Relevant bibliographies by topics / Online algorithm with advice

Academic literature on the topic 'Online algorithm with advice'

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Published: 25 May 2024

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Journal articles on the topic "Online algorithm with advice"

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Lee, Russell, Jessica Maghakian, Mohammad Hajiesmaili, Jian Li, Ramesh Sitaraman, and Zhenhua Liu. "Online peak-aware energy scheduling with untrusted advice." ACM SIGEnergy Energy Informatics Review 1, no. 1 (November 2021): 59–77. http://dx.doi.org/10.1145/3508467.3508473.

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This paper studies the online energy scheduling problem in a hybrid model where the cost of energy is proportional to both the volume and peak usage, and where energy can be either locally generated or drawn from the grid. Inspired by recent advances in online algorithms with Machine Learned (ML) advice, we develop parameterized deterministic and randomized algorithms for this problem such that the level of reliance on the advice can be adjusted by a trust parameter. We then analyze the performance of the proposed algorithms using two performance metrics: robustness that measures the competitive ratio as a function of the trust parameter when the advice is inaccurate, and consistency for competitive ratio when the advice is accurate. Since the competitive ratio is analyzed in two different regimes, we further investigate the Pareto optimality of the proposed algorithms. Our results show that the proposed deterministic algorithm is Pareto-optimal, in the sense that no other online deterministic algorithms can dominate the robustness and consistency of our algorithm. Furthermore, we show that the proposed randomized algorithm dominates the Pareto-optimal deterministic algorithm. Our large-scale empirical evaluations using real traces of energy demand, energy prices, and renewable energy generations highlight that the proposed algorithms outperform worst-case optimized algorithms and fully data-driven algorithms.
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Bianchi, Maria Paola, Hans-Joachim Böckenhauer, Tatjana Brülisauer, Dennis Komm, and Beatrice Palano. "Online Minimum Spanning Tree with Advice." International Journal of Foundations of Computer Science 29, no. 04 (June 2018): 505–27. http://dx.doi.org/10.1142/s0129054118410034.

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In the online minimum spanning tree problem, a graph is revealed vertex by vertex; together with every vertex, all edges to vertices that are already known are given, and an online algorithm must irrevocably choose a subset of them as a part of its solution. The advice complexity of an online problem is a means to quantify the information that needs to be extracted from the input to achieve good results. For a graph of size [Formula: see text], we show an asymptotically tight bound of [Formula: see text] on the number of advice bits to produce an optimal solution for any given graph. For particular graph classes, e.g., with bounded degree or a restricted edge weight function, we prove that the upper bound can be drastically reduced; e.g., [Formula: see text] advice bits allow to compute an optimal result if the weight function equals the Euclidean distance; if the graph is complete and has two different edge weights, even a logarithmic number suffices. Some of these results make use of the optimality of Kruskal’s algorithm for the offline setting. We also study the trade-off between the number of advice bits and the achievable competitive ratio. To this end, we perform a reduction from another online problem to obtain a linear lower bound on the advice complexity for any near-optimal solution. Using our results finally allows us to give a lower bound on the expected competitive ratio of any randomized online algorithm for the problem, even on graphs with three different edge weights.
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Boyar, Joan, Lene M. Favrholdt, Christian Kudahl, Kim S. Larsen, and Jesper W. Mikkelsen. "Online Algorithms with Advice." ACM Computing Surveys 50, no. 2 (June 19, 2017): 1–34. http://dx.doi.org/10.1145/3056461.

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Barrière, Lali, Xavier Muñoz, Janosch Fuchs, and Walter Unger. "Online Matching in Regular Bipartite Graphs." Parallel Processing Letters 28, no. 02 (June 2018): 1850008. http://dx.doi.org/10.1142/s0129626418500081.

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In an online problem, the input is revealed one piece at a time. In every time step, the online algorithm has to produce a part of the output, based on the partial knowledge of the input. Such decisions are irrevocable, and thus online algorithms usually lead to nonoptimal solutions. The impact of the partial knowledge depends strongly on the problem. If the algorithm is allowed to read binary information about the future, the amount of bits read that allow the algorithm to solve the problem optimally is the so-called advice complexity. The quality of an online algorithm is measured by its competitive ratio, which compares its performance to that of an optimal offline algorithm. In this paper we study online bipartite matchings focusing on the particular case of bipartite matchings in regular graphs. We give tight upper and lower bounds on the competitive ratio of the online deterministic bipartite matching problem. The competitive ratio turns out to be asymptotically equal to the known randomized competitive ratio. Afterwards, we present an upper and lower bound for the advice complexity of the online deterministic bipartite matching problem.
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Chen, Li-Hsuan, Ling-Ju Hung, Henri Lotze, and Peter Rossmanith. "Online Node- and Edge-Deletion Problems with Advice." Algorithmica 83, no. 9 (June 30, 2021): 2719–53. http://dx.doi.org/10.1007/s00453-021-00840-9.

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AbstractIn online edge- and node-deletion problems the input arrives node by node and an algorithm has to delete nodes or edges in order to keep the input graph in a given graph class $$\Pi $$ Π at all times. We consider only hereditary properties $$\Pi $$ Π , for which optimal online algorithms exist and which can be characterized by a set of forbidden subgraphs $${{\mathcal{F}}}$$ F and analyze the advice complexity of getting an optimal solution. We give almost tight bounds on the Delayed Connected$${{\mathcal{F}}}$$ F -Node-Deletion Problem, where all graphs of the family $${\mathcal{F}}$$ F have to be connected and almost tight lower and upper bounds for the Delayed$$H$$ H -Node-Deletion Problem, where there is one forbidden induced subgraph H that may be connected or not. For the Delayed$$H$$ H -Node-Deletion Problem the advice complexity is basically an easy function of the size of the biggest component in H. Additionally, we give tight bounds on the Delayed Connected$${\mathcal{F}}$$ F -Edge-Deletion Problem, where we have an arbitrary number of forbidden connected graphs. For the latter result we present an algorithm that computes the advice complexity directly from $${\mathcal{F}}$$ F . We give a separate analysis for the Delayed Connected$$H$$ H -Edge-Deletion Problem, which is less general but admits a bound that is easier to compute.
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Lykouris, Thodoris, and Sergei Vassilvitskii. "Competitive Caching with Machine Learned Advice." Journal of the ACM 68, no. 4 (July 7, 2021): 1–25. http://dx.doi.org/10.1145/3447579.

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Traditional online algorithms encapsulate decision making under uncertainty, and give ways to hedge against all possible future events, while guaranteeing a nearly optimal solution, as compared to an offline optimum. On the other hand, machine learning algorithms are in the business of extrapolating patterns found in the data to predict the future, and usually come with strong guarantees on the expected generalization error. In this work, we develop a framework for augmenting online algorithms with a machine learned predictor to achieve competitive ratios that provably improve upon unconditional worst-case lower bounds when the predictor has low error. Our approach treats the predictor as a complete black box and is not dependent on its inner workings or the exact distribution of its errors. We apply this framework to the traditional caching problem—creating an eviction strategy for a cache of size k . We demonstrate that naively following the oracle’s recommendations may lead to very poor performance, even when the average error is quite low. Instead, we show how to modify the Marker algorithm to take into account the predictions and prove that this combined approach achieves a competitive ratio that both (i) decreases as the predictor’s error decreases and (ii) is always capped by O (log k ), which can be achieved without any assistance from the predictor. We complement our results with an empirical evaluation of our algorithm on real-world datasets and show that it performs well empirically even when using simple off-the-shelf predictions.
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Boyar, Joan, Lene M. Favrholdt, Christian Kudahl, Kim S. Larsen, and Jesper W. Mikkelsen. "Online Algorithms with Advice: A Survey." ACM SIGACT News 47, no. 3 (August 31, 2016): 93–129. http://dx.doi.org/10.1145/2993749.2993766.

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Böckenhauer, Hans-Joachim, Dennis Komm, Rastislav Královič, Richard Královič, and Tobias Mömke. "Online algorithms with advice: The tape model." Information and Computation 254 (June 2017): 59–83. http://dx.doi.org/10.1016/j.ic.2017.03.001.

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Aumayr, Erik, Jeffrey Chan, and Conor Hayes. "Reconstruction of Threaded Conversations in Online Discussion Forums." Proceedings of the International AAAI Conference on Web and Social Media 5, no. 1 (August 3, 2021): 26–33. http://dx.doi.org/10.1609/icwsm.v5i1.14122.

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Online discussion boards, or Internet forums, are a significant part of the Internet. People use Internet forums to post questions, provide advice and participate in discussions. These online conversations are represented as threads, and the conversation trees within these threads are important in understanding the behaviour of online users. Unfortunately, the reply structures of these threads are generally not publicly accessible or not maintained. Hence, in this paper, we introduce an efficient and simple approach to reconstruct the reply structure in threaded conversations. We contrast its accuracy against three baseline algorithms, and show that our algorithm can accurately recreate the in and out degree distributions of forum reply graphs built from the reconstructed reply structures.
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Zhao, Xiaofan, and Hong Shen. "Online algorithms for 2D bin packing with advice." Neurocomputing 189 (May 2016): 25–32. http://dx.doi.org/10.1016/j.neucom.2015.11.035.

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Dissertations / Theses on the topic "Online algorithm with advice"

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Renault, Marc Paul. "Lower and upper bounds for online algorithms with advice." Paris 7, 2014. http://www.theses.fr/2014PA077196.

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Les algorithmes en ligne fonctionnent dans un contexte où l'entrée est révélé au fur et à mesure du temps; chaque morceau révélé est appelé une demande. Après réception de chaque demahde, les algorithmes en ligne doivent prendre une action avant que la prochaine demande soit révélée, c'est-à-dire que les algorithmes en ligne doivent prendre une décision irrévocable basée sur les demandes déjà révélées sans aucune connaissance des demandes à venir. Le but est d'optimiser une fonction de coût dépendante de l'entrée. L'analyse compétitive est la méthode standard utilisée pour analyser la qualité des algorithmes en ligne. Le ratio compétitif est un ratio de pire cas, parmi toutes les séquences de demande finis, entre la performance de l'algorithme en ligne contre un algorithme optimal hors ligne pour la même séquence. Le ratio compétitif compare la performance d'un algorithme sans aucune connaissance de l'avenir contre un algorithme en pleine connaissance de l'avenir. Car l'absence totale de connaissance de l'avenir n'est souvent pas une hypothèse raisonnable, des modèles ont été proposés, appelés algorithmes en ligne avec conseil, qui donne les algorithmes en ligne l'accès à une quantité quantifiée des connaissances de l'avenir. L'intérêt de ce modèle est d'examiner comment le ratio compétitif change en fonction de la quantité de conseil. Dans cette thèse, il est présenté des bornes supérieures et inférieures dans ce modèle pour des problèmes en ligne classiques, tels que le problème de la k-serveur, de bin packing, de dual bin packing (sac à dos multiple), d'ordonnancement sur m machines identiques, du tampon de réordonnancement et de la mise à jour de la liste
Online algorithms operate in a setting where the input is revealed piece by piece; the pieces are called requests. After receiving each request, online algorithms must take an action before the next request is revealed, i. E. Online algorithms must make irrevocable decisions based on the input revealed so far without any knowledge of the future input. The goal is to optimize some cost function over the input. Competitive analysis is the standard method used to analyse the quality of online algorithms. The competitive ratio is the worst case ratio, over all valid finite request sequences, of the online algorithm's performance against an optimal offline algorithm for the same request sequence. The competitive ratio compares the performance of an algorithm with no knowledge about the future against an algorithm with full knowledge about the future. Since the complete absence of future knowledge is often not a reasonable assumption, models, termed online algorithms with advice, which give the online algorithms access to a quantified amount of future knowledge, have been proposed. The interest in this model is in examining how the competitive ratio changes as a function of the amount of advice. In this thesis, we present upper and lower bounds in the advice model for classical online problems such as the k-server problem, the bin packing problem, the dual bin packing (multiple knapsack) problem, scheduling problem on m identical machines, the reordering buffer management problem and the list update problem
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Jin, Shendan. "Online computation beyond standard models." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS152.

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Dans le cadre standard du calcul en ligne, l’entrée de l’algorithme n’est pas entièrement connue à l’avance, mais elle est révélée progressivement sous forme d’une séquence de requêtes. Chaque fois qu'une requête arrive, l'algorithme en ligne doit prendre des décisions irrévocables pour servir la demande, sans connaissance des requêtes futures. Dans le domaine des algorithmes en ligne, le cadre standard utilisé pour évaluer les performances des algorithmes en ligne est l’analyse compétitive. De manière informelle, le concept d’analyse compétitive consiste à comparer les performances d’un algorithme en ligne dans le pire des cas à une solution optimale hors ligne qui aurait pu être calculée si toutes les données étaient connues d’avance. Dans cette thèse, nous étudierons de nouvelles façons d'approcher les problèmes en ligne. Dans un premier temps, nous étudions le calcul en ligne dans le modèle avec ré-optimisation, dans lequel l'irrévocabilité des décisions en ligne est relâchée. Autrement dit, l'algorithme en ligne est autorisé à revenir en arrière et changer les décisions précédemment prises. Plus précisément, nous montrons comment identifier le compromis entre le nombre de ré-optimisation et les performances des algorithmes en ligne pour le problème de couplage maximale en ligne. De plus, nous étudions des mesures autres que l'analyse compétitive pour évaluer les performances des algorithmes en ligne. Nous observons que parfois, l'analyse compétitive ne peut pas distinguer les performances de différents algorithmes en raison de la nature la plus défavorable du ratio de compétitivité. Nous démontrons qu'une situation similaire se pose dans le problème de la recherche linéaire. Plus précisément, nous revisitons le problème de la recherche linéaire et introduisons une mesure, qui peut être appliquée comme un raffinement du ratio de compétitivité. Enfin, nous étudions le calcul en ligne dans le modèle avec des conseils, dans lequel l'algorithme reçoit en entrée non seulement une séquence de requêtes, mais aussi quelques conseils sur la séquence de requêtes. Plus précisément, nous étudions un modèle récent avec des conseils non fiables, dans lequel les conseils peuvent être fiables ou non. Supposons que dans ce dernier cas, les conseils peuvent être générés à partir d'une source malveillante. Nous montrons comment identifier une stratégie optimale de Pareto pour le problème online bidding dans le modèle de conseil non fiable
In the standard setting of online computation, the input is not entirely available from the beginning, but is revealed incrementally, piece by piece, as a sequence of requests. Whenever a request arrives, the online algorithm has to make immediately irrevocable decisions to serve the request, without knowledge on the future requests. Usually, the standard framework to evaluate the performance of online algorithms is competitive analysis, which compares the worst-case performance of an online algorithm to an offline optimal solution. In this thesis, we will study some new ways of looking at online problems. First, we study the online computation in the recourse model, in which the irrevocability on online decisions is relaxed. In other words, the online algorithm is allowed to go back and change previously made decisions. More precisely, we show how to identify the trade-off between the number of re-optimization and the performance of online algorithms for the online maximum matching problem. Moreover, we study measures other than competitive analysis for evaluating the performance of online algorithms. We observe that sometimes, competitive analysis cannot distinguish the performance of different algorithms due to the worst-case nature of the competitive ratio. We demonstrate that a similar situation arises in the linear search problem. More precisely, we revisit the linear search problem and introduce a measure, which can be applied as a refinement of the competitive ratio. Last, we study the online computation in the advice model, in which the algorithm receives as input not only a sequence of requests, but also some advice on the request sequence. Specifically, we study a recent model with untrusted advice, in which the advice can be either trusted or untrusted. Assume that in the latter case, the advice can be generated from a malicious source. We show how to identify a Pareto optimal strategy for the online bidding problem in the untrusted advice model
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Cayuela, Rafols Marc. "Algorithmic Study on Prediction with Expert Advice : Study of 3 novel paradigms with Grouped Experts." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254344.

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The main work for this thesis has been a thorough study of the novel Prediction with Partially Monitored Grouped Expert Advice and Side Information paradigm. This is newly proposed in this thesis, and it extends the widely studied Prediction with Expert Advice paradigm. The extension is based on two assumptions and one restriction that modify the original problem. The first assumption, Grouped, presumes that the experts are structured into groups. The second assumption, Side Information, introduces additional information that can be used to timely relate predictions with groups. Finally, the restriction, Partially Monitored, imposes that the groups’ predictions are only known for one group at a time. The study of this paradigm includes the design of a complete prediction algorithm, the proof of a theoretical bound of the worse-case cumulative regret for such algorithm, and an experimental evaluation of the algorithm (proving the existence of cases where this paradigm outperforms Prediction with Expert Advice). Furthermore, since the development of the algorithm is constructive, it allows to easily build two additional prediction algorithms for the Prediction with Grouped Expert Advice and Prediction with Grouped Expert Advice and Side Information paradigms. Therefore, this thesis presents three novel prediction algorithms, with corresponding regret bounds, and a comparative experimental evaluation including the original Prediction with Expert Advice paradigm.
Huvudarbetet för den här avhandlingen har varit en grundlig studie av den nya Prediction with Partially Monitored Grouped Expert Advice and Side Information paradigmet. Detta är nyligen föreslagit i denna avhandling, och det utökar det brett studerade Prediction with Expert Advice paradigmet. Förlängningen baseras på två antaganden och en begränsning som ändrar det ursprungliga problemet. Det första antagandet, Grouped, förutsätter att experterna är inbyggda i grupper. Det andra antagandet, Side Information, introducerar ytterligare information som kan användas för att i tid relatera förutsägelser med grupper. Slutligen innebär begränsningen, Partially Monitored, att gruppens förutsägelser endast är kända för en grupp i taget. Studien av detta paradigm innefattar utformningen av en komplett förutsägelsesalgoritm, beviset på en teoretisk bindning till det sämre fallet kumulativa ånger för en sådan algoritm och en experimentell utvärdering av algoritmen (bevisar förekomsten av fall där detta paradigm överträffar Prediction with Expert Advice). Eftersom algoritmens utveckling är konstruktiv tillåter den dessutom att enkelt bygga två ytterligare prediksionsalgoritmer för Prediction with Grouped Expert Advice och Prediction with Grouped Expert Advice and Side Information paradigmer. Därför presenterar denna avhandling tre nya prediktionsalgoritmer med motsvarande ångergränser och en jämförande experimentell utvärdering inklusive det ursprungliga Prediction with Expert Advice paradigmet.
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Henke, Hans-Christian. "Online Advice : Konzeption eines ergebnisbasierten Simulationsansatzes /." [S.l. : s.n.], 2003. http://www.gbv.de/dms/zbw/362397171.pdf.

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Furkin, Jennifer D. "MOM TO MOM: ONLINE BREASTFEEDING ADVICE." UKnowledge, 2018. https://uknowledge.uky.edu/comm_etds/64.

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Exploring online support groups has gained more and more popularity in the last decade. Investigating the type of support messages users send each other has broadened the already extensive social support framework built in the last forty years. Mothers utilize online support for various topics, and a very common topic is breastfeeding. The perception of breastfeeding has changed throughout history with shifting beliefs and societal norms coupled with solid facts about its importance in the sustaining of infants. Online breastfeeding support has been previously explored through the categorization of types of support and themes within the interactions. This study extended this by investigating deeper into the advice solicitation patterns and directness of advice patterns. Results indicated that informational support most commonly was responded to support seekers. Support seekers utilized the requesting an opinion or information solicitation type most often when posting to the discussion board. Mothers most commonly offered storytelling as responses to posts and embedded advice within the stories.
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Porter, Noriko. "Japanese and U. S. mother's concerns and experts' advice content analysis of mothers' questions on online message boards and experts' advice in parenting magazines /." Diss., Columbia, Mo. : University of Missouri-Columbia, 2008. http://hdl.handle.net/10355/5517.

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Thesis (Ph. D.)--University of Missouri-Columbia, 2008.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on June 15, 2009) Vita. Includes bibliographical references.
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Fowler-Dawson, Amy E. "Expand your online reach with these 10 social media tips from the pros: An analysis of online social networking advice." OpenSIUC, 2016. https://opensiuc.lib.siu.edu/theses/2047.

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Researchers have suggested that social networking sites are especially suited to creating a two-way communication with audiences as described by Kent & Taylor’s dialogic communication theory. However, researchers have also shown that most organizations are failing to actually create this type of dialogue with their followers on SNS. This leads to the question: why are organizations failing to realize this potential? In this study, I consider one possible reason: that organizations are following advice offered online by self-appointed “experts” on SNS strategy and that advice is not effective. I performed a content analysis of 29 websites that promise easy tips to increase social media engagement, identified by their placement at the top of Google search listings, then tested some of the most common advice from these sites on the Facebook and Twitter pages of a group of state-level advocacy organizations to see whether that advice is effective in increasing engagement or overall reach. I found many sites advising organizations to interact with followers, create engaging content and to include visual elements in posts. However, the recommendations were often hedged with limitations, or backed up by unreliable statistics or anecdotal evidence. My own experiment showed that using a call to action increased engagement on Twitter and including a photo increased reach on Facebook, but no other test variable had an effect on impressions, reach or engagement on either site. This suggests that the advice offered online is not reliable, and organizations may fail to create dialogic communication with their followers because they are relying on faulty advice to build their SNS strategies.
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Barbaro, Billy. "Tuning Hyperparameters for Online Learning." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1522419008006144.

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Murphy, Nicholas John. "An online learning algorithm for technical trading." Master's thesis, Faculty of Science, 2019. http://hdl.handle.net/11427/31048.

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We use an adversarial expert based online learning algorithm to learn the optimal parameters required to maximise wealth trading zero-cost portfolio strategies. The learning algorithm is used to determine the relative population dynamics of technical trading strategies that can survive historical back-testing as well as form an overall aggregated portfolio trading strategy from the set of underlying trading strategies implemented on daily and intraday Johannesburg Stock Exchange data. The resulting population time-series are investigated using unsupervised learning for dimensionality reduction and visualisation. A key contribution is that the overall aggregated trading strategies are tested for statistical arbitrage using a novel hypothesis test proposed by Jarrow et al. [31] on both daily sampled and intraday time-scales. The (low frequency) daily sampled strategies fail the arbitrage tests after costs, while the (high frequency) intraday sampled strategies are not falsified as statistical arbitrages after costs. The estimates of trading strategy success, cost of trading and slippage are considered along with an offline benchmark portfolio algorithm for performance comparison. In addition, the algorithms generalisation error is analysed by recovering a probability of back-test overfitting estimate using a nonparametric procedure introduced by Bailey et al. [19]. The work aims to explore and better understand the interplay between different technical trading strategies from a data-informed perspective.
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Orlansky, Emily. "Beauty is in the mouth of the beholder advice networks at Haverford College /." Diss., Connect to the thesis, 2009. http://hdl.handle.net/10066/3707.

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Books on the topic "Online algorithm with advice"

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Kienholz, Michelle. Online guide to medical research: Valuable internet resources for medical research, practice & advice. San Jose, CA: Ventana, 1999.

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Moore, Alexis. Cyber self-defense: Expert advice to avoid online predators, identity theft, and cyberbullying. Guilford, Connecticut: Lyons Press, 2014.

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The weblog handbook: Practical advice on creating and maintaining your blog. Cambridge, MA: Perseus Pub., 2002.

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John, Yate Martin, ed. Knock 'em dead résumés: Smart advice to make your online and paper résumés more productive. 8th ed. Avon, Mass: Adams Media, 2008.

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Pauline, David, ed. Your personal net doctor: Your guide to health and medical advice on the Internet and online services. New York: Wolff New Media, 1996.

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Twitter marketing tips: 3 surprisingly powerful tips to make Twitter pay off with no costs upfront and much more : 101 world class expert facts, hints, tips and advice on Twitter. Brisbane, Australia]: [Emereo], 2009.

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Price, Joan. The insider's guide to internet health searches: Real-life success stories and expert advice for finding online health information you can trust. Emmaus, PA: Rodale Press, 2002.

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Giuseppe, Persiano, and SpringerLink (Online service), eds. Approximation and Online Algorithms: 9th International Workshop, WAOA 2011, Saarbrücken, Germany, September 8-9, 2011, Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Erlebach, Thomas. Approximation and Online Algorithms: 10th International Workshop, WAOA 2012, Ljubljana, Slovenia, September 13-14, 2012, Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Bolles, Mark Emery. Job-hunting online: A guide to job listings, message boards, research sites, the UnderWeb, counseling, networking, self-assessment tools, niche sites. 5th ed. Berkeley: Ten Speed Press, 2008.

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Book chapters on the topic "Online algorithm with advice"

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Borodin, Allan, Joan Boyar, Kim S. Larsen, and Denis Pankratov. "Advice Complexity of Priority Algorithms." In Approximation and Online Algorithms, 69–86. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04693-4_5.

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Adamaszek, Anna, Marc P. Renault, Adi Rosén, and Rob van Stee. "Reordering Buffer Management with Advice." In Approximation and Online Algorithms, 132–43. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08001-7_12.

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Christ, Marie G., Lene M. Favrholdt, and Kim S. Larsen. "Online Multi-Coloring with Advice." In Approximation and Online Algorithms, 83–94. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18263-6_8.

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Böckenhauer, Hans-Joachim, Janosch Fuchs, and Walter Unger. "Exploring Sparse Graphs with Advice (Extended Abstract)." In Approximation and Online Algorithms, 102–17. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04693-4_7.

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Böckenhauer, Hans-Joachim, Dennis Komm, and Raphael Wegner. "Call Admission Problems on Grids with Advice (Extended Abstract)." In Approximation and Online Algorithms, 118–33. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04693-4_8.

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Dobrev, Stefan, Rastislav Královič, and Richard Královič. "Independent Set with Advice: The Impact of Graph Knowledge." In Approximation and Online Algorithms, 2–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38016-7_2.

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Böckenhauer, Hans-Joachim, Dennis Komm, Rastislav Královič, Richard Královič, and Tobias Mömke. "On the Advice Complexity of Online Problems." In Algorithms and Computation, 331–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10631-6_35.

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Renault, Marc P., and Adi Rosén. "On Online Algorithms with Advice for the k-Server Problem." In Approximation and Online Algorithms, 198–210. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29116-6_17.

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Rohatgi, Dhruv. "Near-Optimal Bounds for Online Caching with Machine Learned Advice." In Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms, 1834–45. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2020. http://dx.doi.org/10.1137/1.9781611975994.112.

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Adamy, Udo, Thomas Erlebach, Dieter Mitsche, Ingo Schurr, Bettina Speckmann, and Emo Welzl. "Off-line Admission Control for Advance Reservations in Star Networks." In Approximation and Online Algorithms, 211–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-31833-0_18.

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Conference papers on the topic "Online algorithm with advice"

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Zhao, Xiaofan, Xin Li, and Hong Shen. "Improved Online Algorithms for One-Dimensional BinPacking with Advice." In 2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT). IEEE, 2017. http://dx.doi.org/10.1109/pdcat.2017.00042.

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Creveling, Jessica, and Cedric Hagen. "COMBINING A δ13CCARB CORRELATION ALGORITHM AND A REFINED RADIOMETRIC CHRONOLOGY TO ADVANCE PRECAMBRIAN (EDIACARAN) BASIN ANALYSIS." In GSA 2020 Connects Online. Geological Society of America, 2020. http://dx.doi.org/10.1130/abs/2020am-355524.

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Vaidya, Nupur D., Yogesh A. Suryawanshi, and Manish Chavan. "Design for enhancing the performance of Advance Encryption Standard algorithm VHDL." In 2016 Online International Conference on Green Engineering and Technologies (IC-GET). IEEE, 2016. http://dx.doi.org/10.1109/get.2016.7916849.

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Ganapathi Subramanian, Sriram, Matthew E. Taylor, Kate Larson, and Mark Crowley. "Multi-Agent Advisor Q-Learning (Extended Abstract)." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/776.

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In the last decade, there have been significant advances in multi-agent reinforcement learning (MARL) but there are still numerous challenges, such as high sample complexity and slow convergence to stable policies, that need to be overcome before wide-spread deployment is possible. However, many real-world environments already, in practice, deploy sub-optimal or heuristic approaches for generating policies. An interesting question that arises is how to best use such approaches as advisors to help improve reinforcement learning in multi-agent domains. We provide a principled framework for incorporating action recommendations from online sub-optimal advisors in multi-agent settings. We describe the problem of ADvising Multiple Intelligent Reinforcement Agents (ADMIRAL) in nonrestrictive general-sum stochastic game environments and present two novel Q-learning-based algorithms: ADMIRAL - Decision Making (ADMIRAL-DM) and ADMIRAL - Advisor Evaluation (ADMIRAL-AE), which allow us to improve learning by appropriately incorporating advice from an advisor (ADMIRAL-DM), and evaluate the effectiveness of an advisor (ADMIRAL-AE). We analyze the algorithms theoretically and provide fixed point guarantees regarding their learning in general-sum stochastic games. Furthermore, extensive experiments illustrate that these algorithms: can be used in a variety of environments, have performances that compare favourably to other related baselines, can scale to large state-action spaces, and are robust to poor advice from advisors.
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Hikima, Yuya, Yasunori Akagi, Naoki Marumo, and Hideaki Kim. "Online Matching with Controllable Rewards and Arrival Probabilities." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/254.

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Online bipartite matching has attracted much attention due to its importance in various applications such as advertising, ride-sharing, and crowdsourcing. In most online matching problems, the rewards and node arrival probabilities are given in advance and are not controllable. However, many real-world matching services require them to be controllable and the decision-maker faces a non-trivial problem of optimizing them. In this study, we formulate a new optimization problem, Online Matching with Controllable Rewards and Arrival probabilities (OM-CRA), to simultaneously determine not only the matching strategy but also the rewards and arrival probabilities. Even though our problem is more complex than the existing ones, we propose a fast 1/2-approximation algorithm for OM-CRA. The proposed approach transforms OM-CRA to a saddle-point problem by approximating the objective function, and then solves it by the Primal-Dual Hybrid Gradient (PDHG) method with acceleration through the use of the problem structure. In simulations on real data from crowdsourcing and ride-sharing platforms, we show that the proposed algorithm can find solutions with high total rewards in practical times.
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Nirala, C. K., and P. Saha. "Development of an algorithm for online pulse discrimination in micro-EDM using current and voltage sensors and their comparison." In 2015 IEEE International Advance Computing Conference (IACC). IEEE, 2015. http://dx.doi.org/10.1109/iadcc.2015.7154758.

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Sivagnanam, Amutheezan, Salah Uddin Kadir, Ayan Mukhopadhyay, Philip Pugliese, Abhishek Dubey, Samitha Samaranayake, and Aron Laszka. "Offline Vehicle Routing Problem with Online Bookings: A Novel Problem Formulation with Applications to Paratransit." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/546.

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Vehicle routing problems (VRPs) can be divided into two major categories: offline VRPs, which consider a given set of trip requests to be served, and online VRPs, which consider requests as they arrive in real-time. Based on discussions with public transit agencies, we identify a real-world problem that is not addressed by existing formulations: booking trips with flexible pickup windows (e.g., 3 hours) in advance (e.g., the day before) and confirming tight pickup windows (e.g., 30 minutes) at the time of booking. Such a service model is often required in paratransit service settings, where passengers typically book trips for the next day over the phone. To address this gap between offline and online problems, we introduce a novel formulation, the offline vehicle routing problem with online bookings. This problem is very challenging computationally since it faces the complexity of considering large sets of requests—similar to offline VRPs—but must abide by strict constraints on running time—similar to online VRPs. To solve this problem, we propose a novel computational approach, which combines an anytime algorithm with a learning-based policy for real-time decisions. Based on a paratransit dataset obtained from the public transit agency of Chattanooga, TN, we demonstrate that our novel formulation and computational approach lead to significantly better outcomes in this setting than existing algorithms.
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Cai, Xia. "Vector Autoregressive Weighting Reversion Strategy for Online Portfolio Selection." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/616.

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Aiming to improve the performance of existing reversion based online portfolio selection strategies, we propose a novel multi-period strategy named “Vector Autoregressive Weighting Reversion” (VAWR). Firstly, vector autoregressive moving-average algorithm used in time series prediction is transformed into exploring the dynamic relationships between different assets for more accurate price prediction. Secondly, we design the modified online passive aggressive technique and advance a scheme to weigh investment risk and cumulative experience to update the closed-form of portfolio. Theoretical analysis and experimental results confirm the effectiveness and robustness of our strategy. Compared with the state-of-the-art strategies, VAWR greatly increases cumulative wealth, and it obtains the highest annualized percentage yield and sharp ratio on various public datasets. These improvements and easy implementation support the practical applications of VAWR.
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Zhu, Yada, Jianbo Li, Jingrui He, Brian L. Quanz, and Ajay A. Deshpande. "A Local Algorithm for Product Return Prediction in E-Commerce." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/517.

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With the rapid growth of e-tail, the cost to handle returned online orders also increases significantly and has become a major challenge in the e-commerce industry. Accurate prediction of product returns allows e-tailers to prevent problematic transactions in advance. However, the limited existing work for modeling customer online shopping behaviors and predicting their return actions fail to integrate the rich information in the product purchase and return history (e.g., return history, purchase-no-return behavior, and customer/product similarity). Furthermore, the large-scale data sets involved in this problem, typically consisting of millions of customers and tens of thousands of products, also render existing methods inefficient and ineffective at predicting the product returns. To address these problems, in this paper, we propose to use a weighted hybrid graph to represent the rich information in the product purchase and return history, in order to predict product returns. The proposed graph consists of both customer nodes and product nodes, undirected edges reflecting customer return history and customer/product similarity based on their attributes, as well as directed edges discriminating purchase-no-return and no-purchase actions. Based on this representation, we study a random-walk-based local algorithm for predicting product return propensity for each customer, whose computational complexity depends only on the size of the output cluster rather than the entire graph. Such a property makes the proposed local algorithm particularly suitable for processing the large-scale data sets to predict product returns. To test the performance of the proposed techniques, we evaluate the graph model and algorithm on multiple e-commerce data sets, showing improved performance over state-of-the-art methods.
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Zenun Franco, Rodrigo. "Online Recommender System for Personalized Nutrition Advice." In RecSys '17: Eleventh ACM Conference on Recommender Systems. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3109859.3109862.

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Reports on the topic "Online algorithm with advice"

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Balman, Mehmet, and Tevfik Kosar. An Online Scheduling Algorithm with Advance Reservation for Large-Scale Data Transfers. Office of Scientific and Technical Information (OSTI), May 2010. http://dx.doi.org/10.2172/1050437.

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Santesson, S., and P. Hallam-Baker. Online Certificate Status Protocol Algorithm Agility. RFC Editor, June 2011. http://dx.doi.org/10.17487/rfc6277.

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Streeter, Matthew, and Daniel Golovin. An Online Algorithm for Maximizing Submodular Functions. Fort Belvoir, VA: Defense Technical Information Center, December 2007. http://dx.doi.org/10.21236/ada476748.

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Meeker, Jessica. Mutual Learning for Policy Impact: Insights from CORE. Shaping Policy and Practice with Intersectional Gender Responsive Evidence (in the Context of Covid-19). Institute of Development Studies (IDS), November 2021. http://dx.doi.org/10.19088/core.2021.007.

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On the 19 and 20 October 2021, the Institute of Development Studies hosted an online dialogue which aimed to enhance efforts to inform and influence policy, management, and practice with intersectional gender-responsive evidence by sharing learning between CORE cohort members from their approaches and experiences at country and regional levels. The event was attended by over 30 participants from 19 partners across the CORE cohort and highlighted the experiences of CORE partners Glasswing and the Arab Reform Initiative (ARI). This learning guide captures the practical insights and advice from the event, to help inform the practice of participants and other projects across the portfolio.
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Schmidt-Sane, Megan, Tabitha Hrynick, Erica Nelson, and Tom Barker. Mutual Learning for Policy Impact: Insights from CORE. Adapting research methods in the context of Covid-19. Institute of Development Studies (IDS), December 2021. http://dx.doi.org/10.19088/core.2021.008.

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On 25 November 2021, the CORE Knowledge Translation Services team at the Institute of Development Studies, UK, hosted an online clinic session to facilitate the sharing of experiences and mutual learning on how CORE projects have or can adapt their research activities in the context of the Covid-19 pandemic. The clinic was attended by 22 CORE members from 12 projects and featured contributions from two CORE projects: The Youth Question in Africa: Impact, Response and Protection Measures in the IGAD Region and A New Digital Deal for an Inclusive Post-Covid-19 Social Compact: Developing Digital Strategies for Social and Economic Reconstruction. This learning guide captures the practical insights and advice from the event, to help inform the practice of participants and other projects across the portfolio.
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Meeker, Jessica. Mutual Learning for Policy Impact: Insights from CORE. Sharing Experience and Learning on Approaches to Influence Policy and Practice. Institute of Development Studies (IDS), August 2021. http://dx.doi.org/10.19088/core.2021.005.

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On 23 June 2021, Southern Voice and the Institute of Development Studies co-hosted an online dialogue which aimed to enhance efforts to inform and influence policy by sharing learning between CORE projects, at different stages in their policy engagement activities, on their approaches and experiences at sub-national, national, and regional levels. The event was attended by over 70 participants from across the CORE cohort and highlighted the experiences of CORE partners, Partnership for Economic Policy (PEP), International Centre for Research on Women (ICRW), and Group for the Analysis of Development (GRADE). This learning guide captures the practical insights and advice from the event to help inform the practice of both participants and other projects across the portfolio. The guide is structured around the key challenges identified in influencing policy, particularly within the changing parameters of the current pandemic, highlighting key messages and examples from the three partners.
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Tidd, Alexander N., Richard A. Ayers, Grant P. Course, and Guy R. Pasco. Scottish Inshore Fisheries Integrated Data System (SIFIDS): work package 6 final report development of a pilot relational data resource for the collation and interpretation of inshore fisheries data. Edited by Mark James and Hannah Ladd-Jones. Marine Alliance for Science and Technology for Scotland (MASTS), 2019. http://dx.doi.org/10.15664/10023.23452.

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[Extract from Executive Summary] The competition for space from competing sectors in the coastal waters of Scotland has never been greater and thus there is a growing a need for interactive seascape planning tools that encompass all marine activities. Similarly, the need to gather data to inform decision makers, especially in the fishing industry, has become essential to provide advice on the economic impact on fishing fleets both in terms of alternative conservation measures (e.g. effort limitations, temporal and spatial closures) as well as the overlap with other activities, thereby allowing stakeholders to derive a preferred option. The SIFIDS project was conceived to allow the different relevant data sources to be identified and to allow these data to be collated in one place, rather than as isolated data sets with multiple data owners. The online interactive tool developed as part of the project (Work Package 6) brought together relevant data sets and developed data storage facilities and a user interface to allow various types of user to view and interrogate the data. Some of these data sets were obtained as static layers which could sit as background data e.g. substrate type, UK fishing limits; whilst other data came directly from electronic monitoring systems developed as part of the SIFIDS project. The main non-static data source was Work Package 2, which was collecting data from a sample of volunteer inshore fishing vessels (<12m). This included data on location; time; vessel speed; count, time and position of deployment of strings of creels (or as fleets and pots as they are also known respectively); and a count of how many creels were hauled on these strings. The interactive online tool allowed all the above data to be collated in a specially designed database and displayed in near real time on the web-based application.
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Gertler, Paul, Sebastian Martinez, Laura B. Rawlings, Patrick Premand, and Christel M. J. Vermeersch. Impact Evaluation in Practice: Second Edition. Inter-American Development Bank, September 2016. http://dx.doi.org/10.18235/0006529.

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The second edition of the Impact Evaluation in Practice handbook is a comprehensive and accessible introduction to impact evaluation for policy makers and development practitioners. First published in 2011, it has been used widely across the development and academic communities. The book incorporates real-world examples to present practical guidelines for designing and implementing impact evaluations. Readers will gain an understanding of impact evaluations and the best ways to use them to design evidence-based policies and programs. The updated version covers the newest techniques for evaluating programs and includes state-of-the-art implementation advice, as well as an expanded set of examples and case studies that draw on recent development challenges. It also includes new material on research ethics and partnerships to conduct impact evaluation. The handbook is divided into four sections: Part One discusses what to evaluate and why; Part Two presents the main impact evaluation methods; Part Three addresses how to manage impact evaluations; Part Four reviews impact evaluation sampling and data collection. Case studies illustrate different applications of impact evaluations. The book links to complementary instructional material available online, including an applied case as well as questions and answers. The updated second edition will be a valuable resource for the international development community, universities, and policy makers looking to build better evidence around what works in development.
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Milek, Karen, and Richard Jones, eds. Science in Scottish Archaeology: ScARF Panel Report. Society of Antiquaries of Scotland, September 2012. http://dx.doi.org/10.9750/scarf.06.2012.193.

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The main recommendations of the panel report can be summarised under four key headings:  High quality, high impact research: the importance of archaeological science is reflected in work that explores issues connected to important contemporary topics, including: the demography of, the nature of movement of, and contact between peoples; societal resilience; living on the Atlantic edge of Europe; and coping with environmental and climatic change. A series of large-scale and integrated archaeological science projects are required to stimulate research into these important topics. To engage fully with Science in Scottish Archaeology iv these questions data of sufficient richness is required that is accessible, both within Scotland and internationally. The RCAHMS’ database Canmore provides a model for digital dissemination that should be built on.  Integration: Archaeological science should be involved early in the process of archaeological investigation and as a matter of routine. Resultant data needs to be securely stored, made accessible and the research results widely disseminated. Sources of advice and its communication must be developed and promoted to support work in the commercial, academic, research, governmental and 3rd sectors.  Knowledge exchange and transfer: knowledge, data and skills need to be routinely transferred and embedded across the archaeological sector. This will enable the archaeological science community to better work together, establishing routes of communication and improving infrastructure. Improvements should be made to communication between different groups including peers, press and the wider public. Mechanisms exist to enable the wider community to engage with, and to feed into, the development of the archaeological and scientific database and to engage with current debates. Projects involving the wider community in data generation should be encouraged and opportunities for public engagement should be pursued through, for example, National Science Week and Scottish Archaeology Month.  Networks and forums: A network of specialists should be promoted to aid collaboration, provide access to the best advice, and raise awareness of current work. This would be complemented by creating a series inter-disciplinary working groups, to discuss and articulate archaeological science issues. An online service to match people (i.e. specialist or student) to material (whether e.g. environmental sample, artefactual assemblage, or skeletal assemblage) is also recommended. An annual meeting should also be held at which researchers would be able to promote current and future work, and draw attention to materials available for analysis, and to specialists/students looking to work on particular assemblages or projects. Such meetings could be rolled into a suitable public outreach event.
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Paule, Bernard, Flourentzos Flourentzou, Tristan de KERCHOVE d’EXAERDE, Julien BOUTILLIER, and Nicolo Ferrari. PRELUDE Roadmap for Building Renovation: set of rules for renovation actions to optimize building energy performance. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau541614638.

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In the context of climate change and the environmental and energy constraints we face, it is essential to develop methods to encourage the implementation of efficient solutions for building renovation. One of the objectives of the European PRELUDE project [1] is to develop a "Building Renovation Roadmap"(BRR) aimed at facilitating decision-making to foster the most efficient refurbishment actions, the implementation of innovative solutions and the promotion of renewable energy sources in the renovation process of existing buildings. In this context, Estia is working on the development of inference rules that will make it possible. On the basis of a diagnosis such as the Energy Performance Certificate, it will help establishing a list of priority actions. The dynamics that drive this project permit to decrease the subjectivity of a human decisions making scheme. While simulation generates digital technical data, interpretation requires the translation of this data into natural language. The purpose is to automate the translation of the results to provide advice and facilitate decision-making. In medicine, the diagnostic phase is a process by which a disease is identified by its symptoms. Similarly, the idea of the process is to target the faulty elements potentially responsible for poor performance and to propose remedial solutions. The system is based on the development of fuzzy logic rules [2],[3]. This choice was made to be able to manipulate notions of membership with truth levels between 0 and 1, and to deliver messages in a linguistic form, understandable by non-specialist users. For example, if performance is low and parameter x is unfavourable, the algorithm can gives an incentive to improve the parameter such as: "you COULD, SHOULD or MUST change parameter x". Regarding energy performance analysis, the following domains are addressed: heating, domestic hot water, cooling, lighting. Regarding the parameters, the analysis covers the following topics: Characteristics of the building envelope. and of the technical installations (heat production-distribution, ventilation system, electric lighting, etc.). This paper describes the methodology used, lists the fields studied and outlines the expected outcomes of the project.
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