Minimax

Results: 375



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1Minimax Differential Dynamic Programming: An Application to Robust Biped Walking Jun Morimoto Human Information Science Labs, Department 3, ATR International

Minimax Differential Dynamic Programming: An Application to Robust Biped Walking Jun Morimoto Human Information Science Labs, Department 3, ATR International

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Source URL: www.cns.atr.jp

Language: English - Date: 2016-12-05 22:52:47
    22015 European Control Conference (ECC) July 15-17, 2015. Linz, Austria Approximate Closed-Loop Minimax Model Predictive Operation Control of Microgrids Christian A. Hans, Vladislav Nenchev, J¨org Raisch and Carsten Rein

    2015 European Control Conference (ECC) July 15-17, 2015. Linz, Austria Approximate Closed-Loop Minimax Model Predictive Operation Control of Microgrids Christian A. Hans, Vladislav Nenchev, J¨org Raisch and Carsten Rein

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    Source URL: www.control.tu-berlin.de

    Language: English - Date: 2016-11-08 07:38:30
      3Minimax Theory and its Applications Volume), No. 2, 265–283 Bounded Solutions to Nonlinear Problems in RN Involving the Fractional Laplacian Depending on Parameters

      Minimax Theory and its Applications Volume), No. 2, 265–283 Bounded Solutions to Nonlinear Problems in RN Involving the Fractional Laplacian Depending on Parameters

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      Source URL: www.math.winkert.de

      Language: English - Date: 2017-09-23 04:49:39
        4Online Convex Optimization: From Gambling to Minimax Theorems by Playing Repeated Games Tim van Erven

        Online Convex Optimization: From Gambling to Minimax Theorems by Playing Repeated Games Tim van Erven

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        Source URL: www.timvanerven.nl

        Language: English - Date: 2018-04-10 09:11:45
          5CS364A: Algorithmic Game Theory Lecture #18: From External Regret to Swap Regret and the Minimax Theorem∗ Tim Roughgarden† November 20, 2013

          CS364A: Algorithmic Game Theory Lecture #18: From External Regret to Swap Regret and the Minimax Theorem∗ Tim Roughgarden† November 20, 2013

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          Source URL: theory.stanford.edu

          - Date: 2016-08-30 19:08:33
            6Minimax Regret Bounds for Reinforcement Learning Mohammad Gheshlaghi Azar 1 Ian Osband 1 Rémi Munos 1 Abstract We consider the problem of provably optimal

            Minimax Regret Bounds for Reinforcement Learning Mohammad Gheshlaghi Azar 1 Ian Osband 1 Rémi Munos 1 Abstract We consider the problem of provably optimal

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            Source URL: proceedings.mlr.press

            - Date: 2018-02-06 15:06:57
              7Parallel Randomized Best-First Minimax Search 1 Yaron Shoham 2, Sivan Toledo 3 School of Computer Science, Tel-Aviv University Abstract

              Parallel Randomized Best-First Minimax Search 1 Yaron Shoham 2, Sivan Toledo 3 School of Computer Science, Tel-Aviv University Abstract

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              Source URL: www.top-5000.nl

              - Date: 2007-02-13 11:21:48
                8Minimax Regret Bounds for Reinforcement Learning Appendices We begin by introducing some notation in Sect. B and Sect. A. We then provide the full analysis of UCBVI in Sect. C. A. Table of Notation

                Minimax Regret Bounds for Reinforcement Learning Appendices We begin by introducing some notation in Sect. B and Sect. A. We then provide the full analysis of UCBVI in Sect. C. A. Table of Notation

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                Source URL: proceedings.mlr.press

                - Date: 2018-02-06 15:06:57
                  9Multi-feature Spectral Clustering with Minimax Optimization Hongxing Wang, Chaoqun Weng, and Junsong Yuan School of Electrical and Electronic Engineering Nanyang Technological University, Singapore, 639798 {hwang8, weng0

                  Multi-feature Spectral Clustering with Minimax Optimization Hongxing Wang, Chaoqun Weng, and Junsong Yuan School of Electrical and Electronic Engineering Nanyang Technological University, Singapore, 639798 {hwang8, weng0

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                  Source URL: eeeweba.ntu.edu.sg

                  - Date: 2014-05-20 23:11:03
                    10JMLR: Workshop and Conference Proceedings vol–25th Annual Conference on Learning Theory Towards Minimax Policies for Online Linear Optimization with Bandit Feedback

                    JMLR: Workshop and Conference Proceedings vol–25th Annual Conference on Learning Theory Towards Minimax Policies for Online Linear Optimization with Bandit Feedback

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                    Source URL: www.jmlr.org

                    - Date: 2012-06-17 06:50:54