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08/17/2023
Conditional Sampling of Variational Autoencoders via Iterated Approximate Ancestral Sampling
Conditional sampling of variational autoencoders (VAEs) is needed in var...
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05/12/2023
Designing Optimal Behavioral Experiments Using Machine Learning
Computational models are powerful tools for understanding human cognitio...
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05/01/2023
Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression
Functions of the ratio of the densities p/q are widely used in machine l...
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08/04/2022
Bayesian Optimization with Informative Covariance
Bayesian Optimization is a methodology for global optimization of unknow...
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06/27/2022
Pen and Paper Exercises in Machine Learning
This is a collection of (mostly) pen-and-paper exercises in machine lear...
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04/29/2022
Statistical applications of contrastive learning
The likelihood function plays a crucial role in statistical inference an...
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11/25/2021
Variational Gibbs inference for statistical model estimation from incomplete data
Statistical models are central to machine learning with broad applicabil...
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11/03/2021
Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods
We introduce implicit Deep Adaptive Design (iDAD), a new method for perf...
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10/29/2021
Bayesian Optimal Experimental Design for Simulator Models of Cognition
Bayesian optimal experimental design (BOED) is a methodology to identify...
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05/10/2021
Gradient-based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds
We introduce a framework for Bayesian experimental design (BED) with imp...
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06/22/2020
Telescoping Density-Ratio Estimation
Density-ratio estimation via classification is a cornerstone of unsuperv...
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03/20/2020
Sequential Bayesian Experimental Design for Implicit Models via Mutual Information
Bayesian experimental design (BED) is a framework that uses statistical ...
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02/19/2020
Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation
Implicit stochastic models, where the data-generation distribution is in...
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06/21/2019
Adaptive Approximate Bayesian Computation Tolerance Selection
Approximate Bayesian Computation (ABC) methods are increasingly used for...
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04/04/2019
To Stir or Not to Stir: Online Estimation of Liquid Properties for Pouring Actions
Our brains are able to exploit coarse physical models of fluids to solve...
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04/01/2019
Robust Optimisation Monte Carlo
This paper is on Bayesian inference for parametric statistical models th...
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02/27/2019
Adaptive Gaussian Copula ABC
Approximate Bayesian computation (ABC) is a set of techniques for Bayesi...
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10/23/2018
Dynamic Likelihood-free Inference via Ratio Estimation (DIRE)
Parametric statistical models that are implicitly defined in terms of a ...
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06/10/2018
Conditional Noise-Contrastive Estimation of Unnormalised Models
Many parametric statistical models are not properly normalised and only ...
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05/31/2018
Ratio Matching MMD Nets: Low dimensional projections for effective deep generative models
Deep generative models can learn to generate realistic-looking images on...
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05/22/2017
VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning
Deep generative models provide powerful tools for distributions over com...
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04/03/2017
Efficient acquisition rules for model-based approximate Bayesian computation
Approximate Bayesian computation (ABC) is a method for Bayesian inferenc...
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06/18/2015
Simultaneous Estimation of Non-Gaussian Components and their Correlation Structure
The statistical dependencies which independent component analysis (ICA) ...
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02/19/2015
Classification and Bayesian Optimization for Likelihood-Free Inference
Some statistical models are specified via a data generating process for ...
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01/14/2015
Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models
Our paper deals with inferring simulator-based statistical models given ...
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07/18/2014
Likelihood-free inference via classification
Increasingly complex generative models are being used across disciplines...
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04/25/2013