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Found 26 papers, 11 papers with code
Bayesian Optimization with Informative Covariance
Bayesian Optimization is a methodology for global optimization of unknown and expensive objectives.
Pen and Paper Exercises in Machine Learning
This is a collection of (mostly) pen-and-paper exercises in machine learning.
Statistical applications of contrastive learning
The likelihood function plays a crucial role in statistical inference and experimental design.
Variational Gibbs inference for statistical model estimation from incomplete data
We address this gap by introducing variational Gibbs inference (VGI), a new general-purpose method to estimate the parameters of statistical models from incomplete data.
Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods
We introduce implicit Deep Adaptive Design (iDAD), a new method for performing adaptive experiments in real-time with implicit models.
Bayesian Optimal Experimental Design for Simulator Models of Cognition
Bayesian optimal experimental design (BOED) is a methodology to identify experiments that are expected to yield informative data.
Scaling Densities For Improved Density Ratio Estimation
As such, estimating density ratios accurately using only samples from $p$ and $q$ is of high significance and has led to a flurry of recent work in this direction.
Gradient-based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds
We introduce a framework for Bayesian experimental design (BED) with implicit models, where the data-generating distribution is intractable but sampling from it is still possible.
Neural Approximate Sufficient Statistics for Likelihood-free Inference
We consider the fundamental problem of how to automatically construct summary statistics for likelihood-free inference where the evaluation of likelihood function is intractable but sampling / simulating data from the model is possible.
Telescoping Density-Ratio Estimation
Density-ratio estimation via classification is a cornerstone of unsupervised learning.
Sequential Bayesian Experimental Design for Implicit Models via Mutual Information
We address this gap in the literature by devising a novel sequential design framework for parameter estimation that uses the Mutual Information (MI) between model parameters and simulated data as a utility function to find optimal experimental designs, which has not been done before for implicit models.
Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation
A fundamental question is how to design the experiments so that the collected data are most useful.
Robust Optimisation Monte Carlo
We demonstrate the effectiveness of the proposed Robust OMC on toy examples and tasks in inverse-graphics where we perform Bayesian inference with a complex image renderer.
Adaptive Gaussian Copula ABC
Approximate Bayesian computation (ABC) is a set of techniques for Bayesian inference when the likelihood is intractable but sampling from the model is possible.
Dynamic Likelihood-free Inference via Ratio Estimation (DIRE)
While several methods for choosing summary statistics have been proposed for ABC, the literature for synthetic likelihood and LFIRE is very thin to date.
Conditional Noise-Contrastive Estimation of Unnormalised Models
Examples of unnormalised models are Gibbs distributions, Markov random fields, and neural network models in unsupervised deep learning.
Generative Ratio Matching Networks
In this work, we take their insight of using kernels as fixed adversaries further and present a novel method for training deep generative models that does not involve saddlepoint optimization.
ELFI: Engine for Likelihood-Free Inference
The stand-alone ELFI graph can be used with any of the available inference methods without modifications.
VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning
Deep generative models provide powerful tools for distributions over complicated manifolds, such as those of natural images.
Efficient acquisition rules for model-based approximate Bayesian computation
We propose to compute the uncertainty in the ABC posterior density, which is due to a lack of simulations to estimate this quantity accurately, and define a loss function that measures this uncertainty.
Likelihood-free inference by ratio estimation
The popular synthetic likelihood approach infers the parameters by modelling summary statistics of the data by a Gaussian probability distribution.
Simultaneous Estimation of Non-Gaussian Components and their Correlation Structure
The precision matrix of the linear components is assumed to be randomly generated by a higher-order process and explicitly parametrized by a parameter matrix.
Classification and Bayesian Optimization for Likelihood-Free Inference
This approach faces at least two major difficulties: The first difficulty is the choice of the discrepancy measure which is used to judge whether the simulated data resemble the observed data.
Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models
The strategy is implemented using Bayesian optimization and is shown to accelerate the inference through a reduction in the number of required simulations by several orders of magnitude.
Likelihood-free inference via classification
Increasingly complex generative models are being used across disciplines as they allow for realistic characterization of data, but a common difficulty with them is the prohibitively large computational cost to evaluate the likelihood function and thus to perform likelihood-based statistical inference.
Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation
We propose a new method for detecting changes in Markov network structure between two sets of samples.
