751"},"PeriodicalIF":1.2,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139767115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Bounds on depth of decision trees derived from decision rule systems with discrete attributes 从具有离散属性的决策规则系统得出的决策树深度的界限
IF 1.2 4区 计算机科学
Annals of Mathematics and Artificial Intelligence Pub Date : 2024-02-08 DOI: 10.1007/s10472-024-09933-x
Kerven Durdymyradov, Mikhail Moshkov
{"title":"Bounds on depth of decision trees derived from decision rule systems with discrete attributes","authors":"Kerven Durdymyradov,&nbsp;Mikhail Moshkov","doi":"10.1007/s10472-024-09933-x","DOIUrl":"10.1007/s10472-024-09933-x","url":null,"abstract":"<div><p>Systems of decision rules and decision trees are widely used as a means for knowledge representation, as classifiers, and as algorithms. They are among the most interpretable models for classifying and representing knowledge. The study of relationships between these two models is an important task of computer science. It is easy to transform a decision tree into a decision rule system. The inverse transformation is a more difficult task. In this paper, we study unimprovable upper and lower bounds on the minimum depth of decision trees derived from decision rule systems with discrete attributes depending on the various parameters of these systems. To illustrate the process of transformation of decision rule systems into decision trees, we generalize well known result for Boolean functions to the case of functions of <i>k</i>-valued logic.</p></div>","PeriodicalId":7971,"journal":{"name":"Annals of Mathematics and Artificial Intelligence","volume":"92 3","pages":"703 - 732"},"PeriodicalIF":1.2,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139767185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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