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(Deadline : August 1, 2023)

  1. Please specify the order of presentations for this MS.
    If there are talks for which the authors have not yet submitted an abstract, please submit it here as soon as possible. You cannot specify the order of the presentations that have not been submitted.
  2. Please specify the presentation format for each of the presentations.
  3. Please register the chairperson(s).
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[00702] Sequential Decision Making for Optimization, Learning and Search

  • Session Time & Room : 4C (Aug.24, 13:20-15:00) @F412 [Chair: Michael McCourt]
    Please specify the order of presentations for this session.
    You can change the order by clicking on △ & ▼. (Click here for latest order)
    1. △ ▼ : [05274]
    2. △ ▼ : [05288]
    3. △ ▼ : [05604]
    4. △ ▼ : [04910]
  • Type : Proposal of Minisymposium
  • Submitted : 2022-12-21
  • Abstract : Key problems such as hyperparameter optimization, model calibration, and inverse/optimal design often involve exploring design spaces to identify desirable designs for one or more objectives of great value and great cost. Intelligently experimenting in this design space is fundamental to gaining valuable, actionable insights in a viable amount of time. In this minisymposium, we will discuss some of the common methodologies for identifying high-performing and optimal designs, including Bayesian and genetic methods, and several exciting applications which motivate the research in this field.
  • Organizer(s) : Michael McCourt
  • Classification : 60G15, 90C26, 62C10
  • Minisymposium Program :
    • 00702 (1/1) : 4C @F412 [Chair: Michael McCourt]
      • [05274] Constraint active search as an alternative to optimization
        • Format : Talk at Waseda University [Change to Online]
        • Author(s) :
          • Michael McCourt (Unaffiliated)
        • Abstract : Bayesian optimization is a sample efficient method for identifying high performing configurations of a black box function. This strategy is extremely powerful, but it is often a misguided tool for many practical circumstances -- problems with heavy noise, input/output imprecision, many objectives, discrepancy in cost of objective evalution, or a human-in-the-loop defined objective/preference all are situations where optimization may be the wrong strategy. Here, we discuss the shortcomings of optimization and propose an alternate strategy: the search for a satisfactory set of outcomes, as guided by user-defined performance thresholds. We refer to this as Constraint Active Search, and we present our motivating application as well as some theoretical analysis.
      • [05288] Evolution Strategies: Principles and Practical Issues
        • Format : Online Talk on Zoom [Change to Onsite]
        • Author(s) :
          • Masahiro Nomura (CyberAgent)
        • Abstract : Evolution strategies (ES) is one of the most powerful frameworks for black-box continuous optimization. This talk will describe the design principles behind the empirical success of ES and the representative methods that have often been employed in science and industry. In addition, key issues that may be encountered when using ES in practice will be discussed.
      • [05604] Optuna: A Software to Solve Black-box Optimization
        • Format : Talk at Waseda University [Change to Online]
        • Author(s) :
          • Hideaki Imamura (Preferred Networks, Inc)
        • Abstract : Optuna is a software tool for solving black-box optimization problems. It provides a Pythonic interface to describe the search space and objective functions. It supports various algorithms, extensive visualization capabilities, and easy distributed optimization. In this presentation, we will introduce some of the latest features of Optuna and discuss the problem awareness that arises in black-box optimization and its application field, specifically hyperparameter optimization in machine learning.
      • [04910] Combinatorial 3D Shape Assembly with Sequential Decision-Making Processes
        • Format : Online Talk on Zoom [Change to Onsite]
        • Author(s) :
          • Jungtaek Kim (University of Pittsburgh)
        • Abstract : We require unit primitives, e.g., voxels and points, to create a 3D shape. In particular, if we consider a way to construct a 3D shape with the connectivity of primitives, a problem of 3D shape creation is characterized by sequential and combinatorial properties. By dealing with the sequential and combinatorial properties, we present a method for 3D shape assembly using sequential decision-making processes, i.e., Bayesian optimization and reinforcement learning.