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Found 55 papers, 25 papers with code
Transfer Learning Bayesian Optimization to Design Competitor DNA Molecules for Use in Diagnostic Assays
With the rise in engineered biomolecular devices, there is an increased need for tailor-made biological sequences.
Recommendations for Baselines and Benchmarking Approximate Gaussian Processes
To address this, we make recommendations for comparing GP approximations based on a specification of what a user should expect from a method.
Transition Constrained Bayesian Optimization via Markov Decision Processes
Bayesian optimization is a methodology to optimize black-box functions.
Turbulence: Systematically and Automatically Testing Instruction-Tuned Large Language Models for Code
Thus, from a single question template, it is possible to ask an LLM a $\textit{neighbourhood}$ of very similar programming questions, and assess the correctness of the result returned for each question.
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Practical Path-based Bayesian Optimization
There has been a surge in interest in data-driven experimental design with applications to chemical engineering and drug manufacturing.
Learning in Deep Factor Graphs with Gaussian Belief Propagation
We propose an approach to do learning in Gaussian factor graphs.
Current Methods for Drug Property Prediction in the Real World
However, it remains unclear for practitioners which method or approach is most suitable, as different papers benchmark on different datasets and methods, leading to varying conclusions that are not easily compared.
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels
Recent works show that Bayesian model selection with Laplace approximations can allow to optimize such hyperparameters just like standard neural network parameters using gradients and on the training data.
Causal Discovery using Bayesian Model Selection
With only observational data on two variables, and without other assumptions, it is not possible to infer which one causes the other.
Actually Sparse Variational Gaussian Processes
For large datasets, sparse GPs reduce these demands by conditioning on a small set of inducing variables designed to summarise the data.
Combining Multi-Fidelity Modelling and Asynchronous Batch Bayesian Optimization
Bayesian Optimization is a useful tool for experiment design.
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees
For low-dimensional tasks such as geospatial modeling, we propose an automated method for computing inducing points satisfying these conditions.
Memory Safe Computations with XLA Compiler
We believe that further focus on removing memory constraints at a compiler level will widen the range of machine learning methods that can be developed in the future.
Relaxing Equivariance Constraints with Non-stationary Continuous Filters
Equivariances provide useful inductive biases in neural network modeling, with the translation equivariance of convolutional neural networks being a canonical example.
Learning Invariant Weights in Neural Networks
Assumptions about invariances or symmetries in data can significantly increase the predictive power of statistical models.
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations
We develop a convenient gradient-based method for selecting the data augmentation without validation data during training of a deep neural network.
SnAKe: Bayesian Optimization with Pathwise Exploration
Bayesian Optimization is a very effective tool for optimizing expensive black-box functions.
Improved Inverse-Free Variational Bounds for Sparse Gaussian Processes
The need for matrix decompositions (inverses) is often named as a major impediment to scaling Gaussian process (GP) models, even in efficient approximations.
Matrix Inversion free variational inference in Conditional Student's T Processes
We propose a new variational lower bound for performing inference in sparse Student's T Processes that does not require computationally intensive operations such as matrix inversions or log determinants of matrices.
Barely Biased Learning for Gaussian Process Regression
We suggest a method that adaptively selects the amount of computation to use when estimating the log marginal likelihood so that the bias of the objective function is guaranteed to be small.
A Bayesian Approach to Invariant Deep Neural Networks
We propose a novel Bayesian neural network architecture that can learn invariances from data alone by inferring a posterior distribution over different weight-sharing schemes.
Last Layer Marginal Likelihood for Invariance Learning
Computing the marginal likelihood is hard for neural networks, but success with tractable approaches that compute the marginal likelihood for the last layer only raises the question of whether this convenient approach might be employed for learning invariances.
Data augmentation in Bayesian neural networks and the cold posterior effect
Here, we provide several approaches to developing principled Bayesian neural networks incorporating data augmentation.
BNNpriors: A library for Bayesian neural network inference with different prior distributions
Bayesian neural networks have shown great promise in many applications where calibrated uncertainty estimates are crucial and can often also lead to a higher predictive performance.
Deep Neural Networks as Point Estimates for Deep Gaussian Processes
This results in models that can either be seen as neural networks with improved uncertainty prediction or deep Gaussian processes with increased prediction accuracy.
GPflux: A Library for Deep Gaussian Processes
GPflux is compatible with and built on top of the Keras deep learning eco-system.
The Promises and Pitfalls of Deep Kernel Learning
Through careful experimentation on the UCI, CIFAR-10, and the UTKFace datasets, we find that the overfitting from overparameterized maximum marginal likelihood, in which the model is "somewhat Bayesian", can in certain scenarios be worse than that from not being Bayesian at all.
Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients
We propose a lower bound on the log marginal likelihood of Gaussian process regression models that can be computed without matrix factorisation of the full kernel matrix.
Bayesian Neural Network Priors Revisited
Isotropic Gaussian priors are the de facto standard for modern Bayesian neural network inference.
Correlated Weights in Infinite Limits of Deep Convolutional Neural Networks
Infinite width limits of deep neural networks often have tractable forms.
Design of Experiments for Verifying Biomolecular Networks
There is a growing trend in molecular and synthetic biology of using mechanistic (non machine learning) models to design biomolecular networks.
Understanding Variational Inference in Function-Space
Then, we propose (featurized) Bayesian linear regression as a benchmark for `function-space' inference methods that directly measures approximation quality.
A Bayesian Perspective on Training Speed and Model Selection
This provides two major insights: first, that a measure of a model's training speed can be used to estimate its marginal likelihood.
Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search
Reliable yet efficient evaluation of generalisation performance of a proposed architecture is crucial to the success of neural architecture search (NAS).
Convergence of Sparse Variational Inference in Gaussian Processes Regression
Gaussian processes are distributions over functions that are versatile and mathematically convenient priors in Bayesian modelling.
Variational Orthogonal Features
We present a construction of features for any stationary prior kernel that allow for computation of an unbiased estimator to the ELBO using $T$ Monte Carlo samples in $\mathcal{O}(\tilde{N}T+M^2T)$ and in $\mathcal{O}(\tilde{N}T+MT)$ with an additional approximation.
Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty
In image segmentation, there is often more than one plausible solution for a given input.
Speedy Performance Estimation for Neural Architecture Search
Reliable yet efficient evaluation of generalisation performance of a proposed architecture is crucial to the success of neural architecture search (NAS).
On the Benefits of Invariance in Neural Networks
Many real world data analysis problems exhibit invariant structure, and models that take advantage of this structure have shown impressive empirical performance, particularly in deep learning.
Capsule Networks -- A Probabilistic Perspective
'Capsule' models try to explicitly represent the poses of objects, enforcing a linear relationship between an object's pose and that of its constituent parts.
A Framework for Interdomain and Multioutput Gaussian Processes
One obstacle to the use of Gaussian processes (GPs) in large-scale problems, and as a component in deep learning system, is the need for bespoke derivations and implementations for small variations in the model or inference.
Variational Gaussian Process Models without Matrix Inverses
We present a variational approximation for a wide range of GP models that does not require a matrix inverse to be performed at each optimisation step.
Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes
We implement gradient-based variational inference routines for Wishart and inverse Wishart processes, which we apply as Bayesian models for the dynamic, heteroskedastic covariance matrix of a multivariate time series.
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models
As we demonstrate in our experiments, the factorisation between latent system states and transition function can lead to a miscalibrated posterior and to learning unnecessarily large noise terms.
Rates of Convergence for Sparse Variational Gaussian Process Regression
Excellent variational approximations to Gaussian process posteriors have been developed which avoid the $\mathcal{O}\left(N^3\right)$ scaling with dataset size $N$.
Bayesian Image Classification with Deep Convolutional Gaussian Processes
We also demonstrate that our fully Bayesian approach improves on dropout-based Bayesian deep learning methods in terms of uncertainty and marginal likelihood estimates.
Non-Factorised Variational Inference in Dynamical Systems
We focus on variational inference in dynamical systems where the discrete time transition function (or evolution rule) is modelled by a Gaussian process.
Bayesian Layers: A Module for Neural Network Uncertainty
We describe Bayesian Layers, a module designed for fast experimentation with neural network uncertainty.
Closed-form Inference and Prediction in Gaussian Process State-Space Models
We examine an analytic variational inference scheme for the Gaussian Process State Space Model (GPSSM) - a probabilistic model for system identification and time-series modelling.
Learning Invariances using the Marginal Likelihood
Generalising well in supervised learning tasks relies on correctly extrapolating the training data to a large region of the input space.
Convolutional Gaussian Processes
We present a practical way of introducing convolutional structure into Gaussian processes, making them more suited to high-dimensional inputs like images.
GPflow: A Gaussian process library using TensorFlow
GPflow is a Gaussian process library that uses TensorFlow for its core computations and Python for its front end.
Understanding Probabilistic Sparse Gaussian Process Approximations
Good sparse approximations are essential for practical inference in Gaussian Processes as the computational cost of exact methods is prohibitive for large datasets.
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models
We show that GP performance improves with increasing amounts of data in regression (on flight data with 2 million records) and latent variable modelling (on MNIST).
Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models - a Gentle Tutorial
In this tutorial we explain the inference procedures developed for the sparse Gaussian process (GP) regression and Gaussian process latent variable model (GPLVM).
