Simulating, Fast and Slow: Learning Policies for Black-Box Optimization
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909218523578368 |
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| author | Massoli, Fabio Valerio Bakker, Tim Hehn, Thomas Orekondy, Tribhuvanesh Behboodi, Arash |
| author_facet | Massoli, Fabio Valerio Bakker, Tim Hehn, Thomas Orekondy, Tribhuvanesh Behboodi, Arash |
| contents | In recent years, solving optimization problems involving black-box simulators has become a point of focus for the machine learning community due to their ubiquity in science and engineering. The simulators describe a forward process $f_{\mathrm{sim}}: (ψ, x) \rightarrow y$ from simulation parameters $ψ$ and input data $x$ to observations $y$, and the goal of the optimization problem is to find parameters $ψ$ that minimize a desired loss function. Sophisticated optimization algorithms typically require gradient information regarding the forward process, $f_{\mathrm{sim}}$, with respect to the parameters $ψ$. However, obtaining gradients from black-box simulators can often be prohibitively expensive or, in some cases, impossible. Furthermore, in many applications, practitioners aim to solve a set of related problems. Thus, starting the optimization ``ab initio", i.e. from scratch, each time might be inefficient if the forward model is expensive to evaluate. To address those challenges, this paper introduces a novel method for solving classes of similar black-box optimization problems by learning an active learning policy that guides a differentiable surrogate's training and uses the surrogate's gradients to optimize the simulation parameters with gradient descent. After training the policy, downstream optimization of problems involving black-box simulators requires up to $\sim$90\% fewer expensive simulator calls compared to baselines such as local surrogate-based approaches, numerical optimization, and Bayesian methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_04261 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Simulating, Fast and Slow: Learning Policies for Black-Box Optimization Massoli, Fabio Valerio Bakker, Tim Hehn, Thomas Orekondy, Tribhuvanesh Behboodi, Arash Machine Learning In recent years, solving optimization problems involving black-box simulators has become a point of focus for the machine learning community due to their ubiquity in science and engineering. The simulators describe a forward process $f_{\mathrm{sim}}: (ψ, x) \rightarrow y$ from simulation parameters $ψ$ and input data $x$ to observations $y$, and the goal of the optimization problem is to find parameters $ψ$ that minimize a desired loss function. Sophisticated optimization algorithms typically require gradient information regarding the forward process, $f_{\mathrm{sim}}$, with respect to the parameters $ψ$. However, obtaining gradients from black-box simulators can often be prohibitively expensive or, in some cases, impossible. Furthermore, in many applications, practitioners aim to solve a set of related problems. Thus, starting the optimization ``ab initio", i.e. from scratch, each time might be inefficient if the forward model is expensive to evaluate. To address those challenges, this paper introduces a novel method for solving classes of similar black-box optimization problems by learning an active learning policy that guides a differentiable surrogate's training and uses the surrogate's gradients to optimize the simulation parameters with gradient descent. After training the policy, downstream optimization of problems involving black-box simulators requires up to $\sim$90\% fewer expensive simulator calls compared to baselines such as local surrogate-based approaches, numerical optimization, and Bayesian methods. |
| title | Simulating, Fast and Slow: Learning Policies for Black-Box Optimization |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2406.04261 |