A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New method samples manifolds efficiently using Dirichlet distribution.
New portfolios outperform traditional methods by using factor weights.
The Dirichlet mechanism protects privacy while minimizing KL divergence.
We present a Dirichlet process mixture model over discrete incomplete rankings and study two Gibbs sampling inference techniques for estimating posterior clusterings. The first approach uses a slice sampling subcomponent for estimating cluster parameters. The second approach marginalizes out several cluster parameters …
The paper introduces a new model to correct bias in treatment effect estimates due to sample selection.
Sharp bounds for Dirichlet sums lead to improved Bayesian algorithm analysis.
Corrected and improved simulation methods for Dirichlet-Laplace prior.
A robust bandit algorithm uses Dirichlet sampling to minimize regret under various distributional assumptions.
TPBS models improve robustness to overfitting with localized Dirichlet energy regularization.
A Dirichlet -partition of a domain is a collection of pairwise disjoint open subsets such that the sum of their first Laplace-Dirichlet eigenvalues is minimal. A discrete version of Dirichlet partitions has been posed on graphs with applications in data analysis. Both versions admit va…
A Bayesian approach termed BAyesian Least Squares Optimization with Nonnegative L1-norm constraint (BALSON) is proposed. The error distribution of data fitting is described by Gaussian likelihood. The parameter distribution is assumed to be a Dirichlet distribution. With the Bayes rule, searching for the optimal parame…
Study on Dirichlet process mixtures for clustering consistency.
Datasets containing large samples of time-to-event data arising from several small heterogeneous groups are commonly encountered in statistics. This presents problems as they cannot be pooled directly due to their heterogeneity or analyzed individually because of their small sample size. Bayesian nonparametric modellin…
We develop a sequential low-complexity inference procedure for Dirichlet process mixtures of Gaussians for online clustering and parameter estimation when the number of clusters are unknown a-priori. We present an easily computable, closed form parametric expression for the conditional likelihood, in which hyperparamet…
The hierarchical Dirichlet process (HDP) has become an important Bayesian nonparametric model for grouped data, such as document collections. The HDP is used to construct a flexible mixed-membership model where the number of components is determined by the data. As for most Bayesian nonparametric models, exact posterio…
We present a mixed multinomial logit (MNL) model, which leverages the truncated stick-breaking process representation of the Dirichlet process as a flexible nonparametric mixing distribution. The proposed model is a Dirichlet process mixture model and accommodates discrete representations of heterogeneity, like a laten…
Efficiently approximates uncertainty in classification models using Dirichlet distributions.
DPPS uses DP priors for Bayesian non-parametric multi-arm bandits.
WBCP improves conformal prediction for distribution shifts using weighted Dirichlet posteriors.
Nonparametric mixture models based on the Dirichlet process are an elegant alternative to finite models when the number of underlying components is unknown, but inference in such models can be slow. Existing attempts to parallelize inference in such models have relied on introducing approximations, which can lead to in…
Nonparametric Thompson Sampling achieves optimal regret for risk-averse bandits with sub-Gaussian rewards.
Recurrent-DBN models dynamic relational data with interpretable latent structures.
A new model separates persistence and transition priors in HDP-HMM.
We propose an exact slice sampler for Hierarchical Dirichlet process (HDP) and its associated mixture models (Teh et al., 2006). Although there are existing MCMC algorithms for sampling from the HDP, a slice sampler has been missing from the literature. Slice sampling is well-known for its desirable properties includin…
We study the problem of multimodal generative modelling of images based on generative adversarial networks (GANs). Despite the success of existing methods, they often ignore the underlying structure of vision data or its multimodal generation characteristics. To address this problem, we introduce the Dirichlet prior fo…
DBU models struggle with robust uncertainty estimates under adversarial attacks.
New model identifies microbial subcommunities robustly, accounting for cross-sample heterogeneity.
A new method uses a product of experts with Dirichlet variables to approximate complex distributions.
Develops a more flexible HDP-HMM for temporal data segmentation.
Improves DPGMM sampler by better initializing subclusters for more effective clustering.
Paper presents a reparameterized DP-DLGMM for clustering.
The Dirichlet process mixture (DPM) is a ubiquitous, flexible Bayesian nonparametric statistical model. However, full probabilistic inference in this model is analytically intractable, so that computationally intensive techniques such as Gibb's sampling are required. As a result, DPM-based methods, which have considera…
Improved Gaussian process experts model for complex data.
A new method for density estimation using nearest neighbor Dirichlet mixtures.
Deviance-style normalization for sparse, jointly overdispersed count matrices
In this technical report, we present jLDADMM---an easy-to-use Java toolkit for conventional topic models. jLDADMM is released to provide alternatives for topic modeling on normal or short texts. It provides implementations of the Latent Dirichlet Allocation topic model and the one-topic-per-document Dirichlet Multinomi…
A new algorithm improves topic model accuracy in small data sets.
The class of chain event graph models is a generalisation of the class of discrete Bayesian networks, retaining most of the structural advantages of the Bayesian network for model interrogation, propagation and learning, while more naturally encoding asymmetric state spaces and the order in which events happen. In this…
Generative model for high-dimensional categorical data using Gaussian-Dirichlet fields.
Adapts DPMM for fast streaming data clustering.
Proposes a nonparametric tensor factorization for sparse data.
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. …
Capital distribution curve is defined as log-log plot of normalized stock capitalizations ranked in descending order. The curve displays remarkable stability over periods of time. Theory of exchangeable distributions on set partitions, developed for purposes of mathematical genetics and recently applied in non-parametr…
Bayesian nonparametrics improves data-driven risk optimization under distributional uncertainty.
The paper proves spectral convergence rates for graph Laplacian to manifold Laplace-Beltrami operator.
Many complex dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes. We consider two such models: the switching linear dynamical system (SLDS) and the switching vector autoregressive (VAR) process. Our Bayesian nonparametric approach utilizes a hiera…
This paper proposes a novel dynamic Hierarchical Dirichlet Process topic model that considers the dependence between successive observations. Conventional posterior inference algorithms for this kind of models require processing of the whole data through several passes. It is computationally intractable for massive or …
The latent Dirichlet allocation (LDA) model is a widely-used latent variable model in machine learning for text analysis. Inference for this model typically involves a single-site collapsed Gibbs sampling step for latent variables associated with observations. The efficiency of the sampling is critical to the success o…