
Pruning untrained neural networks: Principles and Analysis
Overparameterized neural networks display stateofthe art performance. ...
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Augmented Neural ODEs
We show that Neural Ordinary Differential Equations (ODEs) learn represe...
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Schrödinger Bridge Samplers
Consider a reference Markov process with initial distribution π_0 and tr...
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Localised Generative Flows
We argue that flowbased density models based on continuous bijections a...
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Modular MetaLearning with Shrinkage
Most gradientbased approaches to metalearning do not explicitly accoun...
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Asymptotic Bias of Stochastic Gradient Search
The asymptotic behavior of the stochastic gradient algorithm with a bias...
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Filtering Variational Objectives
When used as a surrogate objective for maximum likelihood estimation in ...
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PseudoMarginal Hamiltonian Monte Carlo
Bayesian inference in the presence of an intractable likelihood function...
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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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Bayesian nonparametric image segmentation using a generalized SwendsenWang algorithm
Unsupervised image segmentation aims at clustering the set of pixels of ...
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Expectation Particle Belief Propagation
We propose an original particlebased implementation of the Loopy Belief...
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On Markov chain Monte Carlo methods for tall data
Markov chain Monte Carlo methods are often deemed too computationally in...
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RaoBlackwellised Particle Filtering for Dynamic Bayesian Networks
Particle filters (PFs) are powerful samplingbased inference/learning al...
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Asynchronous Anytime Sequential Monte Carlo
We introduce a new sequential Monte Carlo algorithm we call the particle...
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Reversible Jump MCMC Simulated Annealing for Neural Networks
We propose a novel reversible jump Markov chain Monte Carlo (MCMC) simul...
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Toward Practical N2 Monte Carlo: the Marginal Particle Filter
Sequential Monte Carlo techniques are useful for state estimation in non...
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New inference strategies for solving Markov Decision Processes using reversible jump MCMC
In this paper we build on previous work which uses inferences techniques...
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SparsityPromoting Bayesian Dynamic Linear Models
Sparsitypromoting priors have become increasingly popular over recent y...
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Autoregressive Kernels For Time Series
We propose in this work a new family of kernels for variablelength time...
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Efficient Bayesian Inference for Generalized BradleyTerry Models
The BradleyTerry model is a popular approach to describe probabilities ...
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Controlled Sequential Monte Carlo
Sequential Monte Carlo (SMC) methods are a set of simulationbased techn...
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On the Selection of Initialization and Activation Function for Deep Neural Networks
The weight initialization and the activation function of deep neural net...
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Hamiltonian Variational AutoEncoder
Variational AutoEncoders (VAEs) have become very popular techniques to ...
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Limit theorems for sequential MCMC methods
Sequential Monte Carlo (SMC) methods, also known as particle filters, co...
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Stability of Optimal Filter HigherOrder Derivatives
In many scenarios, a statespace model depends on a parameter which need...
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Bias of Particle Approximations to Optimal Filter Derivative
In many applications, a statespace model depends on a parameter which n...
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Analyticity of Entropy Rates of ContinuousState Hidden Markov Models
The analyticity of the entropy and relative entropy rates of continuous...
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Asymptotic Properties of Recursive Maximum Likelihood Estimation in NonLinear StateSpace Models
Using stochastic gradient search and the optimal filter derivative, it i...
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Hamiltonian Descent Methods
We propose a family of optimization methods that achieve linear converge...
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On the utility of MetropolisHastings with asymmetric acceptance ratio
The MetropolisHastings algorithm allows one to sample asymptotically fr...
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Randomized Hamiltonian Monte Carlo as Scaling Limit of the Bouncy Particle Sampler and DimensionFree Convergence Rates
The Bouncy Particle Sampler is a Markov chain Monte Carlo method based o...
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Scalable Monte Carlo inference for statespace models
We present an original simulationbased method to estimate likelihood ra...
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Large Sample Asymptotics of the PseudoMarginal Method
The pseudomarginal algorithm is a variant of the MetropolisHastings al...
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Unbiased Markov chain Monte Carlo for intractable target distributions
Performing numerical integration when the integrand itself cannot be eva...
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Scalable MetropolisHastings for Exact Bayesian Inference with Large Datasets
Bayesian inference via standard Markov Chain Monte Carlo (MCMC) methods ...
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On the Impact of the Activation Function on Deep Neural Networks Training
The weight initialization and the activation function of deep neural net...
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Training Dynamics of Deep Networks using Stochastic Gradient Descent via Neural Tangent Kernel
Stochastic Gradient Descent (SGD) is widely used to train deep neural ne...
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Efficient MCMC Sampling with DimensionFree Convergence Rate using ADMMtype Splitting
Performing exact Bayesian inference for complex models is intractable. M...
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Unbiased Smoothing using Particle Independent MetropolisHastings
We consider the approximation of expectations with respect to the distri...
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Bernoulli Race Particle Filters
When the weights in a particle filter are not available analytically, st...
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NonReversible Parallel Tempering: an Embarassingly Parallel MCMC Scheme
Parallel tempering (PT) methods are a popular class of Markov chain Mont...
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Replica Conditional Sequential Monte Carlo
We propose a Markov chain Monte Carlo (MCMC) scheme to perform state inf...
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Nonreversible jump algorithms for Bayesian nested model selection
Nonreversible Markov chain Monte Carlo methods often outperform their r...
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Ensemble Rejection Sampling
We introduce Ensemble Rejection Sampling, a scheme for exact simulation ...
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Arnaud Doucet
verfied profile
Professor of Statistics at Oxford University & Research Scientist at Google DeepMind