arXiv research

Survey of Gaussian process constraints for modeling expensive data.

Bayesian search optimizes exploration of feasible solutions under expensive constraints.

This work presents PESMOC, Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints, an information-based strategy for the simultaneous optimization of multiple expensive-to-evaluate black-box functions under the presence of several constraints. PESMOC can hence be used to solve a wide range…

USeMOC framework reduces expensive simulations for MO optimization with constraints.

Parallel BO method for multi-objective optimization with constraints.

MESMOC optimizes constrained multi-objective problems efficiently.

Bayesian optimisation tackles expensive black-box functions with constraints.

The notion of expense in Bayesian optimisation generally refers to the uniformly expensive cost of function evaluations over the whole search space. However, in some scenarios, the cost of evaluation for black-box objective functions is non-uniform since different inputs from search space may incur different costs for …

MVRSM optimizes expensive functions with mixed variables, outperforming state-of-the-art methods.

A new algorithm POGO optimizes thousands of orthogonal matrices efficiently.

A new method for optimizing black-box problems with constraints.

Deep learning for supervised learning has achieved astonishing performance in various machine learning applications. However, annotated data is expensive and rare. In practice, only a small portion of data samples are annotated. Pseudo-ensembling-based approaches have achieved state-of-the-art results in computer visio…

Novel method for efficient optimization of noisy, expensive hybrid models.

Unknown constraints arise in many types of expensive black-box optimization problems. Several methods have been proposed recently for performing Bayesian optimization with constraints, based on the expected improvement (EI) heuristic. However, EI can lead to pathologies when used with constraints. For example, in the c…

UA-SABI uses surrogates to speed up Bayesian inference for expensive models.

ACOL learns constraints from human preferences in driving simulations.

New method optimizes processes under constraints using bivariate Gaussian models.

A multilevel optimization method for constrained problems.

UCRL-CMDP algorithm optimizes RL with constraints on average costs.

Deep generative neural networks have proven effective at both conditional and unconditional modeling of complex data distributions. Conditional generation enables interactive control, but creating new controls often requires expensive retraining. In this paper, we develop a method to condition generation without retrai…

Proposes SPCA to incorporate structural constraints in model identification.

This paper deals with sparse feature selection and grouping for classification and regression. The classification or regression problems under consideration consists in minimizing a convex empirical risk function subject to an $\ell^1$ constraint, a pairwise $\ell^\infty$ constraint, or a pairwise $\ell^1$ constraint. …

We propose a method to impose homogeneous linear inequality constraints of the form $Ax\leq 0$ on neural network activations. The proposed method allows a data-driven training approach to be combined with modeling prior knowledge about the task. One way to achieve this task is by means of a projection step at test time…

Adding inequality constraints (e.g. boundedness, monotonicity, convexity) into Gaussian processes (GPs) can lead to more realistic stochastic emulators. Due to the truncated Gaussianity of the posterior, its distribution has to be approximated. In this work, we consider Monte Carlo (MC) and Markov Chain Monte Carlo (MC…

Develops a new solver for optimizing with stochastic dominance constraints.

Control Barrier Functions (CBF) have been recently utilized in the design of provably safe feedback control laws for nonlinear systems. These feedback control methods typically compute the next control input by solving an online Quadratic Program (QP). Solving QP in real-time can be a computationally expensive process …

New method avoids failures in physics-constrained systems using active learning.

New RL algorithm learns good actions from offline data, reducing uncertainty and divergence.

Bayesian optimization reduces CVaR portfolio risk.

In most machine learning applications, classification accuracy is not the primary metric of interest. Binary classifiers which face class imbalance are often evaluated by the $F_β$ score, area under the precision-recall curve, Precision at K, and more. The maximization of many of these metrics can be expressed as a con…

Automates design of lightweight neural networks for image classification.

Electric vehicles (EVs) have been gaining popularity due to their environmental friendliness and efficiency. EV charging station networks are scalable solutions for supporting increasing numbers of EVs within modern electric grid constraints, yet few tools exist to aid the physical configuration design of new networks.…

New algorithms for efficient causal interventions with budget constraints and without constraints.

Novel NAS method balances performance and hardware metrics efficiently.

Improved MESMOC+ optimizes constrained multi-objective problems efficiently.

In predictive maintenance, model performance is usually assessed by means of precision, recall, and F1-score. However, employing the model with best performance, e.g. highest F1-score, does not necessarily result in minimum maintenance cost, but can instead lead to additional expenses. Thus, we propose to perform model…

A new method solves variational inequality problems with multiple constraints without needing optimal Lagrange multipliers.

A new method combines SciML and UQ with physical constraints.

FedSGM tackles constrained federated learning with unified framework.

In this paper, we aim to learn a low-dimensional Euclidean representation from a set of constraints of the form "item j is closer to item i than item k". Existing approaches for this "ordinal embedding" problem require expensive optimization procedures, which cannot scale to handle increasingly larger datasets. To addr…

PAC-MOO optimizes constrained multi-objective problems with preferences.

Motivated by the asset-liability management of a nuclear power plant operator, we consider the problem of finding the least expensive portfolio, which outperforms a given set of stochastic benchmarks. For a specified loss function, the expected shortfall with respect to each of the benchmarks weighted by this loss func…

Structured regularizers enable faster optimization on SPD manifolds with constraints.

A new method for efficient optimization of expensive simulations on HPC.

The paper proposes modern computational methods for optimizing reinsurance contracts.

Safe active learning for multi-output Gaussian processes reduces data acquisition costs and ensures safety.

Bayesian optimization tackles constrained high-dimensional problems with penalties and trust regions.

Improved online learning with time-varying constraints for complex domains.