Bayesian optimization (BO) and its batch extensions are successful for optimizing expensive black-box functions. However, these traditional BO approaches are not yet ideal for optimizing less expensive functions when the computational cost of BO can dominate the cost of evaluating the blackbox function. Examples of the…
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48 results for “expensive models”
Many expensive black-box optimisation problems are sensitive to their inputs. In these problems it makes more sense to locate a region of good designs, than a single-possibly fragile-optimal design. Expensive black-box functions can be optimised effectively with Bayesian optimisation, where a Gaussian process is a popu…
Uncertainty-Aware Surrogate-based Amortized Bayesian Inference for Computationally Expensive Modelsstat.ML
UA-SABI uses surrogates to speed up Bayesian inference for expensive models.
problem Inference for computationally expensive models is slow and uncertain.
method Combines surrogate modeling with Amortized Bayesian Inference (ABI) to propagate uncertainties.
result Reliable, fast, and repeated Bayesian inference for expensive models is achieved.
Bayesian search optimizes exploration of feasible solutions under expensive constraints.
problem Identifying feasible solutions in computationally expensive constraint spaces.
method Bayesian models with an acquisition function for efficient exploration and exploitation.
result The proposed acquisition function improves the prediction of feasibility.
We present a simple one-parameter model for spatially localised evolving agents competing for spatially localised resources. The model considers selling agents able to evolve their pricing strategy in competition for a fixed market. Despite its simplicity, the model displays extraordinarily rich behavior. In addition t…
This paper develops a method to approximate the whole Pareto set for expensive multi-objective optimization.
problem Finding an approximate Pareto front with limited expensive evaluations.
method A novel learning-based method to approximate the whole Pareto set for multi-objective Bayesian optimization (MOBO).
result The method approximates the whole Pareto set, not just a finite set, for MOBO.
NoFAS combines variational inference and adaptive surrogate models for efficient inference of computationally expensive models.
problem Efficient inference of parameters from data with computationally expensive models.
method Variational inference with normalizing flow and adaptive surrogate model training.
result NoFAS reduces computational cost without sacrificing inferential accuracy.
New Bayesian optimization models for efficient material screening.
problem Efficiently screening materials with expensive and cheap tests.
method Flexible multi-test Bayesian optimization models with complex relationships.
result Demonstrated power on synthetic and real data.
M-estimation under Two-Phase Multiwave Sampling with Applications to Prediction-Powered Inferencestat.ME
Improved statistical inference for expensive data using machine learning predictions.
problem Statistical inference under adaptive two-phase multiwave sampling with expensive measurements.
method Multiwave Predict-Then-Debias estimator combining proxy information and expensive measurements.
result Valid estimators and confidence intervals for M-estimation under adaptive sampling.
We study the problem of determining risk-minimizing investment strategies for insurance payment processes in the presence of taxes and expenses. We consider the situation where taxes and expenses are paid continuously and symmetrically and introduce the concept of tax- and expense-modified risk-minimization. Risk-minim…
Gemini uses inexpensive measurements to correct biases in expensive property evaluations.
problem Accurate estimation of materials properties using expensive measurements is hindered in scientific discovery campaigns.
method Gemini is a data-driven model that corrects systematic biases between property evaluation methods using inexpensive measurements.
result Gemini reduces the number of expensive evaluations needed for Bayesian optimization in materials discovery.
New method uses low-fidelity simulations to efficiently infer parameters of high-fidelity models.
problem Challenges in inferring parameters of computationally expensive high-fidelity models.
method Multifidelity simulation-based inference using transfer learning and adaptive selection of high-fidelity parameters.
result Significant reduction in the number of high-fidelity simulations required for inference.
Survey of Gaussian process constraints for modeling expensive data.
problem Modeling expensive data with physical constraints.
method Overview of various Gaussian process constraints and their implementation.
result Discussion of computational challenges introduced by constraints.
Study examines time-varying betas and their volatility in bank interest income and expense margins.
problem Understanding the variability of bank betas and their impact on net interest margins.
method Used state-space methods to estimate time-varying betas and conditional volatility.
result Substantial variation in interest income and expense betas, leading to varying net interest margin coefficients.
SVH-PSL uses Stein Variational Gradient Descent and Hypernetworks to improve Pareto set learning for expensive MOO.
problem Fragmented surrogate models and pseudo-local optima in expensive multi-objective optimization problems.
method SVH-PSL integrates Stein Variational Gradient Descent (SVGD) with Hypernetworks to address fragmentation and pseudo-local optima.
result SVH-PSL significantly improves the quality of the learned Pareto set, offering a promising solution for expensive MOO.
Machine learning models are vulnerable to adversarial examples. Iterative adversarial training has shown promising results against strong white-box attacks. However, adversarial training is very expensive, and every time a model needs to be protected, such expensive training scheme needs to be performed. In this paper,…
ε-shotgun optimizes expensive function evaluations in parallel.
problem Optimizing expensive black-box functions efficiently.
method ε-greedy batch Bayesian optimisation.
result ε-shotgun outperforms state-of-the-art batch methods.
Improved MCMC sampling for expensive, irregular likelihoods.
problem Bayesian inference challenges with irregular, expensive likelihoods.
method Adapt subset samplers, introduce data-driven proxies, adaptive controller.
result Improved HINTS algorithm achieves best sampling error in fixed budget.
A new MCMC method combines low and high-fidelity models to reduce computation.
problem Inefficient computation of expensive target densities in scientific applications.
method Pseudo-marginal MCMC approach using a telescoping series of low-fidelity models.
result Asymptotically exact multi-fidelity MCMC algorithms for reduced computational cost.
More accurate machine learning models often demand more computation and memory at test time, making them difficult to deploy on CPU- or memory-constrained devices. Teacher-student compression (TSC), also known as distillation, alleviates this burden by training a less expensive student model to mimic the expensive teac…
The paper proposes a scalable framework for uncertainty quantification and propagation in surrogate-based Bayesian inference.
problem Uncertainty in surrogate models and its impact on inference and decision-making.
method Bayesian inference methods for surrogate models with measurement data.
result Scalable framework for uncertainty quantification and propagation in surrogate models.
LOL-GP model improves surrogate modeling of expensive simulators.
problem Costly computer simulations for complex systems.
method Local transfer learning Gaussian process.
result Improved surrogate performance over existing methods.
We study statistical calibration, i.e., adjusting features of a computational model that are not observable or controllable in its associated physical system. We focus on functional calibration, which arises in many manufacturing processes where the unobservable features, called calibration variables, are a function of…
Expected Diverse Utility (EDU): Diverse Bayesian Optimization of Expensive Computer Simulatorsstat.AP
EDU method finds diverse optimal solutions for expensive simulators.
problem Optimizing expensive black-box simulators for diverse solutions.
method EDU method searches for diverse locally-optimal solutions within a tolerance level.
result EDU yields a closed-form acquisition function facilitating efficient sequential queries.
"No free lunch" results state the impossibility of obtaining meaningful bounds on the error of a learning algorithm without prior assumptions and modelling. Some models are expensive (strong assumptions, such as as subgaussian tails), others are cheap (simply finite variance). As it is well known, the more you pay, the…
A Fully Bayesian Gradient-Free Supervised Dimension Reduction Method using Gaussian Processesstat.ML
Gradient-free method reduces dimensionality without gradients for expensive models.
problem Reducing high-dimensional input spaces for expensive models without gradient information.
method Fully Bayesian, gradient-free approach using Gaussian processes.
result Improves active subspace recovery and probabilistic prediction accuracy with limited data.
Novel CE-method variants reduce local minima convergence with fewer function evaluations.
problem Local minima and expensive function evaluations in optimization.
method Surrogate model-based CE-method variants to reduce local minima convergence.
result Surrogate model-based approach reduces local minima convergence using fewer function evaluations.
Enhances PCE surrogates using transfer learning for expensive simulations.
problem Over-sampling in PCE for expensive forward models.
method Transfer learning from similar tasks to a new task with limited training data.
result Improves scalability and accuracy of PCE surrogates.
ABae efficiently computes subset means with expensive predicates using stratified sampling.
problem Computing subset means with expensive predicates efficiently.
method Stratified sampling and proxy models.
result Mean squared error of when is split evenly between stages.
Framework optimizes expensive manufacturing processes efficiently.
problem Optimizing input parameters for advanced manufacturing methods.
method Bayesian optimization with tailored acquisition function and parallel acquisition.
result Framework efficiently finds optimal parameters with minimal process cost.
Despite incredible recent advances in machine learning, building machine learning applications remains prohibitively time-consuming and expensive for all but the best-trained, best-funded engineering organizations. This expense comes not from a need for new and improved statistical models but instead from a lack of sys…
Compressed particle methods for expensive models with application in Astronomy and Remote Sensingcs.CE
Novel method reduces costly model evaluations in inference problems.
problem Efficiently approximating complex, costly model integrals.
method Compressed Monte Carlo (CMC) scheme for selecting model evaluations.
result Empirical evidence of method's performance in astronomy and remote sensing.
BDC uses Distance Correlation for efficient Bayesian optimization of expensive functions.
problem Efficiently optimizing expensive black-box functions with Bayesian methods.
method Integrates Bayesian optimization with Distance Correlation for automatic exploration and exploitation.
result BDC performs similarly to popular BO methods on benchmark tests and real terrain optimization.
Efficiently estimates SAGE values using causal structure learning.
problem Computational infeasibility of exact SAGE calculations.
method Uses causal structure learning to identify conditional independencies and accelerate SAGE approximation.
result Empirically demonstrates efficient and accurate estimation of SAGE values.
USeMOC framework reduces expensive simulations for MO optimization with constraints.
problem Efficiently optimizing multi-objective problems with constraints using expensive function evaluations.
method USeMOC framework uses surrogate models to identify promising candidates and selects the best based on uncertainty.
result USeMOC achieves more than 90% reduction in function evaluations for circuit optimization.
New test assesses probabilistic model calibration without expensive approximations.
problem Assessing calibration of probabilistic models with scores.
method Kernel Calibration Conditional Stein Discrepancy (KCCSD) test using new score-based kernels.
result Control over type-I error with improved scalability and efficiency.
We consider the class of optimization problems arising from computationally intensive L1-regularized M-estimators, where the function or gradient values are very expensive to compute. A particular instance of interest is the L1-regularized MLE for learning Conditional Random Fields (CRFs), which are a popular class of …
The complex and computationally expensive nature of landscape evolution models pose significant challenges in the inference and optimisation of unknown parameters. Bayesian inference provides a methodology for estimation and uncertainty quantification of unknown model parameters. In our previous work, we developed para…
New approach uses low-fidelity data to train ML models efficiently.
problem Training ML models with scarce high-fidelity data leads to high variance and poor generalization.
method Multifidelity linear regression using approximate control variates.
result Multifidelity training achieves similar accuracy with reduced high-fidelity data.
Obtaining detailed and reliable data about local economic livelihoods in developing countries is expensive, and data are consequently scarce. Previous work has shown that it is possible to measure local-level economic livelihoods using high-resolution satellite imagery. However, such imagery is relatively expensive to …
Cost-aware SBI reduces expensive simulations in complex models.
problem High computational cost in simulating complex models.
method Combination of rejection and self-normalised importance sampling.
result Significant reduction in overall cost of inference.
The graphics processing unit (GPU) has emerged as a powerful and cost effective processor for general performance computing. GPUs are capable of an order of magnitude more floating-point operations per second as compared to modern central processing units (CPUs), and thus provide a great deal of promise for computation…
We study reinforcement learning of chatbots with recurrent neural network architectures when the rewards are noisy and expensive to obtain. For instance, a chatbot used in automated customer service support can be scored by quality assurance agents, but this process can be expensive, time consuming and noisy. Previous …
New SMC samplers improve stochastic optimisation efficiency.
problem Optimizing functions with intractable gradients in machine learning and statistics.
method Sequential Monte Carlo (SMC) samplers for stochastic optimisation.
result Significant computational gains achieved with SMC approximations.
Efficiently identifies key input variables for expensive functions using active learning.
problem Efficiently identify key input variables for expensive, black-box functions.
method Proposes novel active learning acquisition functions targeting derivative-based global sensitivity measures (DGSMs) under Gaussian process surrogate models.
result Active learning substantially enhances sample efficiency of DGSM estimation, especially with limited evaluation budgets.
A new Bayesian method optimizes time-dependent expensive functions with lookahead.
problem Maximizing a time-dependent, expensive oracle with limited evaluations.
method Recursive, two-step lookahead expected payoff (r2LEY) acquisition function.
result r2LEY outperforms myopic methods in synthetic and real-world datasets.
MF BO combines MFO and BO to optimize expensive problems.
problem Expensive engineering design optimization problems.
method Gaussian process-based multi-fidelity surrogates and acquisition functions.
result Structured understanding of MF BO.
Data-efficient PDE operator learning without expensive simulations.
problem Expensive numerical PDE solutions limit data efficiency in machine learning.
method Unsupervised pretraining and in-context learning.
result Highly data-efficient and more generalizable than conventional models.