Search Results for author:
Found 44 papers, 17 papers with code
On the connection between Noise-Contrastive Estimation and Contrastive Divergence
Noise-contrastive estimation (NCE) is a popular method for estimating unnormalised probabilistic models, such as energy-based models, which are effective for modelling complex data distributions.
Discriminator Guidance for Autoregressive Diffusion Models
The use of a discriminator to guide a diffusion process has previously been used for continuous diffusion models, and in this work we derive ways of using a discriminator together with a pretrained generative model in the discrete case.
Graph-based Neural Weather Prediction for Limited Area Modeling
The rise of accurate machine learning methods for weather forecasting is creating radical new possibilities for modeling the atmosphere.
Temporal Graph Neural Networks for Irregular Data
Our TGNN4I model is designed to handle both irregular time steps and partial observations of the graph.
DINO as a von Mises-Fisher mixture model
With this interpretation, DINO assumes equal precision for all components when the prototypes are also $L^2$-normalized.
Ranked #24 on
Self-Supervised Image Classification
on ImageNet
Self-Supervised Image Classification
Self-Supervised Learning
+1
Calibration tests beyond classification
In the machine learning literature, different measures and statistical tests have been proposed and studied for evaluating the calibration of classification models.
A Variational Perspective on Generative Flow Networks
Generative flow networks (GFNs) are a class of models for sequential sampling of composite objects, which approximate a target distribution that is defined in terms of an energy function or a reward.
Marginalized particle Gibbs for multiple state-space models coupled through shared parameters
We provide insights on when each sampler should be used and show that they can be combined to form an efficient PG sampler for a model with strong dependencies between states and parameters.
Scalable Deep Gaussian Markov Random Fields for General Graphs
We propose a flexible GMRF model for general graphs built on the multi-layer structure of Deep GMRFs, originally proposed for lattice graphs only.
Active Learning with Weak Supervision for Gaussian Processes
Annotating data for supervised learning can be costly.
Robustness and Reliability When Training With Noisy Labels
We find that strictly proper and robust loss functions both offer asymptotic robustness in accuracy, but neither guarantee that the final model is calibrated.
Markovian Score Climbing: Variational Inference with KL(p||q)
Modern variational inference (VI) uses stochastic gradients to avoid intractable expectations, enabling large-scale probabilistic inference in complex models.
A general framework for ensemble distribution distillation
Ensembles of neural networks have been shown to give better performance than single networks, both in terms of predictions and uncertainty estimation.
Deep Gaussian Markov Random Fields
Gaussian Markov random fields (GMRFs) are probabilistic graphical models widely used in spatial statistics and related fields to model dependencies over spatial structures.
Parameter elimination in particle Gibbs sampling
Bayesian inference in state-space models is challenging due to high-dimensional state trajectories.
Calibration tests in multi-class classification: A unifying framework
In safety-critical applications a probabilistic model is usually required to be calibrated, i. e., to capture the uncertainty of its predictions accurately.
Particle filter with rejection control and unbiased estimator of the marginal likelihood
While the variance reducing properties of rejection control are known, there has not been (to the best of our knowledge) any work on unbiased estimation of the marginal likelihood (also known as the model evidence or the normalizing constant) in this type of particle filter.
Matrix Multilayer Perceptron
Models that output a vector of responses given some inputs, in the form of a conditional mean vector, are at the core of machine learning.
Elements of Sequential Monte Carlo
A core problem in statistics and probabilistic machine learning is to compute probability distributions and expectations.
Evaluating model calibration in classification
Probabilistic classifiers output a probability distribution on target classes rather than just a class prediction.
Constructing the Matrix Multilayer Perceptron and its Application to the VAE
However, in certain scenarios we are interested in learning structured parameters (predictions) in the form of symmetric positive definite matrices.
Graphical model inference: Sequential Monte Carlo meets deterministic approximations
Approximate inference in probabilistic graphical models (PGMs) can be grouped into deterministic methods and Monte-Carlo-based methods.
Learning dynamical systems with particle stochastic approximation EM
By combining stochastic approximation EM and particle Gibbs with ancestor sampling (PGAS), PSAEM obtains superior computational performance and convergence properties compared to plain particle-smoothing-based approximations of the EM algorithm.
Pseudo-extended Markov chain Monte Carlo
In this paper, we introduce the pseudo-extended MCMC method as a simple approach for improving the mixing of the MCMC sampler for multi-modal posterior distributions.
Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo
We are concerned with the problem of learning probabilistic models of dynamical systems from measured data.
Learning of state-space models with highly informative observations: a tempered Sequential Monte Carlo solution
In particular, for learning of unknown parameters in nonlinear state-space models, methods based on the particle filter (a Monte Carlo method) have proven very useful.
Smoothing with Couplings of Conditional Particle Filters
The method combines two recent breakthroughs: the first is a generic debiasing technique for Markov chains due to Rhee and Glynn, and the second is the introduction of a uniformly ergodic Markov chain for smoothing, the conditional particle filter of Andrieu, Doucet and Holenstein.
Methodology Computation
High-dimensional Filtering using Nested Sequential Monte Carlo
Sequential Monte Carlo (SMC) methods comprise one of the most successful approaches to approximate Bayesian filtering.
Pseudo-Marginal Hamiltonian Monte Carlo
When following a Markov chain Monte Carlo (MCMC) approach to approximate the posterior distribution in this context, one typically either uses MCMC schemes which target the joint posterior of the parameters and some auxiliary latent variables, or pseudo-marginal Metropolis--Hastings (MH) schemes.
Interacting Particle Markov Chain Monte Carlo
We introduce interacting particle Markov chain Monte Carlo (iPMCMC), a PMCMC method based on an interacting pool of standard and conditional sequential Monte Carlo samplers.
Accelerating pseudo-marginal Metropolis-Hastings by correlating auxiliary variables
Pseudo-marginal Metropolis-Hastings (pmMH) is a powerful method for Bayesian inference in models where the posterior distribution is analytical intractable or computationally costly to evaluate directly.
Sequential Monte Carlo Methods for System Identification
One of the key challenges in identifying nonlinear and possibly non-Gaussian state space models (SSMs) is the intractability of estimating the system state.
Quasi-Newton particle Metropolis-Hastings
A possible application is parameter inference in the challenging class of SSMs with intractable likelihoods.
Nested Sequential Monte Carlo Methods
NSMC generalises the SMC framework by requiring only approximate, properly weighted, samples from the SMC proposal distribution, while still resulting in a correct SMC algorithm.
Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering
Recently, the Frank-Wolfe optimization algorithm was suggested as a procedure to obtain adaptive quadrature rules for integrals of functions in a reproducing kernel Hilbert space (RKHS) with a potentially faster rate of convergence than Monte Carlo integration (and "kernel herding" was shown to be a special case of this procedure).
Identification of jump Markov linear models using particle filters
Jump Markov linear models consists of a finite number of linear state space models and a discrete variable encoding the jumps (or switches) between the different linear models.
Divide-and-Conquer with Sequential Monte Carlo
We propose a novel class of Sequential Monte Carlo (SMC) algorithms, appropriate for inference in probabilistic graphical models.
Sequential Monte Carlo for Graphical Models
We propose a new framework for how to use sequential Monte Carlo (SMC) algorithms for inference in probabilistic graphical models (PGM).
Particle Gibbs with Ancestor Sampling
Particle Markov chain Monte Carlo (PMCMC) is a systematic way of combining the two main tools used for Monte Carlo statistical inference: sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC).
Identification of Gaussian Process State-Space Models with Particle Stochastic Approximation EM
Gaussian process state-space models (GP-SSMs) are a very flexible family of models of nonlinear dynamical systems.
Particle Metropolis-Hastings using gradient and Hessian information
Particle Metropolis-Hastings (PMH) allows for Bayesian parameter inference in nonlinear state space models by combining Markov chain Monte Carlo (MCMC) and particle filtering.
Particle filter-based Gaussian process optimisation for parameter inference
Finally, we use a heuristic procedure to obtain a revised parameter iterate, providing an automatic trade-off between exploration and exploitation of the surrogate model.
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC
State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems.
Ancestor Sampling for Particle Gibbs
We present a novel method in the family of particle MCMC methods that we refer to as particle Gibbs with ancestor sampling (PG-AS).
