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Found 57 papers, 27 papers with code
A Quadrature Approach for General-Purpose Batch Bayesian Optimization via Probabilistic Lifting
Parallelisation in Bayesian optimisation is a common strategy but faces several challenges: the need for flexibility in acquisition functions and kernel choices, flexibility dealing with discrete and continuous variables simultaneously, model misspecification, and lastly fast massive parallelisation.
Beyond Lengthscales: No-regret Bayesian Optimisation With Unknown Hyperparameters Of Any Type
Previously proposed algorithms with the no-regret property were only able to handle the special case of unknown lengthscales, reproducing kernel Hilbert space norm and applied only to the frequentist case.
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI
In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets.
Looping in the Human Collaborative and Explainable Bayesian Optimization
Like many optimizers, Bayesian optimization often falls short of gaining user trust due to opacity.
Adaptive Batch Sizes for Active Learning A Probabilistic Numerics Approach
Active learning parallelization is widely used, but typically relies on fixing the batch size throughout experimentation.
On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations
KL-regularized reinforcement learning from expert demonstrations has proved successful in improving the sample efficiency of deep reinforcement learning algorithms, allowing them to be applied to challenging physical real-world tasks.
Bayesian Model Selection of Lithium-Ion Battery Models via Bayesian Quadrature
We show that popular model selection criteria, such as root-mean-square error and Bayesian information criterion, can fail to select a parsimonious model in the case of a multimodal posterior.
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization
We prove that under suitable reparameterizations, the BO policy that maximizes the probabilistic objective is the same as that which maximizes the AF, and therefore, PR enjoys the same regret bounds as the original BO policy using the underlying AF.
Log-Linear-Time Gaussian Processes Using Binary Tree Kernels
We present a new kernel that allows for Gaussian process regression in $O((n+m)\log(n+m))$ time.
Bézier Gaussian Processes for Tall and Wide Data
We introduce a kernel that allows the number of summarising variables to grow exponentially with the number of input features, but requires only linear cost in both number of observations and input features.
Bayesian Generational Population-Based Training
Leveraging the new highly parallelizable Brax physics engine, we show that these innovations lead to large performance gains, significantly outperforming the tuned baseline while learning entire configurations on the fly.
Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination
Empirically, we find that our approach significantly outperforms the sampling efficiency of both state-of-the-art BQ techniques and Nested Sampling in various real-world datasets, including lithium-ion battery analytics.
Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations
Using this suite of benchmarking tasks, we show that simple modifications to two popular vision-based online reinforcement learning algorithms, DreamerV2 and DrQ-v2, suffice to outperform existing offline RL methods and establish competitive baselines for continuous control in the visual domain.
Robust Multi-Objective Bayesian Optimization Under Input Noise
In many manufacturing processes, the design parameters are subject to random input noise, resulting in a product that is often less performant than expected.
Adversarial Attacks on Graph Classification via Bayesian Optimisation
While the majority of the literature focuses on such vulnerability in node-level classification tasks, little effort has been dedicated to analysing adversarial attacks on graph-level classification, an important problem with numerous real-life applications such as biochemistry and social network analysis.
Gaussian Process Sampling and Optimization with Approximate Upper and Lower Bounds
More specifically, we propose the first use of such bounds to improve Gaussian process (GP) posterior sampling and Bayesian optimization (BO).
Revisiting Design Choices in Offline Model-Based Reinforcement Learning
Significant progress has been made recently in offline model-based reinforcement learning, approaches which leverage a learned dynamics model.
Universal Approximation of Functions on Sets
We provide a theoretical analysis of Deep Sets which shows that this universal approximation property is only guaranteed if the model's latent space is sufficiently high-dimensional.
Marginalising over Stationary Kernels with Bayesian Quadrature
Marginalising over families of Gaussian Process kernels produces flexible model classes with well-calibrated uncertainty estimates.
Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces
High-dimensional black-box optimisation remains an important yet notoriously challenging problem.
Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective
Achieving the full promise of the Thermodynamic Variational Objective (TVO), a recently proposed variational lower bound on the log evidence involving a one-dimensional Riemann integral approximation, requires choosing a "schedule" of sorted discretization points.
Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search
Building upon tree-Wasserstein (TW), which is a negative definite variant of OT, we develop a novel discrepancy for neural architectures, and demonstrate it within a Gaussian process surrogate model for the sequential NAS settings.
Bayesian Optimization for Iterative Learning
We demonstrate the efficiency of our algorithm by tuning hyperparameters for the training of deep reinforcement learning agents and convolutional neural networks.
Adaptive Configuration Oracle for Online Portfolio Selection Methods
Financial markets are complex environments that produce enormous amounts of noisy and non-stationary data.
Bayesian 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.
Knowing The What But Not The Where in Bayesian Optimization
In this paper, we consider a new setting in BO in which the knowledge of the optimum output f* is available.
Automated Model Selection with Bayesian Quadrature
We present a novel technique for tailoring Bayesian quadrature (BQ) to model selection.
AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs
Stochastic differential equations are an important modeling class in many disciplines.
ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems
Parameter inference in ordinary differential equations is an important problem in many applied sciences and in engineering, especially in a data-scarce setting.
Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation
Batch Bayesian optimisation (BO) has been successfully applied to hyperparameter tuning using parallel computing, but it is wasteful of resources: workers that complete jobs ahead of others are left idle.
Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?"
This article is the rejoinder for the paper "Probabilistic Integration: A Role in Statistical Computation?"
Battery health prediction under generalized conditions using a Gaussian process transition model
Accurately predicting the future health of batteries is necessary to ensure reliable operation, minimise maintenance costs, and calculate the value of energy storage investments.
Fingerprint Policy Optimisation for Robust Reinforcement Learning
Policy gradient methods ignore the potential value of adjusting environment variables: unobservable state features that are randomly determined by the environment in a physical setting, but are controllable in a simulator.
Optimization, fast and slow: optimally switching between local and Bayesian optimization
We develop the first Bayesian Optimization algorithm, BLOSSOM, which selects between multiple alternative acquisition functions and traditional local optimization at each step.
Quantum algorithms for training Gaussian Processes
Gaussian processes (GPs) are important models in supervised machine learning.
Bayesian Optimization for Dynamic Problems
We propose practical extensions to Bayesian optimization for solving dynamic problems.
Fast Information-theoretic Bayesian Optimisation
Information-theoretic Bayesian optimisation techniques have demonstrated state-of-the-art performance in tackling important global optimisation problems.
Bayesian Optimization for Probabilistic Programs
We present the first general purpose framework for marginal maximum a posteriori estimation of probabilistic program variables.
Distributionally Ambiguous Optimization Techniques for Batch Bayesian Optimization
Our acquisition function is a lower bound on the well-known Expected Improvement function, which requires evaluation of a Gaussian Expectation over a multivariate piecewise affine function.
A Novel Approach to Forecasting Financial Volatility with Gaussian Process Envelopes
In this paper we use Gaussian Process (GP) regression to propose a novel approach for predicting volatility of financial returns by forecasting the envelopes of the time series.
Distribution of Gaussian Process Arc Lengths
We fill the gap in the existing literature by deriving the moments of the arc length for a stationary GP with multiple output dimensions.
Gaussian process regression for forecasting battery state of health
Accurately predicting the future capacity and remaining useful life of batteries is necessary to ensure reliable system operation and to minimise maintenance costs.
Practical Bayesian Optimization for Variable Cost Objectives
We propose a novel Bayesian Optimization approach for black-box functions with an environmental variable whose value determines the tradeoff between evaluation cost and the fidelity of the evaluations.
Alternating Optimisation and Quadrature for Robust Control
ALOQ is robust to the presence of significant rare events, which may not be observable under random sampling, but play a substantial role in determining the optimal policy.
Probabilistic Integration: A Role in Statistical Computation?
A research frontier has emerged in scientific computation, wherein numerical error is regarded as a source of epistemic uncertainty that can be modelled.
Blitzkriging: Kronecker-structured Stochastic Gaussian Processes
We present Blitzkriging, a new approach to fast inference for Gaussian processes, applicable to regression, optimisation and classification.
A Variational Bayesian State-Space Approach to Online Passive-Aggressive Regression
Online Passive-Aggressive (PA) learning is a class of online margin-based algorithms suitable for a wide range of real-time prediction tasks, including classification and regression.
Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees
There is renewed interest in formulating integration as an inference problem, motivated by obtaining a full distribution over numerical error that can be propagated through subsequent computation.
Probabilistic Numerics and Uncertainty in Computations
We deliver a call to arms for probabilistic numerical methods: algorithms for numerical tasks, including linear algebra, integration, optimization and solving differential equations, that return uncertainties in their calculations.
Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature
We propose a novel sampling framework for inference in probabilistic models: an active learning approach that converges more quickly (in wall-clock time) than Markov chain Monte Carlo (MCMC) benchmarks.
Variational Inference for Gaussian Process Modulated Poisson Processes
We present the first fully variational Bayesian inference scheme for continuous Gaussian-process-modulated Poisson processes.
Raiders of the Lost Architecture: Kernels for Bayesian Optimization in Conditional Parameter Spaces
In practical Bayesian optimization, we must often search over structures with differing numbers of parameters.
Automated Machine Learning on Big Data using Stochastic Algorithm Tuning
However, the state of the art in Bayesian optimisation is incapable of scaling to the large number of evaluations of algorithm performance required to fit realistic models to complex, big data.
Efficient Bayesian Nonparametric Modelling of Structured Point Processes
This paper presents a Bayesian generative model for dependent Cox point processes, alongside an efficient inference scheme which scales as if the point processes were modelled independently.
Active Learning of Linear Embeddings for Gaussian Processes
We propose an active learning method for discovering low-dimensional structure in high-dimensional Gaussian process (GP) tasks.
A Kernel for Hierarchical Parameter Spaces
We define a family of kernels for mixed continuous/discrete hierarchical parameter spaces and show that they are positive definite.
