Relevant bibliographies by topics / Minimax regret bound

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  1. Journal articles
  2. Dissertations / Theses
  3. Conference papers

Academic literature on the topic 'Minimax regret bound'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "Minimax regret bound"

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Wang, Guanghui, Shiyin Lu, Yao Hu, and Lijun Zhang. "Adapting to Smoothness: A More Universal Algorithm for Online Convex Optimization." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 6162–69. http://dx.doi.org/10.1609/aaai.v34i04.6081.

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We aim to design universal algorithms for online convex optimization, which can handle multiple common types of loss functions simultaneously. The previous state-of-the-art universal method has achieved the minimax optimality for general convex, exponentially concave and strongly convex loss functions. However, it remains an open problem whether smoothness can be exploited to further improve the theoretical guarantees. In this paper, we provide an affirmative answer by developing a novel algorithm, namely UFO, which achieves O(√L*), O(d log L*) and O(log L*) regret bounds for the three types o
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Song, Kyungchul. "POINT DECISIONS FOR INTERVAL–IDENTIFIED PARAMETERS." Econometric Theory 30, no. 2 (2014): 334–56. http://dx.doi.org/10.1017/s0266466613000327.

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This paper considers a decision maker who prefers to make a point decision when the object of interest is interval-identified with regular bounds. When the bounds are just identified along with known interval length, the local asymptotic minimax decision with respect to a symmetric convex loss function takes an obvious form: an efficient lower bound estimator plus the half of the known interval length. However, when the interval length or any nontrivial upper bound for the length is not known, the minimax approach suffers from triviality because the maximal risk is associated with infinitely l
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Hansen, Bruce E. "SHRINKAGE EFFICIENCY BOUNDS." Econometric Theory 31, no. 4 (2014): 860–79. http://dx.doi.org/10.1017/s0266466614000693.

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This paper is an extension of Magnus (2002, Econometrics Journal 5, 225–236) to multiple dimensions. We consider estimation of a multivariate normal mean under sum of squared error loss. We construct the efficiency bound (the lowest achievable risk) for minimax shrinkage estimation in the class of minimax orthogonally invariate estimators satisfying the sufficient conditions of Efron and Morris (1976, Annals of Statistics 4, 11–21). This allows us to compare the regret of existing orthogonally invariate shrinkage estimators. We also construct a new shrinkage estimator which achieves substantia
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Conde, Eduardo. "A Branch and Bound algorithm for the minimax regret spanning arborescence." Journal of Global Optimization 37, no. 3 (2006): 467–80. http://dx.doi.org/10.1007/s10898-006-9074-4.

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Tahir, Mohamed. "An Asymptotic Lower Bound for the Local Minimax Regret in Sequential Point Estimation." Annals of Statistics 17, no. 3 (1989): 1335–46. http://dx.doi.org/10.1214/aos/1176347273.

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Mitra, Siddharth, and Aditya Gopalan. "On Adaptivity in Information-Constrained Online Learning." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5199–206. http://dx.doi.org/10.1609/aaai.v34i04.5964.

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We study how to adapt to smoothly-varying (‘easy’) environments in well-known online learning problems where acquiring information is expensive. For the problem of label efficient prediction, which is a budgeted version of prediction with expert advice, we present an online algorithm whose regret depends optimally on the number of labels allowed and Q* (the quadratic variation of the losses of the best action in hindsight), along with a parameter-free counterpart whose regret depends optimally on Q (the quadratic variation of the losses of all the actions). These quantities can be significantl
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Zhang, Teng, Peng Zhao, and Hai Jin. "Optimal Margin Distribution Learning in Dynamic Environments." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 6821–28. http://dx.doi.org/10.1609/aaai.v34i04.6162.

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Recently a promising research direction of statistical learning has been advocated, i.e., the optimal margin distribution learning with the central idea that instead of the minimal margin, the margin distribution is more crucial to the generalization performance. Although the superiority of this new learning paradigm has been verified under batch learning settings, it remains open for online learning settings, in particular, the dynamic environments in which the underlying decision function varies over time. In this paper, we propose the dynamic optimal margin distribution machine and theoreti
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Dissertations / Theses on the topic "Minimax regret bound"

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Li, Le. "Online stochastic algorithms." Thesis, Angers, 2018. http://www.theses.fr/2018ANGE0031.

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Cette thèse travaille principalement sur trois sujets. Le premier concentre sur le clustering en ligne dans lequel nous présentons un nouvel algorithme stochastique adaptatif pour regrouper des ensembles de données en ligne. Cet algorithme repose sur l'approche quasi-bayésienne, avec une estimation dynamique (i.e., dépendant du temps) du nombre de clusters. Nous prouvons que cet algorithme atteint une borne de regret de l'ordre et que cette borne est asymptotiquement minimax sous la contrainte sur le nombre de clusters. Nous proposons aussi une implémentation par RJMCMC. Le deuxième sujet est
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Gerchinovitz, Sébastien. "Prédiction de suites individuelles et cadre statistique classique : étude de quelques liens autour de la régression parcimonieuse et des techniques d'agrégation." Phd thesis, Université Paris Sud - Paris XI, 2011. http://tel.archives-ouvertes.fr/tel-00653550.

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Cette thèse s'inscrit dans le domaine de l'apprentissage statistique. Le cadre principal est celui de la prévision de suites déterministes arbitraires (ou suites individuelles), qui recouvre des problèmes d'apprentissage séquentiel où l'on ne peut ou ne veut pas faire d'hypothèses de stochasticité sur la suite des données à prévoir. Cela conduit à des méthodes très robustes. Dans ces travaux, on étudie quelques liens étroits entre la théorie de la prévision de suites individuelles et le cadre statistique classique, notamment le modèle de régression avec design aléatoire ou fixe, où les données
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Godinho, Noé Paulo Lopes. "Algorithms for the Min-Max Regret Minimum Spanning Tree Problem." Master's thesis, 2017. http://hdl.handle.net/10316/83276.

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Dissertação de Mestrado em Engenharia Informática apresentada à Faculdade de Ciências e Tecnologia<br>Incerteza em optimização pode ser modelada com o conceito de cenários, onde cada cenário corresponde a um valor possível para cada parâmetro do problema. O critério do Min-Max Regret tem como objectivo encontrar uma solução que minimiza o máximo desvio, de entre todos os cenários, do valor óptimo de cada cenário. O estudo deste critério é motivado pelas aplicações práticas onde uma antecipação do pior caso é crucial. Problemas conhecidos, tais como o caminho mais curto e a árvore mínima de
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Conference papers on the topic "Minimax regret bound"

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Yokoyama, Ryohei, and Koichi Ito. "Multiobjective Robust Optimal Design of a Gas Turbine Cogeneration Plant Under Uncertain Energy Demands." In ASME Turbo Expo 2001: Power for Land, Sea, and Air. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/2001-gt-0208.

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A multiobjective robust optimal design method based on the minimax regret criterion is proposed for sizing equipment of energy supply plants so that they are robust in economic and energy saving characteristics under uncertain energy demands. Equipment capacities and utility contract demands as well as energy flow rates are determined to minimize a weighted sum of the maximum regrets in the annual total cost and primary energy consumption, and satisfy all the possible energy demands. This optimization problem is formulated as a kind of multilevel linear programming one, and its solution is der
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Yokoyama, Ryohei, and Koichi Ito. "Robust Optimal Design of a Gas Turbine Cogeneration Plant Based on Minimax Regret Criterion." In ASME 1999 International Gas Turbine and Aeroengine Congress and Exhibition. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/99-gt-128.

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A robust optimal design method based on the minimax regret criterion is proposed for unit sizing of energy supply plants so that they are robust economically against the uncertainty in energy demands. Equipment capacities and utility contract demands as well as energy flow rates are determined to minimize the maximum regret in the annual total cost and to satisfy all the possible energy demands. This optimization problem is formulated as a kind of multilevel linear programming one, and its solution is derived by repeatedly evaluating lower and upper bounds for the optimal value of the maximum
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Li, Shuai, Wei Chen, Shuai Li, and Kwong-Sak Leung. "Improved Algorithm on Online Clustering of Bandits." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/405.

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We generalize the setting of online clustering of bandits by allowing non-uniform distribution over user frequencies. A more efficient algorithm is proposed with simple set structures to represent clusters. We prove a regret bound for the new algorithm which is free of the minimal frequency over users. The experiments on both synthetic and real datasets consistently show the advantage of the new algorithm over existing methods.
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Xu, Huanle, Yang Liu, Wing Cheong Lau, and Rui Li. "Combinatorial Multi-Armed Bandits with Concave Rewards and Fairness Constraints." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/354.

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The problem of multi-armed bandit (MAB) with fairness constraint has emerged as an important research topic recently. For such problems, one common objective is to maximize the total rewards within a fixed round of pulls, while satisfying the fairness requirement of a minimum selection fraction for each individual arm in the long run. Previous works have made substantial advancements in designing efficient online selection solutions, however, they fail to achieve a sublinear regret bound when incorporating such fairness constraints. In this paper, we study a combinatorial MAB problem with conc
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Rosenberg, Aviv, and Yishay Mansour. "Stochastic Shortest Path with Adversarially Changing Costs." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/404.

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Stochastic shortest path (SSP) is a well-known problem in planning and control, in which an agent has to reach a goal state in minimum total expected cost. In this paper we present the adversarial SSP model that also accounts for adversarial changes in the costs over time, while the underlying transition function remains unchanged. Formally, an agent interacts with an SSP environment for K episodes, the cost function changes arbitrarily between episodes, and the transitions are unknown to the agent. We develop the first algorithms for adversarial SSPs and prove high probability regret bounds o
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