際際滷shows by User: rikija / http://www.slideshare.net/images/logo.gif 際際滷shows by User: rikija / Wed, 06 May 2020 11:17:28 GMT 際際滷Share feed for 際際滷shows by User: rikija Social Cost in Pandemic as Expected Convex Loss /slideshow/social-cost-in-pandemic-as-expected-convex-loss/233276136 socialcostasexpectedconvexloss-200506111728
Power-law distribution of the number of virus victims is derived from heterogenity across spreaders. What makes worse is the convex cost with respect to the number of victims. Similarly to taking higher-order moments under power-law distribution, the expected cost can become surprisingly high even if the number is truncated at some population. Policy for cutting the tail by specifically focusing on the superspreaders should be encouraged.]]>

Power-law distribution of the number of virus victims is derived from heterogenity across spreaders. What makes worse is the convex cost with respect to the number of victims. Similarly to taking higher-order moments under power-law distribution, the expected cost can become surprisingly high even if the number is truncated at some population. Policy for cutting the tail by specifically focusing on the superspreaders should be encouraged.]]>
Wed, 06 May 2020 11:17:28 GMT /slideshow/social-cost-in-pandemic-as-expected-convex-loss/233276136 rikija@slideshare.net(rikija) Social Cost in Pandemic as Expected Convex Loss rikija Power-law distribution of the number of virus victims is derived from heterogenity across spreaders. What makes worse is the convex cost with respect to the number of victims. Similarly to taking higher-order moments under power-law distribution, the expected cost can become surprisingly high even if the number is truncated at some population. Policy for cutting the tail by specifically focusing on the superspreaders should be encouraged. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/socialcostasexpectedconvexloss-200506111728-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Power-law distribution of the number of virus victims is derived from heterogenity across spreaders. What makes worse is the convex cost with respect to the number of victims. Similarly to taking higher-order moments under power-law distribution, the expected cost can become surprisingly high even if the number is truncated at some population. Policy for cutting the tail by specifically focusing on the superspreaders should be encouraged.
Social Cost in Pandemic as Expected Convex Loss from Rikiya Takahashi
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Uncertainty Awareness in Integrating Machine Learning and Game Theory /slideshow/uncertainty-awareness-in-integrating-machine-learning-and-game-theory/72581377 rtakahashigtw20170305-170226030024
Invited Talk at Game Theory Workshop 2017 (Japan)]]>

Invited Talk at Game Theory Workshop 2017 (Japan)]]>
Sun, 26 Feb 2017 03:00:24 GMT /slideshow/uncertainty-awareness-in-integrating-machine-learning-and-game-theory/72581377 rikija@slideshare.net(rikija) Uncertainty Awareness in Integrating Machine Learning and Game Theory rikija Invited Talk at Game Theory Workshop 2017 (Japan) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/rtakahashigtw20170305-170226030024-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Invited Talk at Game Theory Workshop 2017 (Japan)
Uncertainty Awareness in Integrating Machine Learning and Game Theory from Rikiya Takahashi
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On the Dynamics of Machine Learning Algorithms and Behavioral Game Theory /slideshow/on-the-dynamics-of-machine-learning-algorithms-and-behavioral-game-theory/66039743 20160917rtakahashilecture-160915014744
Presentation Material used in guest lecturing at University of Tsukuba on September 17, 2016. Target audience is part-time PhD student working at a machine learning, data mining, or agent-based simulation project.]]>

Presentation Material used in guest lecturing at University of Tsukuba on September 17, 2016. Target audience is part-time PhD student working at a machine learning, data mining, or agent-based simulation project.]]>
Thu, 15 Sep 2016 01:47:44 GMT /slideshow/on-the-dynamics-of-machine-learning-algorithms-and-behavioral-game-theory/66039743 rikija@slideshare.net(rikija) On the Dynamics of Machine Learning Algorithms and Behavioral Game Theory rikija Presentation Material used in guest lecturing at University of Tsukuba on September 17, 2016. Target audience is part-time PhD student working at a machine learning, data mining, or agent-based simulation project. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/20160917rtakahashilecture-160915014744-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Presentation Material used in guest lecturing at University of Tsukuba on September 17, 2016. Target audience is part-time PhD student working at a machine learning, data mining, or agent-based simulation project.
On the Dynamics of Machine Learning Algorithms and Behavioral Game Theory from Rikiya Takahashi
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Predicting Preference Reversals via Gaussian Process Uncertainty Aversion /slideshow/predicting-47969285/47969285 gpuaslide-150510182350-lva1-app6892
Talk material for the following paper http://jmlr.org/proceedings/papers/v38/takahashi15.html]]>

Talk material for the following paper http://jmlr.org/proceedings/papers/v38/takahashi15.html]]>
Sun, 10 May 2015 18:23:49 GMT /slideshow/predicting-47969285/47969285 rikija@slideshare.net(rikija) Predicting Preference Reversals via Gaussian Process Uncertainty Aversion rikija Talk material for the following paper http://jmlr.org/proceedings/papers/v38/takahashi15.html <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/gpuaslide-150510182350-lva1-app6892-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Talk material for the following paper http://jmlr.org/proceedings/papers/v38/takahashi15.html
Predicting Preference Reversals via Gaussian Process Uncertainty Aversion from Rikiya Takahashi
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Collective Response Spike Prediction for Mutually Interacting Consumers /slideshow/collective-spikeslide/29269346 collectivespikeslide-131216205737-phpapp01
The presentation deck used in the 13th IEEE International Conference on Data Mining (ICDM 2013),]]>

The presentation deck used in the 13th IEEE International Conference on Data Mining (ICDM 2013),]]>
Mon, 16 Dec 2013 20:57:37 GMT /slideshow/collective-spikeslide/29269346 rikija@slideshare.net(rikija) Collective Response Spike Prediction for Mutually Interacting Consumers rikija The presentation deck used in the 13th IEEE International Conference on Data Mining (ICDM 2013), <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/collectivespikeslide-131216205737-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The presentation deck used in the 13th IEEE International Conference on Data Mining (ICDM 2013),
Collective Response Spike Prediction for Mutually Interacting Consumers from Rikiya Takahashi
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Large-Scale Nonparametric Estimation of Vehicle Travel Time Distributions /slideshow/nonparametric-traveltimeslide/29269281 nonparametrictraveltimeslide-131216205225-phpapp02
The presentation deck used in the 12th SIAM International Conference on Data Mining (SDM12).]]>

The presentation deck used in the 12th SIAM International Conference on Data Mining (SDM12).]]>
Mon, 16 Dec 2013 20:52:25 GMT /slideshow/nonparametric-traveltimeslide/29269281 rikija@slideshare.net(rikija) Large-Scale Nonparametric Estimation of Vehicle Travel Time Distributions rikija The presentation deck used in the 12th SIAM International Conference on Data Mining (SDM12). <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nonparametrictraveltimeslide-131216205225-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The presentation deck used in the 12th SIAM International Conference on Data Mining (SDM12).
Large-Scale Nonparametric Estimation of Vehicle Travel Time Distributions from Rikiya Takahashi
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https://cdn.slidesharecdn.com/profile-photo-rikija-48x48.jpg?cb=1613294318 jp.linkedin.com/in/rikiya https://cdn.slidesharecdn.com/ss_thumbnails/socialcostasexpectedconvexloss-200506111728-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/social-cost-in-pandemic-as-expected-convex-loss/233276136 Social Cost in Pandemi... https://cdn.slidesharecdn.com/ss_thumbnails/rtakahashigtw20170305-170226030024-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/uncertainty-awareness-in-integrating-machine-learning-and-game-theory/72581377 Uncertainty Awareness ... https://cdn.slidesharecdn.com/ss_thumbnails/20160917rtakahashilecture-160915014744-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/on-the-dynamics-of-machine-learning-algorithms-and-behavioral-game-theory/66039743 On the Dynamics of Mac...