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.
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.
MESMOC optimizes constrained multi-objective problems efficiently.
problem Constrained multi-objective optimization with expensive function evaluations.
method Max-value Entropy Search in the output space.
result MESMOC selects high-quality Pareto solutions efficiently.
A new method for faster multi-objective optimization by evaluating objectives separately.
problem Finding the Pareto front of trade-offs between multiple objectives efficiently.
method Knowledge Gradient with decoupled evaluations, accounting for different costs.
result The method significantly outperforms existing approaches in terms of evaluation cost.
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.
Bayesian optimization with preference learning identifies preferred solutions in multi-objective problems.
problem Optimizing multiple criteria with decision maker preferences in expensive functions.
method Bayesian optimization with interactive preference learning and active acquisition function.
result Identifies the most preferred solution with reduced interaction cost.
MBORE optimizes multi-objective problems using density-ratio estimation.
problem Optimizing complex, multi-objective functions with expensive evaluations.
method Extends BORE to multi-objective Bayesian optimisation, using density-ratio estimation.
result MBORE outperforms BO on high-dimensional and real-world problems.
Many objective Bayesian optimization tackles redundant objectives in expensive black-box functions.
problem Efficiently optimizing multiple expensive and noisy black-box functions with redundant objectives.
method Proposes a metric to identify redundant objectives and a Bayesian optimization algorithm to stop evaluating them.
result Reduces computational cost by stopping evaluation of redundant objectives, improving efficiency.
The design of machine learning systems often requires trading off different objectives, for example, prediction error and energy consumption for deep neural networks (DNNs). Typically, no single design performs well in all objectives; therefore, finding Pareto-optimal designs is of interest. The search for Pareto-optim…
A framework for efficient multi-objective optimization using entropy search.
problem Optimizing expensive black-box functions with multiple objectives.
method Output space entropy search (OSE) to minimize resource cost.
result Improves efficiency and accuracy in multi-objective optimization.
Framework optimizes multiple objectives considering input uncertainty.
problem Efficiently optimizing multiple objectives with input uncertainty.
method Robust Gaussian Process model and two-stage Bayesian optimization process.
result Found a robust Pareto frontier considering input uncertainty.
Many real world applications can be framed as multi-objective optimization problems, where we wish to simultaneously optimize for multiple criteria. Bayesian optimization techniques for the multi-objective setting are pertinent when the evaluation of the functions in question are expensive. Traditional methods for mult…
Parallel BO method for multi-objective optimization with constraints.
problem Optimizing multiple objectives under constraints with expensive evaluations.
method PPESMOC, a batch method for simultaneous optimization of black-box functions.
result Empirical evidence shows PPESMOC is effective for multi-objective optimization with constraints.
Multi-objective optimization aims at finding trade-off solutions to conflicting objectives. These constitute the Pareto optimal set. In the context of expensive-to-evaluate functions, it is impossible and often non-informative to look for the entire set. As an end-user would typically prefer a certain part of the objec…
Proposes MOGFNs for generating diverse Pareto optimal solutions in multi-objective optimization.
problem Generating diverse candidates in multi-objective optimization with conflicting objectives.
method Introduces MOGFNs based on GFlowNets, with two variants: MOGFN-PC and MOGFN-AL.
result Improved candidate diversity compared to existing methods.
MORBO improves multi-objective BO for high-dimensional problems.
problem Optimizing multiple objectives in high-dimensional spaces with expensive evaluations.
method Parallel local BO in multiple regions with coordinated strategy.
result Significant improvement in sample efficiency for high-dimensional problems.
This article addresses the problem of derivative-free (single- or multi-objective) optimization subject to multiple inequality constraints. Both the objective and constraint functions are assumed to be smooth, non-linear and expensive to evaluate. As a consequence, the number of evaluations that can be used to carry ou…
PAC-MOO optimizes constrained multi-objective problems with preferences.
problem Optimizing with constraints and practitioner preferences over objectives.
method Preference-aware constrained multi-objective Bayesian optimization.
result Efficacy demonstrated on real-world analog circuit design problems.
BOAT optimizes multiple antibody properties efficiently.
problem Balancing multiple drug-like properties in antibody design.
method Bayesian optimization framework coupling surrogate modeling and genetic algorithm.
result Competitive performance with state-of-the-art multi-objective protein optimization methods.
PALS extends PAL for optimizing stochastic simulators efficiently.
problem Optimizing stochastic simulators with high output variance and expensive evaluations.
method Bayesian optimization with probabilistic models, extending PAL for stochastic settings.
result PALS outperforms other methods in optimizing stochastic simulators.
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…
Improved MESMOC+ optimizes constrained multi-objective problems efficiently.
problem Optimizing constrained multi-objective problems with expensive evaluations.
method Minimizes entropy of Pareto frontier to guide search, using linear cost and decoupled evaluation.
result Significantly faster than alternatives, with more accurate entropy estimation.
Proposes a new MOBO framework for better estimation of uncertain parameters in expensive black-box functions.
problem Estimating the unknown utopia in multi-objective optimization problems with expensive black-box functions.
method Nested weighted Tchebycheff Multi-objective Bayesian Optimization (WTB-MOBO) with ensemble model selection.
result Improves MOBO performance by selecting the best model from an ensemble of predictive models.
MOBO-OSD optimizes multi-objective functions using orthogonal search directions.
problem Challenging multi-objective optimization problem.
method Solves multiple constrained optimization problems along orthogonal search directions.
result Consistently outperforms state-of-the-art algorithms.
A new method for incorporating preferences in multi-objective Bayesian optimization.
problem Incorporating preferences in computationally expensive multi-objective optimization problems.
method Building independent surrogate models on each objective function and using Generalised value distribution to approximate the scalarizing function.
result The proposed multi-surrogate approach outperforms the mono-surrogate approach on benchmark and real-world problems.
TAMO optimizes multiple objectives in-context using transformers.
problem Balancing competing objectives in expensive, black-box problems.
method Fully amortized, transformer-based policy for multi-objective optimization.
result Significant speedup in proposal time with improved Pareto quality.
We present PESMO, a Bayesian method for identifying the Pareto set of multi-objective optimization problems, when the functions are expensive to evaluate. The central idea of PESMO is to choose evaluation points so as to maximally reduce the entropy of the posterior distribution over the Pareto set. Critically, the PES…
New BO method optimizes multiple objectives under input noise.
problem Optimizing multiple performance metrics in manufacturing processes subject to random input noise.
method Formalizes optimization of multivariate value-at-risk (MVaR) using random scalarizations.
result Significantly outperforms alternative methods in identifying robust designs.
Novel NAS method balances performance and hardware metrics efficiently.
problem Challenging multi-objective optimization in neural architecture search.
method Parameterizes joint architectural distribution via hypernetwork conditioned on hardware features and preferences.
result Zero-shot transferability to new devices with representative and diverse architectures.
Wind farm layout optimisation tackles space constraints with Bayesian multi-objective approach.
problem Optimizing wind farm layout due to limited space and conflicting objectives.
method Set-based multi-objective Bayesian optimisation using Gaussian process.
result Demonstrates potential of set-based Bayesian multi-objective optimisation for wind farm layout.
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 …
BoTier optimizes experiments by balancing multiple objectives hierarchically.
problem Balancing multiple competing objectives in scientific experiments.
method Composite objective that flexibly represents a hierarchy of preferences over outcomes and parameters.
result Demonstrates robust applicability across various use cases and seamless integration.
Optimal best arm identification for multi-objective bandits with fixed error probability.
problem Identifying the best arm for each of multiple objectives with fixed confidence.
method Surrogate proportions to sample arms at each time step, eliminating max-min optimisation.
result Asymptotically optimal algorithm for multi-objective best arm identification.
Bayesian optimization improves DRL for ESG portfolio management.
problem Optimizing hyperparameters of DRL agents for ESG metrics.
method Bayesian optimization for noisy, expensive-to-evaluate functions.
result Multi-objective optimization yields optimal Pareto set of portfolios.
We present mlrMBO, a flexible and comprehensive R toolbox for model-based optimization (MBO), also known as Bayesian optimization, which addresses the problem of expensive black-box optimization by approximating the given objective function through a surrogate regression model. It is designed for both single- and multi…
Proof shows imitation of expert's reward and solutions in multi-objective optimization.
problem Multi-objective optimization with reward and solution imitation.
method Wasserstein inverse reinforcement learning.
result Wasserstein inverse reinforcement learning enables imitation of expert's reward and solutions in multi-objective optimization.
Proof of convergence for multi-objective optimization using inverse reinforcement learning.
problem Proving convergence in multi-objective optimization problems.
method Wasserstein inverse reinforcement learning with projective subgradient method and gradient descent.
result Convergence of inverse reinforcement learning for multi-objective optimization.
Pessimistic estimator improves multi-objective policy optimization.
problem Optimizing multi-objective policies from existing data.
method Pessimistic estimator based on inverse propensity scores (IPS).
result Pessimistic estimator outperforms naive IPS estimator in theory and experiments.
Enhances portfolio optimization under uncertainty using robust multi-objective methods.
problem Uncertainties in real-world portfolio optimization scenarios.
method Robust multi-objective optimization with benchmark comparisons.
result More reliable and adaptable portfolio strategies for market uncertainties.
OP-GFNs sample candidates in order-preserving proportion to a learned reward function.
problem Sampling diverse candidates with varying rewards in multi-objective optimization.
method Order-Preserving GFlowNets (OP-GFNs) use a learned reward function consistent with a provided order on candidates.
result Training OP-GFNs sparsifies the reward landscape, focusing on higher-ranked candidates.
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.
Study on Pareto optimality in multi-objective bandit problems.
problem Pareto optimality in multi-objective multi-armed bandit problems.
method Formulated adversarial multi-objective multi-armed bandit, defined Pareto regrets, presented algorithms, established upper and lower bounds.
result New algorithms are optimal in adversarial settings and nearly optimal in stochastic settings.
A new framework optimizes manufacturing decisions with less data and time.
problem Optimizing complex systems with multiple conflicting objectives.
method Data-driven Bayesian optimization using sequential learning.
result The proposed algorithm achieves the actual Pareto front with less data.
Study KKT conditions for multi-objective optimization on Hadamard manifolds.
problem Optimizing multi-objective interval-valued functions on Hadamard manifolds.
method Developed KKT conditions for Pareto optimal solutions under different ordering and convexity notions.
result Results are more general than on Euclidean spaces.
This paper introduces multi-objective hyperparameter optimization in machine learning.
problem Optimizing machine learning pipelines for multiple objectives, not just accuracy.
method Survey of optimization strategies and applications in multi-objective hyperparameter optimization.
result The importance and utility of multi-objective hyperparameter optimization in applied machine learning.
We apply numerical methods in combination with finite-difference-time-domain (FDTD) simulations to optimize transmission properties of plasmonic mirror color filters using a multi-objective figure of merit over a five-dimensional parameter space by utilizing novel multi-fidelity Gaussian processes approach. We compare …
This paper surveys gradient-based multi-objective deep learning methods.
problem Balancing multiple conflicting objectives in deep learning models.
method Gradient-based techniques adapted from Multi-Objective Optimization.
result Comprehensive survey of gradient-based multi-objective deep learning algorithms.
New method improves neural architecture search by optimizing for both performance and diversity.
problem Traditional multi-objective NAS fails to address practical constraints and niches.
method Formulated as quality diversity optimization, introduces multifidelity optimizers.
result Quality diversity NAS outperforms multi-objective NAS in quality and efficiency.