Journal articles: 'Online learning algorithms'

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Smale, Steve, and Yuan Yao. "Online Learning Algorithms." Foundations of Computational Mathematics 6, no. 2 (September 23, 2005): 145–70. http://dx.doi.org/10.1007/s10208-004-0160-z.

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Xu, Chenyang, and Benjamin Moseley. "Learning-Augmented Algorithms for Online Steiner Tree." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (June 28, 2022): 8744–52. http://dx.doi.org/10.1609/aaai.v36i8.20854.

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This paper considers the recently popular beyond-worst-case algorithm analysis model which integrates machine-learned predictions with online algorithm design. We consider the online Steiner tree problem in this model for both directed and undirected graphs. Steiner tree is known to have strong lower bounds in the online setting and any algorithm’s worst-case guarantee is far from desirable. This paper considers algorithms that predict which terminal arrives online. The predictions may be incorrect and the algorithms’ performance is parameterized by the number of incorrectly predicted terminals. These guarantees ensure that algorithms break through the online lower bounds with good predictions and the competitive ratio gracefully degrades as the prediction error grows. We then observe that the theory is predictive of what will occur empirically. We show on graphs where terminals are drawn from a distribution, the new online algorithms have strong performance even with modestly correct predictions.

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Ying, Yiming, and Ding-Xuan Zhou. "Online Pairwise Learning Algorithms." Neural Computation 28, no. 4 (April 2016): 743–77. http://dx.doi.org/10.1162/neco_a_00817.

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Pairwise learning usually refers to a learning task that involves a loss function depending on pairs of examples, among which the most notable ones are bipartite ranking, metric learning, and AUC maximization. In this letter we study an online algorithm for pairwise learning with a least-square loss function in an unconstrained setting of a reproducing kernel Hilbert space (RKHS) that we refer to as the Online Pairwise lEaRning Algorithm (OPERA). In contrast to existing works (Kar, Sriperumbudur, Jain, & Karnick, 2013 ; Wang, Khardon, Pechyony, & Jones, 2012 ), which require that the iterates are restricted to a bounded domain or the loss function is strongly convex, OPERA is associated with a non-strongly convex objective function and learns the target function in an unconstrained RKHS. Specifically, we establish a general theorem that guarantees the almost sure convergence for the last iterate of OPERA without any assumptions on the underlying distribution. Explicit convergence rates are derived under the condition of polynomially decaying step sizes. We also establish an interesting property for a family of widely used kernels in the setting of pairwise learning and illustrate the convergence results using such kernels. Our methodology mainly depends on the characterization of RKHSs using its associated integral operators and probability inequalities for random variables with values in a Hilbert space.

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Le Thi, Hoai An, and Vinh Thanh Ho. "Online Learning Based on Online DCA and Application to Online Classification." Neural Computation 32, no. 4 (April 2020): 759–93. http://dx.doi.org/10.1162/neco_a_01266.

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We investigate an approach based on DC (Difference of Convex functions) programming and DCA (DC Algorithm) for online learning techniques. The prediction problem of an online learner can be formulated as a DC program for which online DCA is applied. We propose the two so-called complete/approximate versions of online DCA scheme and prove their logarithmic/sublinear regrets. Six online DCA-based algorithms are developed for online binary linear classification. Numerical experiments on a variety of benchmark classification data sets show the efficiency of our proposed algorithms in comparison with the state-of-the-art online classification algorithms.

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Shah, Kulin, and Naresh Manwani. "Online Active Learning of Reject Option Classifiers." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5652–59. http://dx.doi.org/10.1609/aaai.v34i04.6019.

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Active learning is an important technique to reduce the number of labeled examples in supervised learning. Active learning for binary classification has been well addressed in machine learning. However, active learning of the reject option classifier remains unaddressed. In this paper, we propose novel algorithms for active learning of reject option classifiers. We develop an active learning algorithm using double ramp loss function. We provide mistake bounds for this algorithm. We also propose a new loss function called double sigmoid loss function for reject option and corresponding active learning algorithm. We offer a convergence guarantee for this algorithm. We provide extensive experimental results to show the effectiveness of the proposed algorithms. The proposed algorithms efficiently reduce the number of label examples required.

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Ying, Yiming, and Massimiliano Pontil. "Online Gradient Descent Learning Algorithms." Foundations of Computational Mathematics 8, no. 5 (April 25, 2007): 561–96. http://dx.doi.org/10.1007/s10208-006-0237-y.

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BARBAKH, WESAM, and COLIN FYFE. "ONLINE CLUSTERING ALGORITHMS." International Journal of Neural Systems 18, no. 03 (June 2008): 185–94. http://dx.doi.org/10.1142/s0129065708001518.

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We introduce a set of clustering algorithms whose performance function is such that the algorithms overcome one of the weaknesses of K-means, its sensitivity to initial conditions which leads it to converge to a local optimum rather than the global optimum. We derive online learning algorithms and illustrate their convergence to optimal solutions which K-means fails to find. We then extend the algorithm by underpinning it with a latent space which enables a topology preserving mapping to be found. We show visualisation results on some standard data sets.

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Shani, Lior, Tom Zahavy, and Shie Mannor. "Online Apprenticeship Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (June 28, 2022): 8240–48. http://dx.doi.org/10.1609/aaai.v36i8.20798.

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In Apprenticeship Learning (AL), we are given a Markov Decision Process (MDP) without access to the cost function. Instead, we observe trajectories sampled by an expert that acts according to some policy. The goal is to find a policy that matches the expert's performance on some predefined set of cost functions. We introduce an online variant of AL (Online Apprenticeship Learning; OAL), where the agent is expected to perform comparably to the expert while interacting with the environment. We show that the OAL problem can be effectively solved by combining two mirror descent based no-regret algorithms: one for policy optimization and another for learning the worst case cost. By employing optimistic exploration, we derive a convergent algorithm with O(sqrt(K)) regret, where K is the number of interactions with the MDP, and an additional linear error term that depends on the amount of expert trajectories available. Importantly, our algorithm avoids the need to solve an MDP at each iteration, making it more practical compared to prior AL methods. Finally, we implement a deep variant of our algorithm which shares some similarities to GAIL, but where the discriminator is replaced with the costs learned by OAL. Our simulations suggest that OAL performs well in high dimensional control problems.

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Yang, Feidiao, Jiaqing Jiang, Jialin Zhang, and Xiaoming Sun. "Revisiting Online Quantum State Learning." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 6607–14. http://dx.doi.org/10.1609/aaai.v34i04.6136.

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In this paper, we study the online quantum state learning problem which is recently proposed by Aaronson et al. (2018). In this problem, the learning algorithm sequentially predicts quantum states based on observed measurements and losses and the goal is to minimize the regret. In the previous work, the existing algorithms may output mixed quantum states. However, in many scenarios, the prediction of a pure quantum state is required. In this paper, we first propose a Follow-the-Perturbed-Leader (FTPL) algorithm that can guarantee to predict pure quantum states. Theoretical analysis shows that our algorithm can achieve an O(√T) expected regret under some reasonable settings. In the case that the pure state prediction is not mandatory, we propose another deterministic learning algorithm which is simpler and more efficient. The algorithm is based on the online gradient descent (OGD) method and can also achieve an O(√T) regret bound. The main technical contribution of this result is an algorithm of projecting an arbitrary Hermitian matrix onto the set of density matrices with respect to the Frobenius norm. We think this subroutine is of independent interest and can be widely used in many other problems in the quantum computing area. In addition to the theoretical analysis, we evaluate the algorithms with a series of simulation experiments. The experimental results show that our FTPL method and OGD method outperform the existing RFTL approach proposed by Aaronson et al. (2018) in almost all settings. In the implementation of the RFTL approach, we give a closed-form solution to the algorithm. This provides an efficient, accurate, and completely executable solution to the RFTL method.

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Du, Bingqian, Zhiyi Huang, and Chuan Wu. "Adversarial Deep Learning for Online Resource Allocation." ACM Transactions on Modeling and Performance Evaluation of Computing Systems 6, no. 4 (December 31, 2021): 1–25. http://dx.doi.org/10.1145/3494526.

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Online algorithms are an important branch in algorithm design. Designing online algorithms with a bounded competitive ratio (in terms of worst-case performance) can be hard and usually relies on problem-specific assumptions. Inspired by adversarial training from Generative Adversarial Net and the fact that the competitive ratio of an online algorithm is based on worst-case input, we adopt deep neural networks (NNs) to learn an online algorithm for a resource allocation and pricing problem from scratch, with the goal that the performance gap between offline optimum and the learned online algorithm can be minimized for worst-case input. Specifically, we leverage two NNs as the algorithm and the adversary, respectively, and let them play a zero sum game, with the adversary being responsible for generating worst-case input while the algorithm learns the best strategy based on the input provided by the adversary. To ensure better convergence of the algorithm network (to the desired online algorithm), we propose a novel per-round update method to handle sequential decision making to break complex dependency among different rounds so that update can be done for every possible action instead of only sampled actions. To the best of our knowledge, our work is the first using deep NNs to design an online algorithm from the perspective of worst-case performance guarantee. Empirical studies show that our updating methods ensure convergence to Nash equilibrium and the learned algorithm outperforms state-of-the-art online algorithms under various settings.

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Tornede, Alexander, Viktor Bengs, and Eyke Hüllermeier. "Machine Learning for Online Algorithm Selection under Censored Feedback." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (June 28, 2022): 10370–80. http://dx.doi.org/10.1609/aaai.v36i9.21279.

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In online algorithm selection (OAS), instances of an algorithmic problem class are presented to an agent one after another, and the agent has to quickly select a presumably best algorithm from a fixed set of candidate algorithms. For decision problems such as satisfiability (SAT), quality typically refers to the algorithm's runtime. As the latter is known to exhibit a heavy-tail distribution, an algorithm is normally stopped when exceeding a predefined upper time limit. As a consequence, machine learning methods used to optimize an algorithm selection strategy in a data-driven manner need to deal with right-censored samples, a problem that has received little attention in the literature so far. In this work, we revisit multi-armed bandit algorithms for OAS and discuss their capability of dealing with the problem. Moreover, we adapt them towards runtime-oriented losses, allowing for partially censored data while keeping a space- and time-complexity independent of the time horizon. In an extensive experimental evaluation on an adapted version of the ASlib benchmark, we demonstrate that theoretically well-founded methods based on Thompson sampling perform specifically strong and improve in comparison to existing methods.

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Chen, Weirong, Jiaqi Zheng, Haoyu Yu, Guihai Chen, Yixin Chen, and Dongsheng Li. "Online Learning Bipartite Matching with Non-stationary Distributions." ACM Transactions on Knowledge Discovery from Data 16, no. 5 (October 31, 2022): 1–22. http://dx.doi.org/10.1145/3502734.

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Online bipartite matching has attracted wide interest since it can successfully model the popular online car-hailing problem and sharing economy. Existing works consider this problem under either adversary setting or i.i.d. setting. The former is too pessimistic to improve the performance in the general case; the latter is too optimistic to deal with the varying distribution of vertices. In this article, we initiate the study of the non-stationary online bipartite matching problem, which allows the distribution of vertices to vary with time and is more practical. We divide the non-stationary online bipartite matching problem into two subproblems, the matching problem and the selecting problem, and solve them individually. Combining Batch algorithms and deep Q-learning networks, we first construct a candidate algorithm set to solve the matching problem. For the selecting problem, we use a classical online learning algorithm, Exp3, as a selector algorithm and derive a theoretical bound. We further propose CDUCB as a selector algorithm by integrating distribution change detection into UCB. Rigorous theoretical analysis demonstrates that the performance of our proposed algorithms is no worse than that of any candidate algorithms in terms of competitive ratio. Finally, extensive experiments show that our proposed algorithms have much higher performance for the non-stationary online bipartite matching problem comparing to the state-of-the-art.

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Osman, Hassab Elgawi. "Variable Ranking for Online Ensemble Learning." Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no. 3 (May 20, 2009): 331–37. http://dx.doi.org/10.20965/jaciii.2009.p0331.

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In proposing, incremental feature selection based on correlation ranking (CR) for classification problems, we develop on-line training using the random forests (RF) algorithm, then evaluate the performance of the combination based on an NIPS 2003 Feature Selection Challenge dataset. Results show that our approach achieves performance comparable to others batch learning algorithms, including RF.

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Dixit, Rishabh, Amrit Singh Bedi, Ruchi Tripathi, and Ketan Rajawat. "Online Learning With Inexact Proximal Online Gradient Descent Algorithms." IEEE Transactions on Signal Processing 67, no. 5 (March 2019): 1338–52. http://dx.doi.org/10.1109/tsp.2018.2890368.

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Likas, Aristidis. "A Reinforcement Learning Approach to Online Clustering." Neural Computation 11, no. 8 (November 1, 1999): 1915–32. http://dx.doi.org/10.1162/089976699300016025.

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A general technique is proposed for embedding online clustering algorithms based on competitive learning in a reinforcement learning framework. The basic idea is that the clustering system can be viewed as a reinforcement learning system that learns through reinforcements to follow the clustering strategy we wish to implement. In this sense, the reinforcement guided competitive learning (RGCL) algorithm is proposed that constitutes a reinforcement-based adaptation of learning vector quantization (LVQ) with enhanced clustering capabilities. In addition, we suggest extensions of RGCL and LVQ that are characterized by the property of sustained exploration and significantly improve the performance of those algorithms, as indicated by experimental tests on well-known data sets.

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Sun, Boliang, Guohui Li, Li Jia, and Kuihua Huang. "Online Coregularization for Multiview Semisupervised Learning." Scientific World Journal 2013 (2013): 1–15. http://dx.doi.org/10.1155/2013/398146.

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We propose a novel online coregularization framework for multiview semisupervised learning based on the notion of duality in constrained optimization. Using the weak duality theorem, we reduce the online coregularization to the task of increasing the dual function. We demonstrate that the existing online coregularization algorithms in previous work can be viewed as an approximation of our dual ascending process using gradient ascent. New algorithms are derived based on the idea of ascending the dual function more aggressively. For practical purpose, we also propose two sparse approximation approaches for kernel representation to reduce the computational complexity. Experiments show that our derived online coregularization algorithms achieve risk and accuracy comparable to offline algorithms while consuming less time and memory. Specially, our online coregularization algorithms are able to deal with concept drift and maintain a much smaller error rate. This paper paves a way to the design and analysis of online coregularization algorithms.

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LEVINA, TATSIANA, and GLENN SHAFER. "PORTFOLIO SELECTION AND ONLINE LEARNING." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16, no. 04 (August 2008): 437–73. http://dx.doi.org/10.1142/s0218488508005364.

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This paper studies a new strategy for selecting portfolios in the stock market. The strategy is inspired by two streams of previous work: (1) work on universalization of strategies for portfolio selection, which began with Thomas Cover's work on constant rebalanced portfolios, published in 1991,4 and (2) more general work on universalization of online algorithms,17,21,23,30 especially Vladimir Vovk's work on the aggregating algorithm and Markov switching strategies.32 The proposed investment strategy achieves asymptotically the same exponential rate of growth as the portfolio that turns out to be best expost in the long run and does not require any underlying statistical assumptions on the nature of the stock market.

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Ai, Qingyao, Tao Yang, Huazheng Wang, and Jiaxin Mao. "Unbiased Learning to Rank." ACM Transactions on Information Systems 39, no. 2 (March 2021): 1–29. http://dx.doi.org/10.1145/3439861.

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How to obtain an unbiased ranking model by learning to rank with biased user feedback is an important research question for IR. Existing work on unbiased learning to rank (ULTR) can be broadly categorized into two groups—the studies on unbiased learning algorithms with logged data, namely, the offline unbiased learning, and the studies on unbiased parameters estimation with real-time user interactions, namely, the online learning to rank. While their definitions of unbiasness are different, these two types of ULTR algorithms share the same goal—to find the best models that rank documents based on their intrinsic relevance or utility. However, most studies on offline and online unbiased learning to rank are carried in parallel without detailed comparisons on their background theories and empirical performance. In this article, we formalize the task of unbiased learning to rank and show that existing algorithms for offline unbiased learning and online learning to rank are just the two sides of the same coin. We evaluate eight state-of-the-art ULTR algorithms and find that many of them can be used in both offline settings and online environments with or without minor modifications. Further, we analyze how different offline and online learning paradigms would affect the theoretical foundation and empirical effectiveness of each algorithm on both synthetic and real search data. Our findings provide important insights and guidelines for choosing and deploying ULTR algorithms in practice.

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Zhang, Lijun, Rong Jin, Chun Chen, Jiajun Bu, and Xiaofei He. "Efficient Online Learning for Large-Scale Sparse Kernel Logistic Regression." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 1219–25. http://dx.doi.org/10.1609/aaai.v26i1.8300.

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In this paper, we study the problem of large-scale Kernel Logistic Regression (KLR). A straightforward approach is to apply stochastic approximation to KLR. We refer to this approach as non-conservative online learning algorithm because it updates the kernel classifier after every received training example, leading to a dense classifier. To improve the sparsity of the KLR classifier, we propose two conservative online learning algorithms that update the classifier in a stochastic manner and generate sparse solutions. With appropriately designed updating strategies, our analysis shows that the two conservative algorithms enjoy similar theoretical guarantee as that of the non-conservative algorithm. Empirical studies on several benchmark data sets demonstrate that compared to batch-mode algorithms for KLR, the proposed conservative online learning algorithms are able to produce sparse KLR classifiers, and achieve similar classification accuracy but with significantly shorter training time. Furthermore, both the sparsity and classification accuracy of our methods are comparable to those of the online kernel SVM.

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Yao, Yuan. "On Complexity Issues of Online Learning Algorithms." IEEE Transactions on Information Theory 56, no. 12 (December 2010): 6470–81. http://dx.doi.org/10.1109/tit.2010.2079010.

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Chen, Xiaming, and Yunwen Lei. "Refined bounds for online pairwise learning algorithms." Neurocomputing 275 (January 2018): 2656–65. http://dx.doi.org/10.1016/j.neucom.2017.11.049.

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Ravishankar, Saiprasad, Bihan Wen, and Yoram Bresler. "Online Sparsifying Transform Learning—Part I: Algorithms." IEEE Journal of Selected Topics in Signal Processing 9, no. 4 (June 2015): 625–36. http://dx.doi.org/10.1109/jstsp.2015.2417131.

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Li, Guangxia, Peilin Zhao, Tao Mei, Peng Yang, Yulong Shen, Julian Kuiyu Chang, and Steven C. H. Hoi. "Collaborative online ranking algorithms for multitask learning." Knowledge and Information Systems 62, no. 6 (October 15, 2019): 2327–48. http://dx.doi.org/10.1007/s10115-019-01406-6.

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Hafeez, Farrukh, Usman Ullah Sheikh, Asif Iqbal, and Muhammad Naveed Aman. "Incoherent and Online Dictionary Learning Algorithm for Motion Prediction." Electronics 11, no. 21 (October 29, 2022): 3525. http://dx.doi.org/10.3390/electronics11213525.

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Accurate model development and efficient representations of multivariate trajectories are crucial to understanding the behavioral patterns of pedestrian motion. Most of the existing algorithms use offline learning approaches to learn such motion behaviors. However, these approaches cannot take advantage of the streams of data that are available after training has concluded, and typically are not generalizable to data that they have not seen before. To solve this problem, this paper proposes two algorithms for learning incoherent dictionaries in an offline and online manner by extending the offline augmented semi-non-negative sparse coding (ASNSC) algorithm. We do this by adding a penalty into the objective function to promote dictionary incoherence. A trajectory-modeling application is studied, where we consider the learned atoms of the dictionary as local motion primitives. We use real-world datasets to show that the dictionaries trained by the proposed algorithms have enhanced representation ability and converge quickly as compared to ASNSC. Moreover, the trained dictionaries are well conditioned. In terms of pedestrian trajectory prediction, the proposed methods are shown to be on par (and often better) with the state-of-the-art algorithms in pedestrian trajectory prediction.

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Mu, Tong, Georgios Theocharous, David Arbour, and Emma Brunskill. "Constraint Sampling Reinforcement Learning: Incorporating Expertise for Faster Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (June 28, 2022): 7841–49. http://dx.doi.org/10.1609/aaai.v36i7.20753.

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Online reinforcement learning (RL) algorithms are often difficult to deploy in complex human-facing applications as they may learn slowly and have poor early performance. To address this, we introduce a practical algorithm for incorporating human insight to speed learning. Our algorithm, Constraint Sampling Reinforcement Learning (CSRL), incorporates prior domain knowledge as constraints/restrictions on the RL policy. It takes in multiple potential policy constraints to maintain robustness to misspecification of individual constraints while leveraging helpful ones to learn quickly. Given a base RL learning algorithm (ex. UCRL, DQN, Rainbow) we propose an upper confidence with elimination scheme that leverages the relationship between the constraints, and their observed performance, to adaptively switch among them. We instantiate our algorithm with DQN-type algorithms and UCRL as base algorithms, and evaluate our algorithm in four environments, including three simulators based on real data: recommendations, educational activity sequencing, and HIV treatment sequencing. In all cases, CSRL learns a good policy faster than baselines.

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Kumar, Anubhav, Dileep Kumar M, Víctor Daniel Jiménez Macedo, B. R. Mohan, and Achyutha Prasad N. "Machine Learning Approach for Prediction of the Online User Intention for a Product Purchase." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 1s (January 16, 2023): 43–51. http://dx.doi.org/10.17762/ijritcc.v11i1s.5992.

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The deployment of self-learning computer algorithms that can automatically enhance their performance via experience is referred to as machine learning in ecommerce and is a crucial trend of the retail digital transformation. Machine learning algorithms can be unambiguously trained by analysing big datasets, identifying repeating patterns, relationships, and anomalies among all of this data, and creating mathematical models resembling such associations. These models are improved when the algorithms analyse ever-increasing amounts of data, providing us with useful insights into specific ecommerce-related events and the links between all the variables that underlie them. A tool that has been quite effective in studying current affairs, predicting future trends, and making data-driven decisions. The present work investigates the implementation of machine learning algorithms to predict the user intention for purchasing a product on a specific store's website. An Online Shoppers Purchasing Intention data set from the UC Irvine Machine Learning Repository was used for this investigation. In this study, two classification-based machine learning algorithms i.e. Stochastic Gradient Descent (SGD) algorithm and Random Forest algorithm were used. SGD algorithm was used for first time in prediction of the online user intention. The results showed that the Random Forest resulted in the highest F1-Score of 0.90 in contrast to the Stochastic Gradient Descent algorithm.

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Lin, Junhong, and Ding-Xuan Zhou. "Online Learning Algorithms Can Converge Comparably Fast as Batch Learning." IEEE Transactions on Neural Networks and Learning Systems 29, no. 6 (June 2018): 2367–78. http://dx.doi.org/10.1109/tnnls.2017.2677970.

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Pham, D. T., and A. A. Afify. "Online Discretization of Continuous-Valued Attributes in Rule Induction." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 219, no. 8 (August 1, 2005): 829–42. http://dx.doi.org/10.1243/095440605x31571.

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Machine learning algorithms designed for engineering applications must be able to handle numerical attributes, particularly attributes with real (or continuous) values. Many algorithms deal with continuous-valued attributes by discretizing them before starting the learning process. This paper describes a new approach for discretization of continuous-valued attributes during the learning process. Incorporating discretization within the learning process has the advantage of taking into account the bias inherent in the learning system as well as the interactions between the different attributes. Experiments have demonstrated that the proposed method, when used in conjunction with the SRI rule induction algorithm developed by the authors, improves the accuracy of the induced model.

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Yan, Ruixia, Zhijie Xia, Yanxi Xie, Xiaoli Wang, and Zukang Song. "Research on Sentiment Classification Algorithms on Online Review." Complexity 2020 (September 8, 2020): 1–6. http://dx.doi.org/10.1155/2020/5093620.

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The product online review text contains a large number of opinions and emotions. In order to identify the public’s emotional and tendentious information, we present reinforcement learning models in which sentiment classification algorithms of product online review corpus are discussed in this paper. In order to explore the classification effect of different sentiment classification algorithms, we conducted a research on Naive Bayesian algorithm, support vector machine algorithm, and neural network algorithm and carried out some comparison using a concrete example. The evaluation indexes and the three algorithms are compared in different lengths of sentence and word vector dimensions. The results present that neural network algorithm is effective in the sentiment classification of product online review corpus.

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Huai, Mengdi, Di Wang, Chenglin Miao, Jinhui Xu, and Aidong Zhang. "Pairwise Learning with Differential Privacy Guarantees." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 694–701. http://dx.doi.org/10.1609/aaai.v34i01.5411.

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Pairwise learning has received much attention recently as it is more capable of modeling the relative relationship between pairs of samples. Many machine learning tasks can be categorized as pairwise learning, such as AUC maximization and metric learning. Existing techniques for pairwise learning all fail to take into consideration a critical issue in their design, i.e., the protection of sensitive information in the training set. Models learned by such algorithms can implicitly memorize the details of sensitive information, which offers opportunity for malicious parties to infer it from the learned models. To address this challenging issue, in this paper, we propose several differentially private pairwise learning algorithms for both online and offline settings. Specifically, for the online setting, we first introduce a differentially private algorithm (called OnPairStrC) for strongly convex loss functions. Then, we extend this algorithm to general convex loss functions and give another differentially private algorithm (called OnPairC). For the offline setting, we also present two differentially private algorithms (called OffPairStrC and OffPairC) for strongly and general convex loss functions, respectively. These proposed algorithms can not only learn the model effectively from the data but also provide strong privacy protection guarantee for sensitive information in the training set. Extensive experiments on real-world datasets are conducted to evaluate the proposed algorithms and the experimental results support our theoretical analysis.

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Junfei Li, and Xiaomin Chen. "Online Learning Algorithms of Direct Support Vector Machine." International Journal of Advancements in Computing Technology 3, no. 11 (December 31, 2011): 486–97. http://dx.doi.org/10.4156/ijact.vol3.issue11.60.

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Chen, Xiaming, Bohao Tang, Jun Fan, and Xin Guo. "Online gradient descent algorithms for functional data learning." Journal of Complexity 70 (June 2022): 101635. http://dx.doi.org/10.1016/j.jco.2021.101635.

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Buchbinder, Niv, Shahar Chen, Joseph (Seffi) Naor, and Ohad Shamir. "Unified Algorithms for Online Learning and Competitive Analysis." Mathematics of Operations Research 41, no. 2 (May 2016): 612–25. http://dx.doi.org/10.1287/moor.2015.0742.

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Demir, G. K., and K. Ozmehmet. "Online local learning algorithms for linear discriminant analysis." Pattern Recognition Letters 26, no. 4 (March 2005): 421–31. http://dx.doi.org/10.1016/j.patrec.2004.08.005.

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He, Wenwu, and James T. Kwok. "Simple randomized algorithms for online learning with kernels." Neural Networks 60 (December 2014): 17–24. http://dx.doi.org/10.1016/j.neunet.2014.07.006.

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Wu, Yue, Steven C. H. Hoi, Chenghao Liu, Jing Lu, Doyen Sahoo, and Nenghai Yu. "SOL: A library for scalable online learning algorithms." Neurocomputing 260 (October 2017): 9–12. http://dx.doi.org/10.1016/j.neucom.2017.03.077.

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Lin, Junhong, Yunwen Lei, Bo Zhang, and Ding-Xuan Zhou. "Online pairwise learning algorithms with convex loss functions." Information Sciences 406-407 (September 2017): 57–70. http://dx.doi.org/10.1016/j.ins.2017.04.022.

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Guo, Zheng-Chu, and Lei Shi. "Fast and strong convergence of online learning algorithms." Advances in Computational Mathematics 45, no. 5-6 (June 6, 2019): 2745–70. http://dx.doi.org/10.1007/s10444-019-09707-8.

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Shalev-Shwartz, Shai, and Yoram Singer. "A primal-dual perspective of online learning algorithms." Machine Learning 69, no. 2-3 (July 11, 2007): 115–42. http://dx.doi.org/10.1007/s10994-007-5014-x.

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Ning, Hanwen, Jiaming Zhang, Ting-Ting Feng, Eric King-wah Chu, and Tianhai Tian. "Control-based algorithms for high dimensional online learning." Journal of the Franklin Institute 357, no. 3 (February 2020): 1909–42. http://dx.doi.org/10.1016/j.jfranklin.2019.12.039.

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Aravindh, A., S. S. Shiju, and S. Sumitra. "Kernel collaborative online algorithms for multi-task learning." Annals of Mathematics and Artificial Intelligence 86, no. 4 (August 8, 2019): 269–86. http://dx.doi.org/10.1007/s10472-019-09650-w.

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Ying, Yiming, and Ding-Xuan Zhou. "Unregularized online learning algorithms with general loss functions." Applied and Computational Harmonic Analysis 42, no. 2 (March 2017): 224–44. http://dx.doi.org/10.1016/j.acha.2015.08.007.

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Yao, Zhengyu, Hwan-Sik Yoon, and Yang-Ki Hong. "Control of Hybrid Electric Vehicle Powertrain Using Offline-Online Hybrid Reinforcement Learning." Energies 16, no. 2 (January 5, 2023): 652. http://dx.doi.org/10.3390/en16020652.

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Hybrid electric vehicles can achieve better fuel economy than conventional vehicles by utilizing multiple power sources. While these power sources have been controlled by rule-based or optimization-based control algorithms, recent studies have shown that machine learning-based control algorithms such as online Deep Reinforcement Learning (DRL) can effectively control the power sources as well. However, the optimization and training processes for the online DRL-based powertrain control strategy can be very time and resource intensive. In this paper, a new offline–online hybrid DRL strategy is presented where offline vehicle data are exploited to build an initial model and an online learning algorithm explores a new control policy to further improve the fuel economy. In this manner, it is expected that the agent can learn an environment consisting of the vehicle dynamics in a given driving condition more quickly compared to the online algorithms, which learn the optimal control policy by interacting with the vehicle model from zero initial knowledge. By incorporating a priori offline knowledge, the simulation results show that the proposed approach not only accelerates the learning process and makes the learning process more stable, but also leads to a better fuel economy compared to online only learning algorithms.

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Jian, Ling, Fuhao Gao, Peng Ren, Yunquan Song, and Shihua Luo. "A Noise-Resilient Online Learning Algorithm for Scene Classification." Remote Sensing 10, no. 11 (November 20, 2018): 1836. http://dx.doi.org/10.3390/rs10111836.

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The proliferation of remote sensing imagery motivates a surge of research interest in image processing such as feature extraction and scene recognition, etc. Among them, scene recognition (classification) is a typical learning task that focuses on exploiting annotated images to infer the category of an unlabeled image. Existing scene classification algorithms predominantly focus on static data and are designed to learn discriminant information from clean data. They, however, suffer from two major shortcomings, i.e., the noisy label may negatively affect the learning procedure and learning from scratch may lead to a huge computational burden. Thus, they are not able to handle large-scale remote sensing images, in terms of both recognition accuracy and computational cost. To address this problem, in the paper, we propose a noise-resilient online classification algorithm, which is scalable and robust to noisy labels. Specifically, ramp loss is employed as loss function to alleviate the negative affect of noisy labels, and we iteratively optimize the decision function in Reproducing Kernel Hilbert Space under the framework of Online Gradient Descent (OGD). Experiments on both synthetic and real-world data sets demonstrate that the proposed noise-resilient online classification algorithm is more robust and sparser than state-of-the-art online classification algorithms.

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Liu, Yanbin, Yan Yan, Ling Chen, Yahong Han, and Yi Yang. "Adaptive Sparse Confidence-Weighted Learning for Online Feature Selection." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4408–15. http://dx.doi.org/10.1609/aaai.v33i01.33014408.

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In this paper, we propose a new online feature selection algorithm for streaming data. We aim to focus on the following two problems which remain unaddressed in literature. First, most existing online feature selection algorithms merely utilize the first-order information of the data streams, regardless of the fact that second-order information explores the correlations between features and significantly improves the performance. Second, most online feature selection algorithms are based on the balanced data presumption, which is not true in many real-world applications. For example, in fraud detection, the number of positive examples are much less than negative examples because most cases are not fraud. The balanced assumption will make the selected features biased towards the majority class and fail to detect the fraud cases. We propose an Adaptive Sparse Confidence-Weighted (ASCW) algorithm to solve the aforementioned two problems. We first introduce an `0-norm constraint into the second-order confidence-weighted (CW) learning for feature selection. Then the original loss is substituted with a cost-sensitive loss function to address the imbalanced data issue. Furthermore, our algorithm maintains multiple sparse CW learner with the corresponding cost vector to dynamically select an optimal cost. We theoretically enhance the theory of sparse CW learning and analyze the performance behavior in F-measure. Empirical studies show the superior performance over the stateof-the-art online learning methods in the online-batch setting.

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Zhang, Maojun, Cuiqing Zhang, Xijun Liang, Zhonghang Xia, Ling Jian, and Jiangxia Nan. "A noise-resilient online learning algorithm with ramp loss for ordinal regression." Intelligent Data Analysis 26, no. 2 (March 14, 2022): 379–405. http://dx.doi.org/10.3233/ida-205613.

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Ordinal regression has been widely used in applications, such as credit portfolio management, recommendation systems, and ecology, where the core task is to predict the labels on ordinal scales. Due to its learning efficiency, online ordinal regression using passive aggressive (PA) algorithms has gained a much attention for solving large-scale ranking problems. However, the PA method is sensitive to noise especially in the scenario of streaming data, where the ranking of data samples may change dramatically. In this paper, we propose a noise-resilient online learning algorithm using the Ramp loss function, called PA-RAMP, to improve the performance of PA method for noisy data streams. Also, we validate the order preservation of thresholds of the proposed algorithm. Experiments on real-world data sets demonstrate that the proposed noise-resilient online ordinal regression algorithm is more robust and efficient than state-of-the-art online ordinal regression algorithms.

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Wu, Qingyuan, Changchen Zhan, Fu Lee Wang, Siyang Wang, and Zeping Tang. "Clustering of online learning resources via minimum spanning tree." Asian Association of Open Universities Journal 11, no. 2 (September 5, 2016): 197–215. http://dx.doi.org/10.1108/aaouj-09-2016-0036.

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Purpose The quick growth of web-based and mobile e-learning applications such as massive open online courses have created a large volume of online learning resources. Confronting such a large amount of learning data, it is important to develop effective clustering approaches for user group modeling and intelligent tutoring. The paper aims to discuss these issues. Design/methodology/approach In this paper, a minimum spanning tree based approach is proposed for clustering of online learning resources. The novel clustering approach has two main stages, namely, elimination stage and construction stage. During the elimination stage, the Euclidean distance is adopted as a metrics formula to measure density of learning resources. Resources with quite low densities are identified as outliers and therefore removed. During the construction stage, a minimum spanning tree is built by initializing the centroids according to the degree of freedom of the resources. Online learning resources are subsequently partitioned into clusters by exploiting the structure of minimum spanning tree. Findings Conventional clustering algorithms have a number of shortcomings such that they cannot handle online learning resources effectively. On the one hand, extant partitional clustering methods use a randomly assigned centroid for each cluster, which usually cause the problem of ineffective clustering results. On the other hand, classical density-based clustering methods are very computationally expensive and time-consuming. Experimental results indicate that the algorithm proposed outperforms the traditional clustering algorithms for online learning resources. Originality/value The effectiveness of the proposed algorithms has been validated by using several data sets. Moreover, the proposed clustering algorithm has great potential in e-learning applications. It has been demonstrated how the novel technique can be integrated in various e-learning systems. For example, the clustering technique can classify learners into groups so that homogeneous grouping can improve the effectiveness of learning. Moreover, clustering of online learning resources is valuable to decision making in terms of tutorial strategies and instructional design for intelligent tutoring. Lastly, a number of directions for future research have been identified in the study.

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Anita, C. S., P. Nagarajan, G. Aditya Sairam, P. Ganesh, and G. Deepakkumar. "Fake Job Detection and Analysis Using Machine Learning and Deep Learning Algorithms." Revista Gestão Inovação e Tecnologias 11, no. 2 (June 5, 2021): 642–50. http://dx.doi.org/10.47059/revistageintec.v11i2.1701.

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With the pandemic situation, there is a strong rise in the number of online jobs posted on the internet in various job portals. But some of the jobs being posted online are actually fake jobs which lead to a theft of personal information and vital information. Thus, these fake jobs can be precisely detected and classified from a pool of job posts of both fake and real jobs by using advanced deep learning as well as machine learning classification algorithms. In this paper, machine learning and deep learning algorithms are used so as to detect fake jobs and to differentiate them from real jobs. The data analysis part and data cleaning part are also proposed in this paper, so that the classification algorithm applied is highly precise and accurate. It has to be noted that the data cleaning step is a very important step in machine learning project because it actually determines the accuracy of the machine learning as well as deep learning algorithms. Hence a great importance is emphasized on data cleaning and pre-processing step in this paper. The classification and detection of fake jobs can be done with high accuracy and high precision. Hence the machine learning and deep learning algorithms have to be applied on cleaned and pre-processed data in order to achieve a better accuracy. Further, deep learning neural networks are used so as to achieve higher accuracy. Finally all these classification models are compared with each other to find the classification algorithm with highest accuracy and precision.

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Li, Chang. "Optimizing ranking systems online as bandits." ACM SIGIR Forum 55, no. 2 (December 2021): 1–2. http://dx.doi.org/10.1145/3527546.3527575.

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Ranking system is the core part of modern retrieval and recommender systems, where the goal is to rank candidate items given user contexts. Optimizing ranking systems online means that the deployed system can serve users' requests, e.g., queries in the web search, and optimize the ranking policy by learning from user interactions, e.g., clicks. Bandit is a general online learning framework and can be used in our optimization task. However, due to the unique features of ranking, there are several challenges in designing bandit algorithms for ranking system optimization. In this thesis, we study and propose bandit algorithms for four challenges in optimizing ranking systems online: effectiveness, safety, nonstationarity, and diversification. We first focus on the large-scale online ranker evaluation problem. The challenge is that the number of pair-wise ranker comparisons grows quadratically with respect to the number of rankers. We proposed the merge double Thompson sampling (MergeDTS) method to solve the problem. MergeDTS takes the divide-and-conquer idea in Merge Sort to decrease the complexity and uses the Thompson sampling to increase the effectiveness of pair-wise comparisons. We then address the safety Online Learning to Rank (OLTR) by introducing the BubbleRank algorithm. BubbleRank uses the offline trained ranker, e.g., the production ranker, to obtain the initial ranked list, and then conducts safe online pairwise exploration to improve this list. The safety comes from the fact that BubbleRank explores the ranked lists by randomly exchanging items with their neighbors. Thus, during exploration, low-quality items are hardly shifted at top positions. Non-stationarity widely appears in interactive systems since user preferences are affected by different factors and change over time. It is critical to design algorithms that capture the non-stationarity in OLTR. This thesis provides CascadeDUCB and CascadeSWUCB algorithms to solve the non-stationary OLTR. We derive gap-dependent bounds on their regret and show the theoretical soundness of the proposed algorithms, and then we conduct simulated experiments to validate the empirical effectiveness. Result diversification and relevance ranking are two important aspects in modern recommender systems. Ideal learning algorithms should be able to display a ranked list whose items are relevant and the topics of items are diverse. The last research chapter of the thesis focuses on this challenge and provides the CascadeHybrid algorithm. CascadeHybrid learns from interactive feedback online and trained a ranker, which is a hybrid of a linear function capturing the relevance part and a submodular function responding to the results diversification. Awarded by : University of Amsterdam, Amsterdam, the Netherlands on 4 March 2021. Supervised by : Maarten de Rijke. Available at : https://dare.uva.nl/search?identifier=f043b9b4-e666-48e0-8a6c-7c5431660e17.

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Chae, Byungjoo, Jinsun Park, Tae-Hyun Kim, and Donghyeon Cho. "Online Learning for Reference-Based Super-Resolution." Electronics 11, no. 7 (March 28, 2022): 1064. http://dx.doi.org/10.3390/electronics11071064.

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Online learning is a method for exploiting input data to update deep networks in the test stage to derive potential performance improvement. Existing online learning methods for single-image super-resolution (SISR) utilize an input low-resolution (LR) image for the online adaptation of deep networks. Unlike SISR approaches, reference-based super-resolution (RefSR) algorithms benefit from an additional high-resolution (HR) reference image containing plenty of useful features for enhancing the input LR image. Therefore, we introduce a new online learning algorithm, using several reference images, which is applicable to not only RefSR but also SISR networks. Experimental results show that our online learning method is seamlessly applicable to many existing RefSR and SISR models, and that improves performance. We further present the robustness of our method to non-bicubic degradation kernels with in-depth analyses.

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Journal articles on the topic 'Online learning algorithms'

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