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Augmented Two-Stage Bandit Framework: Practical Approaches for Improved online Ad Selection

Augmented Two-Stage Bandit Framework: Practical Approaches f...

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2024 workshop on Data Mining for online Advertising, AdKDD 2024

作者： Han, Seowon Lakritz, Ryan Wu, Hanxiao Reddit Inc. 303 2nd Street South Tower Floor 5 San FranciscoCA94107 United States

In online advertising, maximizing user engagement and advertiser performance hinges on effective ad selection algorithms. algorithms that tackle Multi-armed bandit problems, such as Thompson Sampling, excel in exploration, but their utilization of contextual information remains limited. Conversely, contextual bandit approaches personalize ad selection by leveraging user and ad-specific features. However, they perform poorly in contexts with limited data and often encounter cold start problems for new ad groups. To address this dilemma, we propose a novel bandit framework that combines context-free and context-aware rewards and is augmented with historical predicted performance, for which we use predicted click-through rate (pCTR) scores. We will refer to this bandit framework as the Augmented Two-Stage Bandit Framework. Our bandit framework is comprised of two stages. In the first stage, the framework applies context-free Thompson Sampling augmented by historical pCTR scores for initial exploration. The non-contextual bandit algorithm and generalized patterns recognized by our pCTR model allow for effective mitigation of the cold start problem. In the second stage, the framework shifts to a contextual bandit algorithm for refined exploration and exploitation. We demonstrate the efficacy of our proposed method using extensive simulation and experiments conducted on a real-world ads marketplace at Reddit. Compared to traditional bandit algorithms, our historical pCTR augmented Two-Stage Bandit framework achieves significant improvements in click-through rate. These findings underscore the ability of an Augmented Two-Stage Bandit Framework to enhance online ad selection and improve key performance metrics. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Deep neural networks

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Improved approximation algorithms for Multiprocessor Scheduling with Testing 15th

Improved Approximation Algorithms for Multiprocessor Schedul...

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15th international Frontiers of Algorithmics workshop (FAW) / Game Theory in Blockchain Track Conference / 2nd international Joint Conference on Theoretical Computer Science (IJTCS)

作者： Gong, Mingyang Lin, Guohui Univ Alberta Dept Comp Sci Edmonton AB T6G 2E8 Canada

ISBN: (纸本)9783030970994;9783030970987

Multiprocessor scheduling, also called scheduling on parallel identical machines to minimize the makespan, is a classic optimization problem that has received numerous studies. Scheduling with testing is an online variant where the processing time of a job is revealed by an extra test operation, or otherwise the job has to be executed for a given upper bound on the processing time. Albers and Eckl recently studied the multiprocessor scheduling with testing;among others, for the non-preemptive setting they presented an approximation algorithm with competitive ratio approaching 3.1016 when the number of machines tends to infinity and an improved approximation algorithm with competitive ratio approaching 3 when all test operations take a time unit. We propose to first sort the jobs into the non-increasing order of the minimum value between the upper bound and the testing time, then partition the jobs into three groups and process them group by group according to the sorted job order. We show that our algorithm achieves improved competitive ratios, which approach 2.9513 when the number of machines tends to infinity in the general case;when all test operations take a time unit, our algorithm achieves even better competitive ratios approaching 2.8081.

关键词： Multiprocessor scheduling scheduling with testing makespan non-preemptive competitive ratio approximation algorithm

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online Deterministic Minimum Cost Bipartite Matching with Delays on a Line 22nd

Online Deterministic Minimum Cost Bipartite Matching with D...

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22nd international workshop on approximation and online algorithms, WAOA 2024

作者： Kuo, Tung-Wei Department of Computer Science National Chengchi University Taipei City Taiwan

ISBN: (纸本)9783031813955

We study the online minimum cost bipartite perfect matching with delays problem. In this problem, m servers and m requests arrive over time, and an online algorithm can delay the matching between servers and requests by paying the delay cost. The objective is to minimize the total distance and delay cost. When servers and requests lie in a known metric space, there is a randomized O(logn)-competitive algorithm, where n is the size of the metric space. When the metric space is unknown a priori, Azar and Jacob-Fanani proposed a deterministic O1ϵmlog3+ϵ2-competitive algorithm for any fixed ϵ>0. This competitive ratio is tight when n=1 and becomes O(m0.59) for sufficiently small ϵ. We improve upon the result of Azar and Jacob-Fanani for the case where servers and requests are on the real line, providing a deterministic O~(m0.5)-competitive algorithm. Our algorithm is based on the Robust Matching (RM) algorithm proposed by Raghvendra for the minimum cost bipartite perfect matching problem. In this problem, delay is not allowed, and all servers arrive in the beginning. When a request arrives, the RM algorithm immediately matches the request to a free server based on the request’s minimum t-net-cost augmenting path, where t>1 is a constant. In our algorithm, we delay the matching of a request until its waiting time exceeds its minimum t-net-cost divided by t. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Computational cost

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OASIS'22: 2nd international workshop on Open Challenges in online Social Networks 22

OASIS'22: 2nd International Workshop on Open Challenges in O...

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33rd ACM Conference on Hypertext and Social Media, HT 2022 - Co-located with ACM WebSci 2022 and ACM UMAP 2022

作者： Guidi, Barbara Ricci, Laura Michienzi, Andrea Department of Computer Science University of Pisa Pisa Italy

ISBN: (纸本)9781450392334

online Social Networks (OSNs) became part of the everyday life for many people around the world. They are one of the main channel through which information can spread at lightning speed. Thanks to this fact, people use them for the most disparate reasons, such as sources of information in place of newspapers, to receive emotional or technical support, or to share their ideas and opinions to satisfy their need of sociality. Since their introduction, people questioned these services because they are affected by several problems. These problems include: preservation of the users' privacy, fake news diffusion, diffusion of illegal pieces of content, censorship vs free speech, economic value redistribution, security vs trust, and so on. The aim of this workshop is to partially trying to overcome these problems by setting up a platform for researchers to publish their contributions. The contributions can point to: innovative methods and algorithms for social graph mining, which can be helpful to develop more efficient information diffusion techniques;the problem of privacy, and how can be enforced in these system is current, and in particular the relation between security, trust and privacy is crucial in the scenario of OSNs;the decentralisation and its impact on the implementation of social services;how Artificial Intelligence techniques that respect the privacy of the users can be implemented;technologies that enable the metaverse. © 2022 Owner/Author.

关键词： Blockchain

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approximation algorithms for the Directed Path Partition Problems 15th

Approximation Algorithms for the Directed Path Partition Pro...

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15th international Frontiers of Algorithmics workshop (FAW) / Game Theory in Blockchain Track Conference / 2nd international Joint Conference on Theoretical Computer Science (IJTCS)

作者： Chen, Yong Chen, Zhi-Zhong Kennedy, Curtis Lin, Guohui Xu, Yao Zhang, An Hangzhou Dianzi Univ Dept Math Hangzhou Peoples R China Tokyo Denki Univ Div Informat Syst Design Saitama Japan Univ Alberta Dept Comp Sci Edmonton AB Canada Georgia Southern Univ Dept Comp Sci Statesboro GA USA

ISBN: (纸本)9783030970994;9783030970987

Given a digraph G = (V, E), the k-path partition problem is to find a minimum collection of vertex-disjoint directed paths each of order at most k to cover all the vertices of V. The problem has various applications in facility location, network monitoring, transportation and others. Its special case on undirected graphs has received much attention recently, but the general version is seemingly untouched in the literature. We present the first k/2-approximation algorithm, for any k >= 3, based on a novel concept of augmenting path to minimize the number of singletons in the partition. When k >= 7, we present an improved (k + 2)/3-approximation algorithm based on the maximum path-cycle cover followed by a careful 2-cycle elimination process. When k = 3, we define the second novel kind of augmenting paths to reduce the number of 2-paths and propose an improved 13/9-approximation algorithm.

关键词： Path partition digraph augmenting path matching path-cycle cover approximation algorithm

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Evaluating PaRSEC Through Matrix Computations in Scientific Applications 1

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2nd international workshop on Asynchronous Many-Task Systems and Applications (WAMTA)

作者： Cao, Qinglei Herault, Thomas Bouteiller, Aurelien Schuchart, Joseph Bosilca, George St Louis Univ St Louis MO 63103 USA Univ Tennessee Knoxville TN USA NVIDIA Knoxville TN USA

ISBN: (数字)9783031617638

ISBN: (纸本)9783031617621;9783031617638

Task-based runtime systems, characterized by their dynamic execution models and optimized resource management, contribute significantly to the computational revolution. They enable the development of more intricate and adaptable algorithms, essential in the field of computational science. This paper provides an in-depth exploration of the PaRSEC task-based runtime system, particularly focusing on its versatility in managing a variety of matrix computations. More specifically, we examine PaRSEC's role in enhancing efficiency when solving linear systems and processing dense, low-rank, mixed-precision, and sparse matrix operations, which are crucial in scientific applications, e.g., climate/weather prediction and 3D unstructured mesh deformation-the primary focus of this study. Through experimentation and analysis, we showcase PaRSEC's ability to significantly boost computational efficiency and scalability across a range of computationally intensive and less intensive tasks on various hardware architectures. Our findings not only underscore the potential of PaRSEC in advancing sustainable, efficient, and accurate domain modeling and simulation but also emphasize the growing necessity of task-based runtime systems in supporting the next generation of matrix computations.

关键词： Task-based runtime Matrix computations Cholesky factorization Low rank approximation Mixed precision Sparse operation

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AHPCAI 2022 - Proceedings of the 2022 2nd international Conference on algorithms, High Performance Computing and Artificial Intelligence

AHPCAI 2022 - Proceedings of the 2022 2nd International Conf...

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2nd international Conference on algorithms, High Performance Computing and Artificial Intelligence, AHPCAI 2022

The proceedings contain 13 papers. The topics discussed include: research on classification effect of PSO optimized SVM;blockchain-based risk tracing of animal-derived food supply chain;multi-feature fusion TextRank algorithm for sentence-oriented keyword extraction;analysis of the evolution and causes of educational involution based on prisoner's dilemma and reinforcement learning;research on DQN stock trading strategy based on investor's compound sentiment;analysis of the causes and evolution of class solidification based on Q-learning and hawk-dove game;mechanism designs of boxed pig game and experiment and analysis based on Q-learning;spiking emotions: dynamic vision emotion recognition using spiking neural networks;joint extract method from scholarly papers;a semantic code search method based on program conversion;and a model for detecting malware adversarial samples based on anomaly detection technology.

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Random Sparse approximation for a Fast and Scalable Gaussian Process 2

Random Sparse Approximation for a Fast and Scalable Gaussian...

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2nd international Seminar on Machine Learning, Optimization, and Data Science, ISMODE 2022

作者： Eressa, Muluken Regas Badis, Hakim Langar, Rami Grosso, Dorian Esiee Marne-la-ValléeF-77454 France Metron -Energy Intelligence for Industries 30 Rue de Gramont Paris75002 France

ISBN: (纸本)9781665455640

For machine learning algorithms the availability of huge data offers ample opportunity to learn and infer educated generalizations. However, for gaussian process the size of the data presents a challenge for their wider application in the areas of big data domain. Various approaches have been suggested to ensure scalability and computational efficiency. Such as, the kernel approximation and the variational inference are few notable mentions. This paper proposes a random sparse Gaussian approximation method based on a stochastic column sampling. It employs frequency analysis to select subsets of points that would generalize the observed data. Then, applies sparsity and sampling without replacement strategy when building the model. The predictive performance of the model is evaluated using the Variational Gaussian Process (VGA) as a benchmark. We run a Monte Carlo type model building and evaluation scheme using the mean square error (MSE) and R2 score as quality metrics. An ensemble of models were trained and evaluated for different sampling sizes under the same setting. The experiments have shown that the RSGA, on average is 10 times faster and offer better predictive performance compared to VGA. In addition, the RSGA offers a robust response to changes in kernel type compared to the VGA. Hence, for a fast optimal kernel estimation and big data analysis, the RSGA can give an alternative route to model building and inference. © 2022 IEEE.

关键词： Gaussian distribution

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Research on full-process product defect traceability analysis technology based on workshop big data 2

Research on full-process product defect traceability analysi...

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2nd international Conference on algorithms, High Performance Computing and Artificial Intelligence, AHPCAI 2022

作者： Zhang, Jianquan Lan, Shiqing Liu, Lei He, Ru Shenzhen Catic Information Industry Group Co. Ltd. Guangdong Shenzhen518055 China IoT Research Institute Shenzhen Polytechnic Guangdong Shenzhen518055 China

ISBN: (纸本)9798350334784

The Internet of Things(IOT) technology collects real-time data from material management, equipment status, production process integration, process parameters and other accurate traceability analysis, obtains the information of the whole production process, optimizes the production plan and workshop scheduling, and promotes the intellectualization of the manufacturing industry in the digitization of the whole production process. In the production process of modern discrete manufacturing industry, various types of data will be continuously generated. Radio Frequency Identification(RFID), industrial wireless sensor network, Ethernet and intelligent gateway equipment will be used to realize interconnection and intercommunication. The product quality data in the production process will be collected automatically and in real time. The data will be returned to the data integration center in the form of sensor network networking and stored in the big data platform system, and the big data analysis will be applied, Mining, forecasting, analyzing and processing the product quality, and combining scientific decision-making methods to optimize the production process and achieve more rapid and accurate control. This paper analyzes the structure of industrial big data platform, gives the process of product quality prediction based on big data, and designs the discrete manufacturing product quality prediction model to realize the regression prediction of product quality based on eXtreme Gradient Boosting(XGBoost). © 2022 IEEE.

关键词： Internet of things

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Proceedings - 2021 IEEE/ACM 2nd international workshop on Quantum Software Engineering, Q-SE 2021

Proceedings - 2021 IEEE/ACM 2nd International Workshop on Qu...

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2nd IEEE/ACM international workshop on Quantum Software Engineering, Q-SE 2021

ISBN: (纸本)9781665444620

The proceedings contain 8 papers. The topics discussed include: modeling quantum circuits with UML;some size and structure metrics for quantum software;quantum software models: the density matrix for classical and quantum software systems design;towards model-driven quantum software engineering;identifying bug patterns in quantum programs;QBugs: a collection of reproducible bugs in quantum algorithms and a supporting infrastructure to enable controlled quantum software testing and debugging experiments;experimental implementation of discrete time quantum walk with the IBM Qiskit library;and generalized quantum tree search.

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