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Found 16 papers, 7 papers with code
Chained Generalisation Bounds
This work discusses how to derive upper bounds for the expected generalisation error of supervised learning algorithms by means of the chaining technique.
Neural Score Matching for High-Dimensional Causal Inference
We leverage these balancing scores to perform matching for high-dimensional causal inference and call this procedure neural score matching.
Conditional Simulation Using Diffusion Schrödinger Bridges
Denoising diffusion models have recently emerged as a powerful class of generative models.
On Mixing Times of Metropolized Algorithm With Optimization Step (MAO) : A New Framework
In this paper, we consider sampling from a class of distributions with thin tails supported on $\mathbb{R}^d$ and make two primary contributions.
Conditionally Gaussian PAC-Bayes
Recent studies have empirically investigated different methods to train stochastic neural networks on a classification task by optimising a PAC-Bayesian bound via stochastic gradient descent.
Quantitative Uniform Stability of the Iterative Proportional Fitting Procedure
We establish the uniform in time stability, w. r. t.
Wide stochastic networks: Gaussian limit and PAC-Bayesian training
The limit of infinite width allows for substantial simplifications in the analytical study of overparameterized neural networks.
Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms
As our main contribution, we prove that the generalization error of a stochastic optimization algorithm can be bounded based on the `complexity' of the fractal structure that underlies its invariant measure.
Differentiable Particle Filtering via Entropy-Regularized Optimal Transport
Particle Filtering (PF) methods are an established class of procedures for performing inference in non-linear state-space models.
Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks
Despite its success in a wide range of applications, characterizing the generalization properties of stochastic gradient descent (SGD) in non-convex deep learning problems is still an important challenge.
Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows
We show that normalising flows become pathological when used to model targets whose supports have complicated topologies.
Localised Generative Flows
We argue that flow-based density models based on continuous bijections are limited in their ability to learn target distributions with complicated topologies, and propose localised generative flows (LGFs) to address this problem.
Bernoulli Race Particle Filters
When the weights in a particle filter are not available analytically, standard resampling methods cannot be employed.
Unbiased Smoothing using Particle Independent Metropolis-Hastings
We consider the approximation of expectations with respect to the distribution of a latent Markov process given noisy measurements.
Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets
Bayesian inference via standard Markov Chain Monte Carlo (MCMC) methods is too computationally intensive to handle large datasets, since the cost per step usually scales like $\Theta(n)$ in the number of data points $n$.
