Papers Bayesian Optimisation
“Bayesian Optimisation” 태그가 달린 논문 221편 · 필터 해제
Interpretable Neural Architecture Search via Bayesian Optimisation with Weisfeiler-Lehman Kernels
Current neural architecture search (NAS) strategies focus only on finding a single, good, architecture. They offer little insight into why a specific network is performing well, or how we should modify the architecture i…
Bayesian OptimisationNeural Architecture SearchRandomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation
In order to improve the performance of Bayesian optimisation, we develop a modified Gaussian process upper confidence bound (GP-UCB) acquisition function. This is done by sampling the exploration-exploitation trade-off p…
Bayesian OptimisationBayesian Optimisation vs. Input Uncertainty Reduction
Simulators often require calibration inputs estimated from real world data and the quality of the estimate can significantly affect simulation output. Particularly when performing simulation optimisation to find an optim…
Bayesian OptimisationBOP-Elites, a Bayesian Optimisation algorithm for Quality-Diversity search
Quality Diversity (QD) algorithms such as MAP-Elites are a class of optimisation techniques that attempt to find a set of high-performing points from an objective function while enforcing behavioural diversity of the poi…
Bayesian OptimisationDiversityGaussian ProcessesBayesOpt Adversarial Attack
Black-box adversarial attacks require a large number of attempts before finding successful adversarial examples that are visually indistinguishable from the original input. Current approaches relying on substitute model …
Adversarial AttackBayesian OptimisationDimensionality ReductionModel SelectionWhat do you Mean? The Role of the Mean Function in Bayesian Optimisation
Bayesian optimisation is a popular approach for optimising expensive black-box functions. The next location to be evaluated is selected via maximising an acquisition function that balances exploitation and exploration. G…
Bayesian OptimisationGaussian ProcessesBayesian optimisation of large-scale photonic reservoir computers
Introduction. Reservoir computing is a growing paradigm for simplified training of recurrent neural networks, with a high potential for hardware implementations. Numerous experiments in optics and electronics yield compa…
Bayesian OptimisationEfficient ExplorationNeural Architecture Generator Optimization
Neural Architecture Search (NAS) was first proposed to achieve state-of-the-art performance through the discovery of new architecture patterns, without human intervention. An over-reliance on expert knowledge in the sear…
Bayesian OptimisationNeural Architecture SearchvalidIncorporating Expert Prior in Bayesian Optimisation via Space Warping
Bayesian optimisation is a well-known sample-efficient method for the optimisation of expensive black-box functions. However when dealing with big search spaces the algorithm goes through several low function value regio…
Bayesian OptimisationSequential Bayesian Experimental Design for Implicit Models via Mutual Information
Bayesian experimental design (BED) is a framework that uses statistical models and decision making under uncertainty to optimise the cost and performance of a scientific experiment. Sequential BED, as opposed to static B…
Bayesian OptimisationDecision MakingDecision Making Under UncertaintyExperimental Design+1Misspecification-robust likelihood-free inference in high dimensions
Likelihood-free inference for simulator-based statistical models has developed rapidly from its infancy to a useful tool for practitioners. However, models with more than a handful of parameters still generally remain a …
Bayesian OptimisationEfficient ExplorationVocal Bursts Intensity PredictionRegret Bounds for Noise-Free Kernel-Based Bandits
Kernel-based bandit is an extensively studied black-box optimization problem, in which the objective function is assumed to live in a known reproducing kernel Hilbert space. While nearly optimal regret bounds (up to loga…
Bayesian Optimisation$ε$-shotgun: $ε$-greedy Batch Bayesian Optimisation
Bayesian optimisation is a popular, surrogate model-based approach for optimising expensive black-box functions. Given a surrogate model, the next location to expensively evaluate is chosen via maximisation of a cheap-to…
Bayesian OptimisationBlack-Box Saliency Map Generation Using Bayesian Optimisation
Saliency maps are often used in computer vision to provide intuitive interpretations of what input regions a model has used to produce a specific prediction. A number of approaches to saliency map generation are availabl…
Bayesian OptimisationBayesian Quantile and Expectile Optimisation
Bayesian optimisation (BO) is widely used to optimise stochastic black box functions. While most BO approaches focus on optimising conditional expectations, many applications require risk-averse strategies and alternativ…
Bayesian OptimisationGaussian ProcessesThompson SamplingHidden Markov Model: Tutorial
This is a tutorial paper for Hidden Markov Model (HMM). First, we briefly review the background on Expectation Maximization (EM), Lagrange multiplier, factor graph, the sum-product algorithm, the max-product algorithm, a…
Action RecognitionBayesian InferenceBayesian Optimisationmodel+5Ordinal Bayesian Optimisation
Bayesian optimisation is a powerful tool to solve expensive black-box problems, but fails when the stationary assumption made on the objective function is strongly violated, which is the case in particular for ill-condit…
Bayesian OptimisationThompson SamplingMax-value Entropy Search for Multi-Objective Bayesian Optimization
We consider the problem of multi-objective (MO) blackbox optimization using expensive function evaluations, where the goal is to approximate the true Pareto-set of solutions by minimizing the number of function evaluatio…
Bayesian OptimisationBayesian OptimizationMultiobjective OptimizationGreed is Good: Exploration and Exploitation Trade-offs in Bayesian Optimisation
The performance of acquisition functions for Bayesian optimisation to locate the global optimum of continuous functions is investigated in terms of the Pareto front between exploration and exploitation. We show that Expe…
Active LearningBayesian OptimisationTrading Convergence Rate with Computational Budget in High Dimensional Bayesian Optimization
Scaling Bayesian optimisation (BO) to high-dimensional search spaces is a active and open research problems particularly when no assumptions are made on function structure. The main reason is that at each iteration, BO r…
Bayesian OptimisationBayesian OptimizationVocal Bursts Intensity Prediction