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Black-Box Optimization for Automated Discovery. [PDF]
Accounts of Chemical Research, 2021 ConspectusIn chemistry and materials science, researchers and engineers discover, design, and optimize chemical compounds or materials with their professional knowledge and techniques.
Kei Terayama +3 more
semanticscholar +3 more sources
Application of QUBO solver using black-box optimization to structural design for resonance avoidance. [PDF]
Sci Rep, 2022 Quadratic unconstrained binary optimization (QUBO) solvers can be applied to design an optimal structure to avoid resonance. QUBO algorithms that work on a classical or quantum device have succeeded in some industrial applications.
Matsumori T, Taki M, Kadowaki T.
europepmc +3 more sources
Diffusion Models for Black-Box Optimization [PDF]
International Conference on Machine Learning, 2023 The goal of offline black-box optimization (BBO) is to optimize an expensive black-box function using a fixed dataset of function evaluations. Prior works consider forward approaches that learn surrogates to the black-box function and inverse approaches ...
S. Krishnamoorthy +2 more
semanticscholar +3 more sources
Efficient unconstrained black box optimization [PDF]
Mathematical Programming Computation, 2022 For the unconstrained optimization of black box functions, this paper introduces a new randomized algorithm called VRBBO. In practice, VRBBO matches the quality of other state-of-the-art algorithms for finding, in small and large dimensions, a local ...
M. Kimiaei, A. Neumaier
semanticscholar +3 more sources
Generative Pretraining for Black-Box Optimization [PDF]
International Conference on Machine Learning, 2022 Many problems in science and engineering involve optimizing an expensive black-box function over a high-dimensional space. For such black-box optimization (BBO) problems, we typically assume a small budget for online function evaluations, but also often ...
S. Krishnamoorthy +2 more
semanticscholar +3 more sources
Extensive antibody search with whole spectrum black-box optimization [PDF]
Scientific ReportsIn designing functional biological sequences with machine learning, the activity predictor tends to be inaccurate due to shortage of data. Top ranked sequences are thus unlikely to contain effective ones.
Andrejs Tučs +7 more
doaj +2 more sources
Reinforced In-Context Black-Box Optimization
Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial IntelligenceBlack-Box Optimization (BBO) has found successful applications in many fields of science and engineering. Recently, there has been a growing interest in meta-learning particular components of BBO algorithms to speed up optimization and get rid of tedious
Lei Song +7 more
semanticscholar +3 more sources
The strength of Nesterov's accelerated gradient in boosting transferability of stealthy adversarial attacks. [PDF]
PLoS ONEDeep neural networks have been shown to be highly vulnerable to adversarial examples-inputs crafted to mislead models by adding subtle, human-imperceptible perturbations. Transferability and stealthiness are two crucial metrics for evaluating adversarial
Chen Lin, Sheng Long
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Sharpness-Aware Black-Box Optimization
arXiv.orgBlack-box optimization algorithms have been widely used in various machine learning problems, including reinforcement learning and prompt fine-tuning.
Feiyang Ye +5 more
semanticscholar +3 more sources
Black-Box Optimization Using Geodesics in Statistical Manifolds
Entropy, 2015 Information geometric optimization (IGO) is a general framework for stochastic optimization problems aiming at limiting the influence of arbitrary parametrization choices: the initial problem is transformed into the optimization of a smooth function on a
Jérémy Bensadon
doaj +3 more sources