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Joint Bottom-up Method for Probabilistic Forecasting of Hierarchical Time Series
- Nicolò Bertani
  Corresponding Author
  Nicolò Bertani
  [email protected]
  https://orcid.org/0000-0003-4785-0611
  Católica Lisbon School of Business & Economics, 1649-023 Lisbon, Portugal
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  ,
- Shane T. Jensen
  Shane T. Jensen
  [email protected]
  Department of Statistics and Data Science, The Wharton School of the University of Pennsylvania, Philadelphia, Pennsylvania 19104;
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  ,
- Ville A. Satopää
  Ville A. Satopää
  [email protected]
  https://orcid.org/0000-0002-3787-7303
  Department of Technology and Operations Management, INSEAD, 77300 Fontainebleau, France
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Published Online:January 7, 2025
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Abstract
In a stark departure from conventional wisdom, Nicolò Bertani, Shane T. Jensen, and Ville A. Satopää’s recently published research article titled “Joint Bottom-up Method for Probabilistic Forecasting of Hierarchical Time Series” dismantles a long-held ...
Technical Note: Data-Driven Sample Average Approximation with Covariate Information
- Rohit Kannan
  Corresponding Author
  Rohit Kannan
  [email protected]
  https://orcid.org/0000-0002-7963-7682
  Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia 24061
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- Güzin Bayraksan
  Güzin Bayraksan
  [email protected]
  https://orcid.org/0000-0001-5521-1313
  Department of Integrated Systems Engineering, The Ohio State University, Columbus, Ohio 43210
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- James R. Luedtke
  James R. Luedtke
  [email protected]
  https://orcid.org/0000-0001-9265-7728
  Department of Industrial & Systems Engineering and Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, Wisconsin 53706
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Published Online:January 6, 2025
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Abstract
Using Side Information to Improve Decision Making Under UncertaintyIn many real-world planning settings, side information (also called covariate information, contextual information, or features) can be used to improve the estimates of uncertain ...
Doubly Optimal No-Regret Online Learning in Strongly Monotone Games with Bandit Feedback
- Wenjia Ba
  Wenjia Ba
  [email protected]
  https://orcid.org/0000-0003-3427-415X
  Sauder School of Business, University of British Columbia, Vancouver, British Columbia V6S 0G6, Canada
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- Tianyi Lin
  Tianyi Lin
  [email protected]
  https://orcid.org/0000-0002-5323-1852
  Department of Industrial Engineering and Operations Research, Columbia University, New York, New York 10027
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- Jiawei Zhang
  Jiawei Zhang
  [email protected]
  https://orcid.org/0000-0003-4988-6028
  Stern School of Business, New York University, New York, New York 10012
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- Zhengyuan Zhou
  Corresponding Author
  Zhengyuan Zhou
  [email protected]
  https://orcid.org/0000-0002-0005-9411
  Stern School of Business, New York University, New York, New York 10012
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Published Online:January 3, 2025
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Abstract
Doubly Optimal No-Regret Online Learning in Strongly Monotone Games with Bandit FeedbackCurious about how players can learn and adapt in unknown games without knowing the game’s dynamics? In “Doubly Optimal No-Regret Online Learning in Strongly Monotone ...
Selection of the Most Probable Best
- Taeho Kim
  Taeho Kim
  [email protected]
  https://orcid.org/0000-0001-9329-0614
  HKUST Business School, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
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- Kyoung-Kuk Kim
  Kyoung-Kuk Kim
  [email protected]
  https://orcid.org/0000-0002-9661-8707
  College of Business, Korea Advanced Institute of Science and Technology, Seoul 02455, Republic of Korea
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- Eunhye Song
  Corresponding Author
  Eunhye Song
  [email protected]
  https://orcid.org/0000-0002-5171-0614
  H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
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Published Online:December 31, 2025
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Abstract
In practice, simulation parameters are estimated from finite data, which introduces model uncertainty. In “Selection of the Most Probable Best,” Taeho Kim, Kyoung-Kuk Kim, and Eunhye Song propose a new estimator of the optimum, the most probable best (MPB)...
The Fragility of Optimized Bandit Algorithms
- Lin Fan
  Corresponding Author
  Lin Fan
  [email protected]
  https://orcid.org/0000-0002-1144-1480
  Kellogg School of Management, Northwestern University, Evanston, Illinois 60208
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- Peter W. Glynn
  Peter W. Glynn
  [email protected]
  https://orcid.org/0000-0003-1370-6638
  Department of Management Science and Engineering, Stanford University, Stanford, California 94305
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Published Online:December 30, 2025
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Abstract
What Does the Distribution of Regret Look Like for Bandit Algorithms?In multiarmed bandit theory, the expected regret has long been the predominant performance measure studied in the literature. For the first time, in “The fragility of optimized bandit ...

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