1,588 research outputs found
Bayesian optimization in adverse scenarios
Optimization problems with expensive-to-evaluate objective functions are ubiquitous in scientific and industrial settings. Bayesian optimization has gained widespread acclaim for optimizing expensive (and often black box) functions due to its theoretical performance guarantees and empirical sample efficiency in a variety of settings. Nevertheless, many practical scenarios remain where prevailing Bayesian optimization techniques fall short. We consider four such scenarios. First, we formalize the optimization problem where the goal is to identify robust designs with respect to multiple objective functions that are subject to input noise. Such robust design problems frequently arise, for example, in manufacturing settings where fabrication can only be performed with limited precision. We propose a method that identifies a set of optimal robust designs, where each design provides probabilistic guarantees jointly on multiple objectives. Second, we consider sample-efficient high-dimensional multi-objective optimization. This line of research is motivated by the challenging task of designing optical displays for augmented reality to optimize visual quality and efficiency, where the designs are specified by high-dimensional parameterizations governing complex geometries. Our proposed trust-region based algorithm yields order-of-magnitude improvements in sample complexity on this problem. Third, we consider multi-objective optimization of expensive functions with variable-cost, decoupled, and/or multi-fidelity evaluations and propose a Bayes-optimal, non-myopic acquisition function, which significantly improves sample efficiency in scenarios with incomplete information. We apply this to hardware-aware neural architecture search where the objective, on-device latency and model accuracy, can often be evaluated independently. Fourth, we consider the setting where the search space consists of discrete (and potentially continuous) parameters. We propose a theoretically grounded technique that uses a probabilistic reparameterization to transform the discrete or mixed inner optimization problem into a continuous one leading to more effective Bayesian optimization policies. Together, this thesis provides a playbook for Bayesian optimization in several practical adverse scenarios
OPTIMAL COMPUTING BUDGET ALLOCATION FOR STOCHASTIC SIMULATION OPTIMIZATION
Ph.DDOCTOR OF PHILOSOPH
Multi-surrogate Assisted Efficient Global Optimization for Discrete Problems
Decades of progress in simulation-based surrogate-assisted optimization and
unprecedented growth in computational power have enabled researchers and
practitioners to optimize previously intractable complex engineering problems.
This paper investigates the possible benefit of a concurrent utilization of
multiple simulation-based surrogate models to solve complex discrete
optimization problems. To fulfill this, the so-called Self-Adaptive
Multi-surrogate Assisted Efficient Global Optimization algorithm (SAMA-DiEGO),
which features a two-stage online model management strategy, is proposed and
further benchmarked on fifteen binary-encoded combinatorial and fifteen ordinal
problems against several state-of-the-art non-surrogate or single surrogate
assisted optimization algorithms. Our findings indicate that SAMA-DiEGO can
rapidly converge to better solutions on a majority of the test problems, which
shows the feasibility and advantage of using multiple surrogate models in
optimizing discrete problems
MULTI-FIDELITY OPTIMIZATION WITH GAUSSIAN REGRESSION ON ORDINAL TRANSFORMATION SPACE
Master'sMASTER OF ENGINEERIN
Multi-Fidelity Methods for Optimization: A Survey
Real-world black-box optimization often involves time-consuming or costly
experiments and simulations. Multi-fidelity optimization (MFO) stands out as a
cost-effective strategy that balances high-fidelity accuracy with computational
efficiency through a hierarchical fidelity approach. This survey presents a
systematic exploration of MFO, underpinned by a novel text mining framework
based on a pre-trained language model. We delve deep into the foundational
principles and methodologies of MFO, focusing on three core components --
multi-fidelity surrogate models, fidelity management strategies, and
optimization techniques. Additionally, this survey highlights the diverse
applications of MFO across several key domains, including machine learning,
engineering design optimization, and scientific discovery, showcasing the
adaptability and effectiveness of MFO in tackling complex computational
challenges. Furthermore, we also envision several emerging challenges and
prospects in the MFO landscape, spanning scalability, the composition of lower
fidelities, and the integration of human-in-the-loop approaches at the
algorithmic level. We also address critical issues related to benchmarking and
the advancement of open science within the MFO community. Overall, this survey
aims to catalyze further research and foster collaborations in MFO, setting the
stage for future innovations and breakthroughs in the field.Comment: 47 pages, 9 figure
Generative-Discriminative Complementary Learning
Majority of state-of-the-art deep learning methods are discriminative
approaches, which model the conditional distribution of labels given inputs
features. The success of such approaches heavily depends on high-quality
labeled instances, which are not easy to obtain, especially as the number of
candidate classes increases. In this paper, we study the complementary learning
problem. Unlike ordinary labels, complementary labels are easy to obtain
because an annotator only needs to provide a yes/no answer to a randomly chosen
candidate class for each instance. We propose a generative-discriminative
complementary learning method that estimates the ordinary labels by modeling
both the conditional (discriminative) and instance (generative) distributions.
Our method, we call Complementary Conditional GAN (CCGAN), improves the
accuracy of predicting ordinary labels and can generate high-quality instances
in spite of weak supervision. In addition to the extensive empirical studies,
we also theoretically show that our model can retrieve the true conditional
distribution from the complementarily-labeled data
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