203 citations · 1.6k across the 78 of their papers we have counts for
17 papers · 1 filter
How to Approximate Inference with Subtractive Mixture Models
Lena Zellinger, Nicola Branchini, Lennert De Smet +3
Classical mixture models (MMs) are widely used tractable proposals for approximate inference settings such as variational inference (VI) and importance sampling (IS). Recently, mix…
Signature-Kernel Based Evaluation Metrics for Robust Probabilistic and Tail-Event Forecasting
Benjamin R. Redhead, Thomas L. Lee, Peng Gu +2
Probabilistic forecasting is increasingly critical across high-stakes domains, from finance and epidemiology to climate science. However, current evaluation frameworks lack a conse…
Discrete diffusion samplers and bridges: Off-policy algorithms and applications in latent spaces
Arran Carter, Sanghyeok Choi, Kirill Tamogashev +2
Sampling from a distribution known up to a normalising constant is an important and challenging problem in statistics. Recent years have seen the…
Reinforced sequential Monte Carlo for amortised sampling
Sanghyeok Choi, Sarthak Mittal, Víctor Elvira +2
This paper proposes a synergy of amortised and particle-based methods for sampling from distributions defined by unnormalised density functions. We state a connection between seque…
Multi-Marginal Flow Matching with Adversarially Learnt Interpolants
Oskar Kviman, Kirill Tamogashev, Nicola Branchini +3
Learning the dynamics of a process given sampled observations at several time points is an important but difficult task in many scientific applications. When no ground-truth trajec…
Scalable Expectation Estimation with Subtractive Mixture Models
Lena Zellinger, Nicola Branchini, Víctor Elvira +1
Many Monte Carlo (MC) and importance sampling (IS) methods use mixture models (MMs) for their simplicity and ability to capture multimodal distributions. Recently, subtractive mixt…
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks
Benjamin Cox, Santiago Segarra, Victor Elvira
State-space models are a popular statistical framework for analysing sequential data. Within this framework, particle filters are often used to perform inference on non-linear stat…
Regime Learning for Differentiable Particle Filters
John-Joseph Brady, Yuhui Luo, Wenwu Wang +2
Differentiable particle filters are an emerging class of models that combine sequential Monte Carlo techniques with the flexibility of neural networks to perform state space infere…
Efficient Mixture Learning in Black-Box Variational Inference
Alexandra Hotti, Oskar Kviman, Ricky Molén +2
Mixture variational distributions in black box variational inference (BBVI) have demonstrated impressive results in challenging density estimation tasks. However, currently scaling…
Graphs in State-Space Models for Granger Causality in Climate Science
Víctor Elvira, Émilie Chouzenoux, Jordi Cerdà +1
Granger causality (GC) is often considered not an actual form of causality. Still, it is arguably the most widely used method to assess the predictability of a time series from ano…
Sparse Graphical Linear Dynamical Systems
Emilie Chouzenoux, Victor Elvira
Time-series datasets are central in machine learning with applications in numerous fields of science and engineering, such as biomedicine, Earth observation, and network analysis.…
Bayesian data fusion with shared priors
Peng Wu, Tales Imbiriba, Victor Elvira +1
The integration of data and knowledge from several sources is known as data fusion. When data is only available in a distributed fashion or when different sensors are used to infer…
Efficient Bayes Inference in Neural Networks through Adaptive Importance Sampling
Yunshi Huang, Emilie Chouzenoux, Victor Elvira +1
Bayesian neural networks (BNNs) have received an increased interest in the last years. In BNNs, a complete posterior distribution of the unknown weight and bias parameters of the n…
Hamiltonian Adaptive Importance Sampling
Ali Mousavi, Reza Monsefi, Víctor Elvira
Importance sampling (IS) is a powerful Monte Carlo (MC) methodology for approximating integrals, for instance in the context of Bayesian inference. In IS, the samples are simulated…
Cooperation in the Latent Space: The Benefits of Adding Mixture Components in Variational Autoencoders
Oskar Kviman, Ricky Molén, Alexandra Hotti +3
In this paper, we show how the mixture components cooperate when they jointly adapt to maximize the ELBO. We build upon recent advances in the multiple and adaptive importance samp…
Multiple Importance Sampling ELBO and Deep Ensembles of Variational Approximations
Oskar Kviman, Harald Melin, Hazal Koptagel +2
In variational inference (VI), the marginal log-likelihood is estimated using the standard evidence lower bound (ELBO), or improved versions as the importance weighted ELBO (IWELBO…
Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space
Steven Van Vaerenbergh, Ignacio Santamaria, Victor Elvira +1
In this paper, we study the problem of locating a predefined sequence of patterns in a time series. In particular, the studied scenario assumes a theoretical model is available tha…