Papers Bayesian Optimisation
“Bayesian Optimisation” 태그가 달린 논문 221편 · 필터 해제
Interactive Text Ranking with Bayesian Optimisation: A Case Study on Community QA and Summarisation
For many NLP applications, such as question answering and summarisation, the goal is to select the best solution from a large space of candidates to meet a particular user's needs. To address the lack of user-specific tr…
Bayesian OptimisationCommunity Question AnsweringQuestion AnsweringReinforcement Learning+1Parameter Optimization and Learning in a Spiking Neural Network for UAV Obstacle Avoidance targeting Neuromorphic Processors
The Lobula Giant Movement Detector (LGMD) is an identified neuron of the locust that detects looming objects and triggers the insect's escape responses. Understanding the neural principles and network structure that lead…
Bayesian OptimisationAchieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation
Bayesian optimisation is a sample-efficient search methodology that holds great promise for accelerating drug and materials discovery programs. A frequently-overlooked modelling consideration in Bayesian optimisation str…
Bayesian OptimisationDecision MakingDrug DiscoveryDistributional Bayesian optimisation for variational inference on black-box simulators
Inverse problems are ubiquitous in natural sciences and refer to the challenging task of inferring complex and potentially multi-modal posterior distributions over hidden parameters given a set of observations. Typically…
Bayesian OptimisationVariational InferenceBatch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation
The computational efficiency of approximate Bayesian computation (ABC) has been improved by using surrogate models such as Gaussian processes (GP). In one such promising framework the discrepancy between the simulated an…
Bayesian OptimisationComputational EfficiencyExperimental DesignGaussian Processes+1BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
Bayesian optimization provides sample-efficient global optimization for a broad range of applications, including automatic machine learning, engineering, physics, and experimental design. We introduce BoTorch, a modern p…
Bayesian OptimisationBayesian OptimizationExperimental Designglobal-optimizationOptimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks
We study the problem of causal discovery through targeted interventions. Starting from few observational measurements, we follow a Bayesian active learning approach to perform those experiments which, in expectation with…
Active LearningBayesian OptimisationBayesian OptimizationCausal Discovery+1Antifragile and Robust Heteroscedastic Bayesian Optimisation
Bayesian Optimisation is an important decision-making tool for high-stakes applications in drug discovery and materials design. An oft-overlooked modelling consideration however is the representation of input-depende…
Bayesian OptimisationDecision MakingDrug DiscoveryBayesian Optimisation with Gaussian Processes for Premise Selection
Heuristics in theorem provers are often parameterised. Modern theorem provers such as Vampire utilise a wide array of heuristics to control the search space explosion, thereby requiring optimisation of a large set of par…
Bayesian OptimisationGaussian ProcessesCost-aware Multi-objective Bayesian optimisation
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 object…
Bayesian OptimisationOn the Expressiveness of Approximate Inference in Bayesian Neural Networks
While Bayesian neural networks (BNNs) hold the promise of being flexible, well-calibrated statistical models, inference often requires approximations whose consequences are poorly understood. We study the quality of comm…
Active LearningBayesian InferenceBayesian OptimisationDecision Making+1'In-Between' Uncertainty in Bayesian Neural Networks
We describe a limitation in the expressiveness of the predictive uncertainty estimate given by mean-field variational inference (MFVI), a popular approximate inference method for Bayesian neural networks. In particular, …
Active LearningBayesian OptimisationVariational InferenceSparse Spectrum Gaussian Process for Bayesian Optimization
We propose a novel sparse spectrum approximation of Gaussian process (GP) tailored for Bayesian optimization. Whilst the current sparse spectrum methods provide desired approximations for regression problems, it is obser…
Bayesian OptimisationBayesian OptimizationThompson SamplingBayesian Optimisation over Multiple Continuous and Categorical Inputs
Efficient optimisation of black-box problems that comprise both continuous and categorical inputs is important, yet poses significant challenges. We propose a new approach, Continuous and Categorical Bayesian Optimisatio…
Bayesian OptimisationDiversityMulti-Armed BanditsMEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning
Efficient approximation lies at the heart of large-scale machine learning problems. In this paper, we propose a novel, robust maximum entropy algorithm, which is capable of dealing with hundreds of moments and allows for…
Bayesian OptimisationBIG-bench Machine LearningVariational InferenceFast and Reliable Architecture Selection for Convolutional Neural Networks
The performance of a Convolutional Neural Network (CNN) depends on its hyperparameters, like the number of layers, kernel sizes, or the learning rate for example. Especially in smaller networks and applications with limi…
Bayesian OptimisationBIG-bench Machine LearningNeural Architecture SearchParallel Gaussian process surrogate Bayesian inference with noisy likelihood evaluations
We consider Bayesian inference when only a limited number of noisy log-likelihood evaluations can be obtained. This occurs for example when complex simulator-based statistical models are fitted to data, and synthetic lik…
Bayesian InferenceBayesian OptimisationExperimental DesignBayesian Search for Robust Optima
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 f…
Bayesian OptimisationEffective Estimation of Deep Generative Language Models
Advances in variational inference enable parameterisation of probabilistic models by deep neural networks. This combines the statistical transparency of the probabilistic modelling framework with the representational pow…
Bayesian OptimisationLanguage ModellingVariational InferenceMeta-Learning surrogate models for sequential decision making
We introduce a unified probabilistic framework for solving sequential decision making problems ranging from Bayesian optimisation to contextual bandits and reinforcement learning. This is accomplished by a probabilistic …
Bayesian OptimisationDecision MakingMeta-LearningMulti-Armed Bandits+4