Results 31 to 40 of about 1,247,707 (283)
An Online Minorization-Maximization Algorithm
, 2023 AbstractModern statistical and machine learning settings often involve high data volume and data streaming, which require the development of online estimation algorithms. The online Expectation–Maximization (EM) algorithm extends the popular EM algorithm to this setting, via a stochastic approximation approach.We show that an online version of the ...
Nguyen, Hien +3 more
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Online Mixed Packing and Covering [PDF]
, 2012 In many problems, the inputs arrive over time, and must be dealt with irrevocably when they arrive. Such problems are online problems. A common method of solving online problems is to first solve the corresponding linear program, and then round the ...
Bhaskar, Umang, Fleischer, Lisa
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Landscape and Taxonomy of Online Parser-Supported Log Anomaly Detection Methods
IEEE AccessAs production system estates become larger and more complex, ensuring stability through traditional monitoring approaches becomes more challenging. Rule-based monitoring is common in industrial settings, but it has limitations.
Scott Lupton +3 more
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Randomized Competitive Analysis for Two Server Problems
Algorithms, 2008 We prove that there exists a randomized online algorithm for the 2-server 3-point problem whose expected competitive ratio is at most 1.5897. This is the first nontrivial upper bound for randomized k-server algorithms in a general metric space whose ...
Jun Kawahara, Kazuo Iwama, Wolfgang Bein
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EURO Journal on Computational Optimization, 2015 In a k-min search problem, a player wants to buy k units of an asset with the objective of minimizing the total buying cost. At each time period t, a price qt is observed, and the player has to decide on the number of units to buy without any knowledge ...
Javeria Iqbal, Iftikhar Ahmad
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The Scary Black Box: AI Driven Recommender Algorithms as The Most Powerful Social Force
Etnoantropološki Problemi, 2022 Recommender algorithms shape societies by individually exposing online users to everything they see, hear and feel in real time. We examine the development of recommender algorithms from the Page Rank and advertising platforms to social media trending ...
Ljubiša Bojić +2 more
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Online Learning With Inexact Proximal Online Gradient Descent Algorithms [PDF]
IEEE Transactions on Signal Processing, 2019 We consider non-differentiable dynamic optimization problems such as those arising in robotics and subspace tracking. Given the computational constraints and the time-varying nature of the problem, a low-complexity algorithm is desirable, while the accuracy of the solution may only increase slowly over time.
Rishabh Dixit +3 more
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Online Algorithm Selection [PDF]
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017 Algorithm selection approaches have achieved impressive performance improvements in many areas of AI. Most of the literature considers the offline algorithm selection problem, where the initial selection model is never updated after training. However, new data from running algorithms on instances becomes available while an algorithm selection method is
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Competitive Algorithms for Online Pricing [PDF]
, 2011 Given a seller with m amount of items, a sequence of users {u1, u2, ...} come one by one, the seller must set the unit price and assign some amount of items to each user on his/her arrival. Items can be sold fractionally. Each ui has his/her value function vi(ċ) such that vi(x) is the highest unit price ui is willing to pay for x items.
Zhang, Y, Chin, FYL, Ting, HF
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Control of Hybrid Electric Vehicle Powertrain Using Offline-Online Hybrid Reinforcement Learning
Energies, 2023 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
Zhengyu Yao, Hwan-Sik Yoon, Yang-Ki Hong
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