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Generalized Contextual Bandits With Latent Features: algorithms and Applications

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第8期34卷 4763-4775页

作者： Xu, Xiongxiao Xie, Hong Lui, John C. S. Chongqing Univ Chongqing Key Lab Software Theory & Technol Chongqing 400044 Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China

Contextual bandit is a popular sequential decision-making framework to balance the exploration and exploitation tradeoff in many applications such as recommender systems, search engines, etc. Motivated by two important factors in real-world applications: 1) latent contexts (or features) often exist and 2) feedbacks often have humans in the loop leading to human biases, we formulate a generalized contextual bandit framework with latent contexts. Our proposed framework includes a two-layer probabilistic interpretable model for the feedbacks from human with latent features. We design a GCL-PS algorithm for the proposed framework, which utilizes posterior sampling to balance the exploration and exploitation tradeoff. We prove a sublinear regret upper bound for GCL-PS, and prove a lower bound for the proposed bandit framework revealing insights on the optimality of GCL-PS. To further improve the computational efficiency of GCL-PS, we propose a Markov Chain Monte Carlo (MCMC) algorithm to generate approximate samples, resulting in our GCL-PSMC algorithm. We not only prove a sublinear Bayesian regret upper bound for our GCL-PSMC algorithm, but also reveal insights into the tradeoff between computational efficiency and sequential decision accuracy. Finally, we apply the proposed framework to hotel recommendations and news article recommendations, and show its superior performance over a variety of baselines via experiments on two public datasets.

关键词： Context modeling Motion pictures approximation algorithms Decision making Search engines Upper bound Logistics Generalized contextual bandit latent features Markov Chain Monte Carlo (MCMC) posterior sampling

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Incremental Cycle Bases for Cycle-Based Pose Graph Optimization

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IEEE ROBOTICS AND AUTOMATION LETTERS 2023年第2期8卷 1021-1028页

作者： Forsgren, Brendon Brink, Kevin Ganesh, Prashant McLain, Timothy W. W. Brigham Young Univ Dept Mech Engn Provo UT 84606 USA Eglin AFB AF Res Lab Eglin AFB FL 32542 USA Univ Florida Dept Mech & Aerosp Engn Shalimar FL 32579 USA

Post graph optimization is a special case of the simultaneous localization and mapping problem where the only variables to be estimated are pose variables and the only measurements are inter-pose constraints. The vast majority of pose graph optimization techniques are vertex based (variables are robot poses), but recent work has parameterized the pose graph optimization problem in a relative fashion (variables are the transformations between poses) that utilizes a minimum cycle basis to maximize the sparsity of the problem. We explore the construction of a cycle basis in an incremental manner while maximizing the sparsity. We validate an algorithm that constructs a sparse cycle basis incrementally and compare its performance with a minimum cycle basis. Additionally, we present an algorithm to approximate the minimum cycle basis of two graphs that are sparsely connected as is common in multi agent scenarios. Lastly, the relative parameterization of pose graph optimization has been limited to using rigid body transforms on SE(2) or SE(3) as the constraints between poses. We introduce a methodology to allow for the use of lower-degree-of-freedom measurements in the relative pose graph optimization problem. We provide extensive validation of our algorithms on standard benchmarks, simulated datasets, and custom hardware.

关键词： approximation algorithms Robots Optimization Simultaneous localization and mapping Standards Heuristic algorithms Trajectory SLAM localization multi-robot systems

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H8 Control for Oscillator Systems With Event-Triggering Signal Transmission of Internet of

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IEEE ACCESS 2023年 11卷 8938-8949页

作者： Tan, Luy Nguyen Gupta, Nishu Derawi, Mohammad Ho Chi Minh City Univ Technol HCMUT Vietnam Natl Univ Ho Chi Minh City VNU HCM Fac Elect Elect Engn FEEE Ho Chi Minh City 700000 Vietnam Norwegian Univ Sci & Technol Fac Informat Technol & Elect Engn Dept Elect Syst N-2815 Gjovik Norway

This article proposes to design a distributed H0,0 optimal control algorithm for Van der Pol oscillators with unknown internal dynamics, input constraints and external disturbances, via event-triggering signal transmission of the Internet of Things (IoT). First, the graph theory for the IoT is introduced. Second, the dynamics of Van der Pol oscillators are transformed into the tracking dynamics which cooperate via the IoT network. Third, unlike the existing online optimal control algorithms using adaptive dynamic programming, we design an H(infinity )optimal control algorithm employing an event-triggering signal transmission mechanism to reduce the burden of communication resource and computation bandwidth of the IoT network. As the triggering condition and approximation parameter update policies are appropriately designed, the algorithm guarantees that the Zeno phenomenon is free, the consensus errors are uniformly ultimately bounded, and the external disturbance is compensated. Finally, numerical simulation results with comparison to the time-triggering algorithms confirm the effectiveness of the proposed algorithm.

关键词： Oscillators Heuristic algorithms Optimal control Internet of Things Decentralized control approximation algorithms Nonlinear systems Communication resources event-triggering IoT multi-agent systems neural network Van der Pol oscillators

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Laplace Distribution Based Online Identification of Linear Systems With Robust Recursive Expectation-Maximization Algorithm

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第8期19卷 9028-9036页

作者： Chen, Xin Zhao, Shunyi Liu, Fei Tao, Chongben Suzhou Univ Sci & Technol Sch Elect & Informat Engn Suzhou 215009 Peoples R China Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Peoples R China

The robust online identification problem of linear systems is considered in this article using a faster robust recursive expectation-maximization (RREM) framework. To improve the convergence rate, the outliers, which would deteriorate the identified models, are accommodated with a Laplace distribution instead of Student's $t$-distribution. Then, the recursive transformation of the maximum likelihood function is realized with a recursive $Q$-function. The extensively recognized autoregressive exogenous (ARX) models are used for the description of general linear systems. As a result, the unknown parameters, including the regression coefficient vector of the ARX models, the variance of the noise without outliers, and the scale parameter of the Laplace distribution, are determined in a recursive manner. The performance of the proposed approach is tested with a simulated continuous fermentation reactor system example and a coupled-tank experiment.

关键词： Gaussian distribution Pollution measurement Bayes methods approximation algorithms Linear systems Heuristic algorithms Informatics Laplace distribution linear systems online identification robust recursive expectation-maximization (EM) algorithm

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Primal-Dual Fixed Point algorithms Based on Adapted Metric for Distributed Optimization

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第6期34卷 2923-2937页

作者： Li, Huaqing Zheng, Zuqing Lu, Qingguo Wang, Zheng Gao, Lan Wu, Guo-Cheng Ji, Lianghao Wang, Huiwei Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Beihang Univ Hangzhou Innovat Inst Hangzhou 310023 Peoples R China Neijiang Normal Univ Coll Math & Informat Sci Data Recovery Key Lab Sichuan Prov Neijiang 641100 Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Image Cognit Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Computat Intelligence Chongqing 400065 Peoples R China

This article considers distributed optimization by a group of agents over an undirected network. The objective is to minimize the sum of a twice differentiable convex function and two possibly nonsmooth convex functions, one of which is composed of a bounded linear operator. A novel distributed primal-dual fixed point algorithm is proposed based on an adapted metric method, which exploits the second-order information of the differentiable convex function. Furthermore, by incorporating a randomized coordinate activation mechanism, we propose a randomized asynchronous iterative distributed algorithm that allows each agent to randomly and independently decide whether to perform an update or remain unchanged at each iteration, and thus alleviates the communication cost. Moreover, the proposed algorithms adopt nonidentical stepsizes to endow each agent with more independence. Numerical simulation results substantiate the feasibility of the proposed algorithms and the correctness of the theoretical results.

关键词： Optimization Measurement Convex functions approximation algorithms Signal processing algorithms Convergence Prediction algorithms Adapted metric method completely distributed nonsmooth convex function primal-dual fixed point algorithm randomized asynchronous iterative

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Cover Trees Revisited: Exploiting Unused Distance and Direction Information

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第11期35卷 11231-11245页

作者： Wang, Zhi-Jie Nie, Mengdie Zhao, Kaiqi Quan, Zhe Yao, Bin Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Chongqing Univ Key Lab Dependable Serv Comp Cyber Phys Soc Minist Educ Chongqing 400044 Peoples R China Pinduoduo Co Shanghai 200051 Peoples R China Univ Auckland Dept Comp Sci Auckland 1010 New Zealand Hunan Univ Coll Comp Sci Changsha 410012 Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci Shanghai 200240 Peoples R China

The cover tree (CT) and its improved version are hierarchical data structures that simplified navigating nets while maintaining good runtime guarantees. They can perform nearest neighbor search in logarithmic time and provide efficient computation in practice. In this article, we revisit cover trees for nearest neighbor search, and propose a more competitive method. The central idea of our method is to fully exploit the unused distance and direction information. More specially, our method introduces three novel concepts/techniques: (i) range list, (ii) quadrant information, and (iii) vectorial angle cosine. These techniques are seamlessly integrated into our suggested data structure and search algorithms. As an extra bonus, we explore approximate nearest neighbor and k nearest neighbor based on the proposed techniques, and present algorithms for handling updates. Extensive experimental results, based on both real and synthetic datasets, consistently demonstrate that our method is attractive and competitive, compared against existing cover tree structures for nearest neighbor search and its variants.

关键词： Nearest neighbor methods Data structures Navigation Runtime approximation algorithms Computer science Computed tomography Cover tree data mining machine learing data structure and algorithms

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Noise Robustness of Quantum Relaxation for Combinatorial Optimization

IEEE TRANSACTIONS ON QUANTUM ENGINEERING

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IEEE TRANSACTIONS ON QUANTUM ENGINEERING 2024年第1期5卷 1页

作者： Tamura, Kentaro Suzuki, Yohichi Raymond, Rudy Watanabe, Hiroshi C. Sato, Yuki Kondo, Ruho Sugawara, Michihiko Yamamoto, Naoki Keio Univ Dept Appl Phys & Physico Informat Yokohama 2238522 Japan Keio Univ Quantum Comp Ctr Yokohama 2238522 Japan IBM Japan Ltd IBM Quantum Tokyo 1038510 Japan Univ Tokyo Dept Comp Sci Tokyo 1130033 Japan Kyushu Univ Grad Sch Sci Dept Chem Fukuoka 8190395 Japan Toyota Cent Res & Dev Labs Inc Nagakute Aichi 4801192 Japan

Relaxation is a common way for dealing with combinatorial optimization problems. Quantum random-access optimization (QRAO) is a quantum-relaxation-based optimizer that uses fewer qubits than the number of bits in the original problem by encoding multiple variables per qubit using quantum random-access code (QRAC). Reducing the number of qubits will alleviate physical noise (typically, decoherence), and as a result, the quality of the binary solution of QRAO may be robust against noise, which is, however, unknown. In this article, we numerically demonstrate that the mean approximation ratio of the (3, 1)-QRAC Hamiltonian, i.e., the Hamiltonian utilizing the encoding of three bits into one qubit by QRAC, is less affected by noise compared with the conventional Ising Hamiltonian used in the quantum annealer and the quantum approximate optimization algorithm. Based on this observation, we discuss a plausible mechanism behind the robustness of QRAO under depolarizing noise. Finally, we assess the number of shots required to estimate the values of binary variables correctly under depolarizing noise and show that the (3, 1)-QRAC Hamiltonian requires less shots to achieve the same accuracy compared with the Ising Hamiltonian.

关键词： Qubit Noise Optimization Encoding Quantum state Decoding approximation algorithms Combinatorial optimization depolarizing noise quantum random-access code (QRAC) quantum relaxation

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Minimizing cost for influencing target groups in social network: A model and algorithmic approach

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COMPUTER COMMUNICATIONS 2023年 212卷 182-197页

作者： Pham, Phuong N. H. Pham, Canh, V Duong, Hieu, V Snasel, Vaclav Thanh, Nguyen Trung Ho Chi Minh City Univ Ind & Trade Fac Informat Technol Ho Chi Minh Vietnam Phenikaa Univ ORLab Hanoi 12116 Vietnam Peoples Secur Acced Hanoi Vietnam VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Ostrava Czech Republic

Stimulated by practical applications arising from economics, viral marketing, and elections, this paper studies the problem of Groups Influence with Minimum cost (GIM), which aims to find a seed set with the smallest cost that can influence all target groups in a social network, where each user is assigned a cost and a score and a group of users is influenced if the total score of influenced users in the group is at least a certain threshold. As the group influence function, defined as the number of influenced groups or users, is neither submodular nor supermodular, theoretical bounds on the quality of solutions returned by the well-known greedy approach may not be *** this work, two efficient algorithms with theoretical guarantees for tackling the GIM problem, named Groups Influence approximation (GIA) and Exact Groups Influence (EGI), are proposed. GIA is a bi-criteria polynomial-time approximation algorithm and EGI is an (almost) exact algorithm;both can return good approximate solutions with high probability. The novelty of our approach lies in two aspects. Firstly, a novel group reachable reverse sample concept is proposed to estimate the group influence function within an error bound. Secondly, a framework algorithmic is designed to find serial candidate solutions with checking theoretical guarantees at the same time. Besides theoretical results, extensive experiments conducted on real social networks show our algorithms' performance. In particular, both EGI and GIA provide the solution quality several times better, while GIA is up to 800 times faster than the state-of-the-art algorithms.

关键词： Online social networks Social influence Group influence approximation algorithms

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Multi-UAV Cooperative Trajectory for Servicing Dynamic Demands and Charging Battery

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2023年第3期22卷 1599-1614页

作者： Wang, Kai Zhang, Xiao Duan, Lingjie Tie, Jun South Cent Univ Nationalities Coll Comp Sci Wuhan Peoples R China Chinese Univ Hong Kong Dept Informat Engn Hong Kong Peoples R China Hubei Prov Engn Res Ctr Intelligent Management Mfg Wuhan 430074 Peoples R China Singapore Univ Technol & Design Engn Syst & Design Singapore 487372 Singapore

Unmanned Aerial Vehicle (UAV) technology is a promising solution for providing high-quality mobile services (e.g., edge computing, fast Internet connection, and local caching) to ground users, where a UAV with limited service coverage travels among multiple geographical user locations (e.g., hotspots) for servicing their demands locally. How to dynamically determine a UAV swarm's cooperative path planning to best meet many users' spatio-temporally distributed demands is an important question but is unaddressed in the literature. To our best knowledge, this paper is the first to design and analyze cooperative path planning algorithms of a large UAV swarm for optimally servicing many spatial locations, where ground users' demands are released dynamically in the long time horizon. Regarding a single UAV's path planning design, we manage to substantially simplify the traditional dynamic program and propose an optimal algorithm of low computation complexity, which is only polynomial with respect to both the numbers of spatial locations and user demands. After coordinating a large number $K$K of UAVs, this simplified dynamic optimization problem becomes intractable and we alternatively present a fast iterative cooperation algorithm with provable approximation ratio 1-(1-1/K)(K) in the worst case, which is proved to obviously outperform the traditional approach of partitioning UAVs to serve different location clusters separately. To relax UAVs' battery capacity limit for sustainable service provisioning, we further allow UAVs to travel to charging stations in the mean time and thus jointly design UAVs' path planning over users' locations and charging stations. Despite of the problem difficulty, for the optimal solution, we successfully transform the problem to an integer linear program by creating novel directed acyclic graph of the UAV-state transition diagram, and propose an iterative algorithm with constant approximation ratio. Finally, we validate the theoretical resul

关键词： Path planning Heuristic algorithms Wireless communication Trajectory Charging stations Vehicle dynamics Optimization UAV swarm cooperation optimal trajectory planning dynamic demands battery charging stations approximation algorithms

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An Improved Gray Wolf Algorithm for Radiation Near-Field Source Localization in Exact Model

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IEEE SENSORS JOURNAL 2023年第21期23卷 26311-26326页

作者： Zhang, Qi Li, Wenxing Li, Si Yang, Bin Mao, Yunlong Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China Jiangsu Univ Sci & Technol Sch Elect & Informat Zhenjiang 212100 Jiangsu Peoples R China Hangzhou Dianzi Univ Hangzhou Sch Cyberspace Hangzhou 310018 Peoples R China

Spectral search algorithms are widely used in radiation near-field (RNF) source locations because of their high estimation accuracy, but they have disadvantages such as large computation and long running time. To solve this problem, the Fresnel model is mainly adopted in the existing works of literature to realize the source location estimation in the algorithm of spectral search. Although the operation time is effectively reduced, there is a mismatch between the Fresnel model and the exact model, resulting in the existing method being not suitable for the source location under the exact model. In order to shorten the running time of the RNF source spectrum search algorithm under the exact model, an improved gray wolf optimal (IGWO) algorithm based on the exact model is proposed to estimate the RNF source position, which guarantees the high precision and shorten the operation time. The algorithm uses the noise subspace fitting method as the cost function and the power function as the convergence factor, which shortens the time required for source location and improves the estimation accuracy of the algorithm. The simulation results show that the IGWO algorithm takes less time and has better estimation accuracy than the existing source location algorithms.

关键词： Position measurement Estimation Sensors approximation algorithms Signal processing algorithms Sensor arrays Location awareness Array signal processing improved gray wolf optimal (IGWO) algorithm near field source localization

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