
On SignaltoNoise Ratio Issues in Variational Inference for Deep Gaussian Processes
We show that the gradient estimates used in training Deep Gaussian Proce...
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Improving Transformation Invariance in Contrastive Representation Learning
We propose methods to strengthen the invariance properties of representa...
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Towards a Theoretical Understanding of the Robustness of Variational Autoencoders
We make inroads into understanding the robustness of Variational Autoenc...
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Rethinking SemiSupervised Learning in VAEs
We present an alternative approach to semisupervision in variational au...
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A note on blind contact tracing at scale with applications to the COVID19 pandemic
The current COVID19 pandemic highlights the utility of contact tracing,...
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Statistically Robust Neural Network Classification
Recently there has been much interest in quantifying the robustness of n...
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A Unified Stochastic Gradient Approach to Designing BayesianOptimal Experiments
We introduce a fully stochastic gradient based approach to Bayesian opti...
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Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support
Universal probabilistic programming systems (PPSs) provide a powerful an...
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Amortized Rejection Sampling in Universal Probabilistic Programming
Existing approaches to amortized inference in probabilistic programs wit...
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Amortized Monte Carlo Integration
Current approaches to amortizing Bayesian inference focus solely on appr...
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On the Fairness of Disentangled Representations
Recently there has been a significant interest in learning disentangled ...
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Hijacking Malaria Simulators with Probabilistic Programming
Epidemiology simulations have become a fundamental tool in the fight aga...
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Variational Estimators for Bayesian Optimal Experimental Design
Bayesian optimal experimental design (BOED) is a principled framework fo...
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Variational Bayesian Optimal Experimental Design
Bayesian optimal experimental design (BOED) is a principled framework fo...
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LFPPL: A LowLevel First Order Probabilistic Programming Language for NonDifferentiable Models
We develop a new Lowlevel, Firstorder Probabilistic Programming Langua...
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Disentangling Disentanglement
We develop a generalised notion of disentanglement in Variational AutoE...
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Statistical Verification of Neural Networks
We present a new approach to neural network verification based on estima...
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On Exploration, Exploitation and Learning in Adaptive Importance Sampling
We study adaptive importance sampling (AIS) as an online learning proble...
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Inference Trees: Adaptive Inference with Exploration
We introduce inference trees (ITs), a new class of inference methods tha...
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Nesting Probabilistic Programs
We formalize the notion of nesting probabilistic programming queries and...
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Tighter Variational Bounds are Not Necessarily Better
We provide theoretical and empirical evidence that using tighter evidenc...
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On the Opportunities and Pitfalls of Nesting Monte Carlo Estimators
We present a formalization of nested Monte Carlo (NMC) estimation, where...
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Bayesian Optimization for Probabilistic Programs
We present the first general purpose framework for marginal maximum a po...
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AutoEncoding Sequential Monte Carlo
We introduce AESMC: a method for using deep neural networks for simultan...
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Probabilistic structure discovery in time series data
Existing methods for structure discovery in time series data construct i...
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Interacting Particle Markov Chain Monte Carlo
We introduce interacting particle Markov chain Monte Carlo (iPMCMC), a P...
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Tom Rainforth
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