Search Results for author:
Found 17 papers, 7 papers with code
Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion Modelling
To address these limitations, we introduce Neural Flow Diffusion Models (NFDM), a novel framework that enhances diffusion models by supporting a broader range of forward processes beyond the fixed linear Gaussian.
VISA: Variational Inference with Sequential Sample-Average Approximations
We present variational inference with sequential sample-average approximation (VISA), a method for approximate inference in computationally intensive models, such as those based on numerical simulations.
Neural Diffusion Models
In this paper, we present Neural Diffusion Models (NDMs), a generalization of conventional diffusion models that enables defining and learning time-dependent non-linear transformations of data.
E-Valuating Classifier Two-Sample Tests
Compared to $p$-values-based tests, tests with E-values have finite sample guarantees for the type I error.
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.
Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transport
Variational inference often minimizes the "reverse" Kullbeck-Leibler (KL) KL(q||p) from the approximate distribution q to the posterior p. Recent work studies the "forward" KL KL(p||q), which unlike reverse KL does not lead to variational approximations that underestimate uncertainty.
Variational Combinatorial Sequential Monte Carlo Methods for Bayesian Phylogenetic Inference
Bayesian phylogenetic inference is often conducted via local or sequential search over topologies and branch lengths using algorithms such as random-walk Markov chain Monte Carlo (MCMC) or Combinatorial Sequential Monte Carlo (CSMC).
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.
Elements of Sequential Monte Carlo
A core problem in statistics and probabilistic machine learning is to compute probability distributions and expectations.
Variational Sequential Monte Carlo
The success of variational approaches depends on (i) formulating a flexible parametric family of distributions, and (ii) optimizing the parameters to find the member of this family that most closely approximates the exact posterior.
High-dimensional Filtering using Nested Sequential Monte Carlo
Sequential Monte Carlo (SMC) methods comprise one of the most successful approaches to approximate Bayesian filtering.
Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms
Variational inference using the reparameterization trick has enabled large-scale approximate Bayesian inference in complex probabilistic models, leveraging stochastic optimization to sidestep intractable expectations.
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.
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.
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.
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).
