scholarly journals Modelling human active search in optimizing black-box functions

2020 ◽  
Vol 24 (23) ◽  
pp. 17771-17785
Author(s):  
Antonio Candelieri ◽  
Riccardo Perego ◽  
Ilaria Giordani ◽  
Andrea Ponti ◽  
Francesco Archetti
Keyword(s):  
Active Learning ◽  
Bayesian Learning ◽  
Surrogate Models ◽  
Black Box ◽  
Bayesian Optimization ◽  
Learning Approaches ◽  
Function Learning ◽  
The Subject ◽  
Human Function ◽  
Exploration Exploitation

AbstractModelling human function learning has been the subject of intense research in cognitive sciences. The topic is relevant in black-box optimization where information about the objective and/or constraints is not available and must be learned through function evaluations. In this paper, we focus on the relation between the behaviour of humans searching for the maximum and the probabilistic model used in Bayesian optimization. As surrogate models of the unknown function, both Gaussian processes and random forest have been considered: the Bayesian learning paradigm is central in the development of active learning approaches balancing exploration/exploitation in uncertain conditions towards effective generalization in large decision spaces. In this paper, we analyse experimentally how Bayesian optimization compares to humans searching for the maximum of an unknown 2D function. A set of controlled experiments with 60 subjects, using both surrogate models, confirm that Bayesian optimization provides a general model to represent individual patterns of active learning in humans.

Molecular Modeling as the Spark for Active Learning Approaches for Interdisciplinary Biology Teaching

2018 ◽  
Author(s):  
Antoine Taly ◽  
Francesco Nitti ◽  
Marc Baaden ◽  
samuela pasquali
Keyword(s):  
High School ◽  
High School Students ◽  
Molecular Modeling ◽  
Active Learning ◽  
Computer Science ◽  
Poster Presentation ◽  
Learning Approaches ◽  
School Students ◽  
The Subject ◽  
The Origin Of Life

<div>We present here an interdisciplinary workshop on the subject of biomolecules offered to undergraduate and high-school students with the aim of boosting their interest toward all areas of science contributing to the study of life. The workshop involves Mathematics, Physics, Chemistry, Computer Science and Biology. Based on our own areas of research, molecular modeling is chosen as central axis as it involves all disciplines. In order to provide a strong biological motivation for the study of the dynamics of biomolecules, the theme of the workshop is the origin of life. </div><div>All sessions are built around active pedagogies, including games, and a final poster presentation.</div>

Molecular Modeling as the Spark for Active Learning Approaches for Interdisciplinary Biology Teaching

2018 ◽  
Author(s):  
Antoine Taly ◽  
Francesco Nitti ◽  
Marc Baaden ◽  
samuela pasquali
Keyword(s):  
High School ◽  
High School Students ◽  
Molecular Modeling ◽  
Active Learning ◽  
Computer Science ◽  
Poster Presentation ◽  
Learning Approaches ◽  
School Students ◽  
The Subject ◽  
The Origin Of Life

<div>We present here an interdisciplinary workshop on the subject of biomolecules offered to undergraduate and high-school students with the aim of boosting their interest toward all areas of science contributing to the study of life. The workshop involves Mathematics, Physics, Chemistry, Computer Science and Biology. Based on our own areas of research, molecular modeling is chosen as central axis as it involves all disciplines. In order to provide a strong biological motivation for the study of the dynamics of biomolecules, the theme of the workshop is the origin of life. </div><div>All sessions are built around active pedagogies, including games, and a final poster presentation.</div>

scholarly journals Molecular modelling as the spark for active learning approaches for interdisciplinary biology teaching

2019 ◽  
Vol 9 (3) ◽  
pp. 20180065 ◽  
Author(s):  
A. Taly ◽  
F. Nitti ◽  
M. Baaden ◽  
S. Pasquali
Keyword(s):  
High School ◽  
High School Students ◽  
Active Learning ◽  
Computer Science ◽  
Molecular Modelling ◽  
Learning Approaches ◽  
School Students ◽  
The Subject ◽  
The Origin Of Life ◽  
Active Pedagogy

We present here an interdisciplinary workshop on the subject of biomolecules offered to undergraduate and high school students with the aim of boosting their interest toward all areas of science contributing to the study of life. The workshop involves mathematics, physics, chemistry, computer science and biology. Based on our own areas of research, molecular modelling is chosen as the central axis as it involves all disciplines. To provide a strong biological motivation for the study of the dynamics of biomolecules, the theme of the workshop is the origin of life. All sessions are built around active pedagogy, including games, and a final poster presentation.

scholarly journals Active Function Learning

2018 ◽  
Author(s):  
Angela Jones ◽  
Eric Schulz ◽  
Björn Meder ◽  
Azzurra Ruggeri
Keyword(s):  
Active Learning ◽  
Information Search ◽  
Gaussian Process Regression ◽  
Linear Functions ◽  
Learning Approaches ◽  
Function Learning ◽  
Rule Based ◽  
Functional Relationships ◽  
Functional Relations ◽  
Search For Information

AbstractHow do people actively explore to learn about functional relationships, that is, how continuous inputs map onto continuous outputs? We introduce a novel paradigm to investigate information search in continuous, multi-feature function learning scenarios. Participants either actively selected or passively observed information to learn about an underlying linear function. We develop and compare different variants of rule-based (linear regression) and non-parametric (Gaussian process regression) active learning approaches to model participants’ active learning behavior. Our results show that participants’ performance is best described by a rule-based model that attempts to efficiently learn linear functions with a focus on high and uncertain outcomes. These results advance our understanding of how people actively search for information to learn about functional relations in the environment.

scholarly journals Multi-fidelity black-box optimization for time-optimal quadrotor maneuvers

2021 ◽  
pp. 027836492110333
Author(s):  
Gilhyun Ryou ◽  
Ezra Tal ◽  
Sertac Karaman
Keyword(s):  
Real World ◽  
High Speed ◽  
Analytical Approximation ◽  
Black Box ◽  
Bayesian Optimization ◽  
Optimization Framework ◽  
Quadrotor Aircraft ◽  
Time Optimal ◽  
Feasibility Constraints ◽  
Generation Problem

We consider the problem of generating a time-optimal quadrotor trajectory for highly maneuverable vehicles, such as quadrotor aircraft. The problem is challenging because the optimal trajectory is located on the boundary of the set of dynamically feasible trajectories. This boundary is hard to model as it involves limitations of the entire system, including complex aerodynamic and electromechanical phenomena, in agile high-speed flight. In this work, we propose a multi-fidelity Bayesian optimization framework that models the feasibility constraints based on analytical approximation, numerical simulation, and real-world flight experiments. By combining evaluations at different fidelities, trajectory time is optimized while the number of costly flight experiments is kept to a minimum. The algorithm is thoroughly evaluated for the trajectory generation problem in two different scenarios: (1) connecting predetermined waypoints; (2) planning in obstacle-rich environments. For each scenario, we conduct both simulation and real-world flight experiments at speeds up to 11 m/s. Resulting trajectories were found to be significantly faster than those obtained through minimum-snap trajectory planning.

Learning for infinitely divisible GARCH models in option pricing

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Fumin Zhu ◽  
Michele Leonardo Bianchi ◽  
Young Shin Kim ◽  
Frank J. Fabozzi ◽  
Hengyu Wu
Keyword(s):  
Option Pricing ◽  
Stock Returns ◽  
Bayesian Learning ◽  
Space Structure ◽  
Estimation Methods ◽  
Garch Models ◽  
Learning Approaches ◽  
Infinitely Divisible ◽  
Asymmetric Garch ◽  
Non Gaussian

AbstractThis paper studies the option valuation problem of non-Gaussian and asymmetric GARCH models from a state-space structure perspective. Assuming innovations following an infinitely divisible distribution, we apply different estimation methods including filtering and learning approaches. We then investigate the performance in pricing S&P 500 index short-term options after obtaining a proper change of measure. We find that the sequential Bayesian learning approach (SBLA) significantly and robustly decreases the option pricing errors. Our theoretical and empirical findings also suggest that, when stock returns are non-Gaussian distributed, their innovations under the risk-neutral measure may present more non-normality, exhibit higher volatility, and have a stronger leverage effect than under the physical measure.

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