arXiv research

FlexCodeTS is a flexible time series density estimator.

Combines MCTM and NF for flexible multivariate density regression with interpretable marginals.

New framework quantifies uncertainty in flexible density-based clustering.

A normalizing flow models a complex probability density as an invertible transformation of a simple base density. Flows based on either coupling or autoregressive transforms both offer exact density evaluation and sampling, but rely on the parameterization of an easily invertible elementwise transformation, whose choic…

New tractable density models from squaring neural networks.

Gradient Boosted Normalizing Flows improve flexibility of NFs without increasing complexity.

FISHDBC is a flexible, incremental, scalable, and hierarchical density-based clustering algorithm. It is flexible because it empowers users to work on arbitrary data, skipping the feature extraction step that usually transforms raw data in numeric arrays letting users define an arbitrary distance function instead. It i…

WDL models density curves using Wasserstein distance and flexible mixture models.

SPQR package uses neural networks for flexible quantile regression.

Copula-based normalizing flows improve flexibility and stability for heavy-tailed data.

A new density model using Fourier basis achieves better approximations and compression.

Roundtrip uses deep generative models for flexible density estimation.

Survival MDN uses invertible functions to speed up survival analysis models.

Neural-g models mixtures of densities with flexibility and accuracy.

We build on the work in Fackler and King 1990, and propose a more general calibration model for implied risk neutral densities. Our model allows for the joint calibration of a set of densities at different maturities and dates through a Bayesian dynamic Beta Markov Random Field. Our approach allows for possible time de…

CEBMs learn flexible latent mappings from data.

Unified view of score estimators for flexible densities.

E$^2$M optimizes tensor density estimation by relaxing $α$-divergence to KL-divergence.

In this paper we propose a model with a Dirichlet process mixture of gamma densities in the bulk part below threshold and a generalized Pareto density in the tail for extreme value estimation. The proposed model is simple and flexible allowing us posterior density estimation and posterior inference for high quantiles. …

Stan is a probabilistic programming language that has been increasingly used for real-world scalable projects. However, to make practical inference possible, the language sacrifices some of its usability by adopting a block syntax, which lacks compositionality and flexible user-defined functions. Moreover, the semantic…

A new framework for flexible neural network receptive fields.

Proposes a new method for high-dimensional density estimation.

RNE provides a flexible framework for diffusion models, enabling inference-time control and energy-based training.

GCAE uses density estimation to achieve reliable disentanglement in latent space.

This paper describes a recursive estimation procedure for multivariate binary densities (probability distributions of vectors of Bernoulli random variables) using orthogonal expansions. For $d$ covariates, there are $2^d$ basis coefficients to estimate, which renders conventional approaches computationally prohibitive …

Autoregressive models are among the best performing neural density estimators. We describe an approach for increasing the flexibility of an autoregressive model, based on modelling the random numbers that the model uses internally when generating data. By constructing a stack of autoregressive models, each modelling th…

Maximum entropy modeling is a flexible and popular framework for formulating statistical models given partial knowledge. In this paper, rather than the traditional method of optimizing over the continuous density directly, we learn a smooth and invertible transformation that maps a simple distribution to the desired ma…

The study applies spatial density models to mobile node movements using Möbius distributions.

Neural density estimators are flexible families of parametric models which have seen widespread use in unsupervised machine learning in recent years. Maximum-likelihood training typically dictates that these models be constrained to specify an explicit density. However, this limitation can be overcome by instead using …

Riesz regression connects to density ratio estimation for causal inference.

DDN models flexible free-form conditional distributions.

Semi-implicit variational inference (SIVI) is introduced to expand the commonly used analytic variational distribution family, by mixing the variational parameter with a flexible distribution. This mixing distribution can assume any density function, explicit or not, as long as independent random samples can be generat…

In the modal approach to clustering, clusters are defined as the local maxima of the underlying probability density function, where the latter can be estimated either non-parametrically or using finite mixture models. Thus, clusters are closely related to certain regions around the density modes, and every cluster corr…

Learning a distribution conditional on a set of discrete-valued features is a commonly encountered task. This becomes more challenging with a high-dimensional feature set when there is the possibility of interaction between the features. In addition, many frequently applied techniques consider only prediction of the me…

Unified representation of density-power-based divergences simplifies estimation to M-estimation.

TTF improves performance of normalizing flows for heavy-tailed distributions.

Modeling complex conditional distributions is critical in a variety of settings. Despite a long tradition of research into conditional density estimation, current methods employ either simple parametric forms or are difficult to learn in practice. This paper employs normalising flows as a flexible likelihood model and …

Normalized compound random measures are flexible nonparametric priors for related distributions. We consider building general nonparametric regression models using normalized compound random measure mixture models. Posterior inference is made using a novel pseudo-marginal Metropolis-Hastings sampler for normalized comp…

Neural network model improves loss reserving accuracy and distribution flexibility.

In this paper we contribute a novel algorithm family, which generalizes many unsupervised techniques including unnormalized and energy models, and allows us to infer different statistical modalities (e.g. data likelihood and ratio between densities) from data samples. The proposed unsupervised technique, named Probabil…

Logistic Gaussian process (LGP) priors provide a flexible alternative for modelling unknown densities. The smoothness properties of the density estimates can be controlled through the prior covariance structure of the LGP, but the challenge is the analytically intractable inference. In this paper, we present approximat…

We model the dynamics of asset prices and associated derivatives by consideration of the dynamics of the conditional probability density process for the value of an asset at some specified time in the future. In the case where the price process is driven by Brownian motion, an associated "master equation" for the dynam…

Flow Matching improves statistical guarantees through kernel density estimation.

SPH-ParVI uses fluid dynamics to sample unknown densities efficiently.

We introduce a copula mixture model to perform dependency-seeking clustering when co-occurring samples from different data sources are available. The model takes advantage of the great flexibility offered by the copulas framework to extend mixtures of Canonical Correlation Analysis to multivariate data with arbitrary c…

Paper proposes a novel approach to density ratio estimation using projection pursuit.

Transformer with denoising diffusion improves probabilistic density estimation.

GNet uses Gaussian processes for scalable, flexible neural networks.