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.
A deep Neyman-Scott process uses Poisson processes for efficient inference in complex point processes.
HIP-GP improves GP inference for inter-domain observations with millions of inducing points.
Proposes a method to estimate and infer networks from multiple high-dimensional point processes.
We develop dependent hierarchical normalized random measures and apply them to dynamic topic modeling. The dependency arises via superposition, subsampling and point transition on the underlying Poisson processes of these measures. The measures used include normalised generalised Gamma processes that demonstrate power …
We propose deep convolutional Gaussian processes, a deep Gaussian process architecture with convolutional structure. The model is a principled Bayesian framework for detecting hierarchical combinations of local features for image classification. We demonstrate greatly improved image classification performance compared …
This paper introduces a novel framework for modeling temporal events with complex longitudinal dependency that are generated by dependent sources. This framework takes advantage of multidimensional point processes for modeling time of events. The intensity function of the proposed process is a mixture of intensities, a…
Proposes a neural network for accurate and reconciled hierarchical time series forecasting.
New approach for classification using trigonometric polynomial kernels from signal processing.
Posterior regularization enhances Bayesian hierarchical mixture clustering by improving node separation.
Bayesian Gaussian Processes layer detects out-of-distribution data in medical imaging.
The cooperative hierarchical structure is a common and significant data structure observed in, or adopted by, many research areas, such as: text mining (author-paper-word) and multi-label classification (label-instance-feature). Renowned Bayesian approaches for cooperative hierarchical structure modeling are mostly bas…
BITS for GAPS uses Bayesian methods to improve surrogate model accuracy in complex systems.
A new hierarchical clustering method selects representative points from sub-minimum-spanning-trees.
Change-point detection (CPD) aims to locate abrupt transitions in the generative model of a sequence of observations. When Bayesian methods are considered, the standard practice is to infer the posterior distribution of the change-point locations. However, for complex models (high-dimensional or heterogeneous), it is n…
The paper reformulates U-Nets as wavelet-based models and applies this to hierarchical VAEs.
We address the problem of analyzing sets of noisy time-varying signals that all report on the same process but confound straightforward analyses due to complex inter-signal heterogeneities and measurement artifacts. In particular we consider single-molecule experiments which indirectly measure the distinct steps in a b…
New model detects gradual changes in processes more accurately.
Deep Gaussian Processes (DGP) are hierarchical generalizations of Gaussian Processes (GP) that have proven to work effectively on a multiple supervised regression tasks. They combine the well calibrated uncertainty estimates of GPs with the great flexibility of multilayer models. In DGPs, given the inputs, the outputs …
Paper develops a new objective for hierarchical clustering in Euclidean space.
We prove a geometric model for HHS hierarchies as CAT(0) cube complexes.
Hierarchical beta process has found interesting applications in recent years. In this paper we present a modified hierarchical beta process prior with applications to hierarchical modeling of multiple data sources. The novel use of the prior over a hierarchical factor model allows factors to be shared across different …
This paper proposes a new meta-learning method -- named HARMLESS (HAwkes Relational Meta LEarning method for Short Sequences) for learning heterogeneous point process models from short event sequence data along with a relational network. Specifically, we propose a hierarchical Bayesian mixture Hawkes process model, whi…
A scalable Bayesian linear regression framework for spatial data.
Proposes GPHMEs using Gaussian processes for hierarchical expert models.
Conditional DGP learns effective kernels from low-fidelity data.
A novel extrapolation method is proposed for longitudinal forecasting. A hierarchical Gaussian process model is used to combine nonlinear population change and individual memory of the past to make prediction. The prediction error is minimized through the hierarchical design. The method is further extended to joint mod…
Modeling multiple Hawkes processes with shared dynamics using graphons.
Unified framework for efficient surrogate modeling in manufacturing.
The Dirichlet process and its extension, the Pitman-Yor process, are stochastic processes that take probability distributions as a parameter. These processes can be stacked up to form a hierarchical nonparametric Bayesian model. In this article, we present efficient methods for the use of these processes in this hierar…
Improved outlier detection in hierarchical Gaussian Processes using Wasserstein-2 kernels.
HIRM models noisy, sparse, heterogeneous relational data using hierarchical clustering and Dirichlet processes.
A new framework scales active search for large datasets.
We develop a nested hierarchical Dirichlet process (nHDP) for hierarchical topic modeling. The nHDP is a generalization of the nested Chinese restaurant process (nCRP) that allows each word to follow its own path to a topic node according to a document-specific distribution on a shared tree. This alleviates the rigid, …
MTNPs jointly model multiple correlated tasks from various sources.
Standard Gaussian Process (GP) regression, a powerful machine learning tool, is computationally expensive when it is applied to large datasets, and potentially inaccurate when data points are sparsely distributed in a high-dimensional feature space. To address these challenges, a new multiscale, sparsified GP algorithm…
LION generates high-quality 3D shapes using hierarchical latent diffusion models.
Proposes HypCSE for enhanced hierarchical clustering.
Neural NMF discovers hierarchical topics in multilayer data.
This work evaluates uncertainty in deep Gaussian processes.
Free Random Projection enhances reinforcement learning by naturally incorporating hierarchical structure.
Generative Adversarial Networks (GAN) can achieve promising performance on learning complex data distributions on different types of data. In this paper, we first show a straightforward extension of existing GAN algorithm is not applicable to point clouds, because the constraint required for discriminators is undefined…
We develop a nested hierarchical Dirichlet process (nHDP) for hierarchical topic modeling. The nHDP is a generalization of the nested Chinese restaurant process (nCRP) that allows each word to follow its own path to a topic node according to a document-specific distribution on a shared tree. This alleviates the rigid, …
The development of algorithms for hierarchical clustering has been hampered by a shortage of precise objective functions. To help address this situation, we introduce a simple cost function on hierarchies over a set of points, given pairwise similarities between those points. We show that this criterion behaves sensibl…
Modeling trading volume curves using hierarchical Poisson processes.
Proposes a nonparametric tensor factorization for sparse data.
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 …
Bayesian estimators for causal inference using hierarchical Gaussian Processes.
Hierarchical models are versatile tools for joint modeling of data sets arising from different, but related, sources. Fully Bayesian inference may, however, become computationally prohibitive if the source-specific data models are complex, or if the number of sources is very large. To facilitate computation, we propose…