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.
Generative Kernel PCA explores latent spaces for data interpretation and novelty detection.
We introduce a novel kernel that models input-dependent couplings across multiple latent processes. The pairwise joint kernel measures covariance along inputs and across different latent signals in a mutually-dependent fashion. A latent correlation Gaussian process (LCGP) model combines these non-stationary latent comp…
The paper tackles model collapse in GPLVMs by improving kernel flexibility and projection variance.
Enhances deep kernel learning with stochastic latent variables for better model regularization.
We introduce two kernels that extend the mean map, which embeds probability measures in Hilbert spaces. The generative mean map kernel (GMMK) is a smooth similarity measure between probabilistic models. The latent mean map kernel (LMMK) generalizes the non-iid formulation of Hilbert space embeddings of empirical distri…
We propose a kernel-based nonparametric test of relative goodness of fit, where the goal is to compare two models, both of which may have unobserved latent variables, such that the marginal distribution of the observed variables is intractable. The proposed test generalizes the recently proposed kernel Stein discrepanc…
Bayesian model merges multi-view latent models and kernel methods.
Enhances GPLVM for multi-view data with scalable latent representation learning.
cvHM framework speeds up GP inference for neural spike train analysis.
SKR-VAE improves VAEs for ICA with reduced computational cost.
A method selects key genes from tumor transcriptomics data using kernel methods and improves classification performance.
Method learns latent dynamics of complex systems from noisy data.
Zero-inflated datasets, which have an excess of zero outputs, are commonly encountered in problems such as climate or rare event modelling. Conventional machine learning approaches tend to overestimate the non-zeros leading to poor performance. We propose a novel model family of zero-inflated Gaussian processes (ZiGP) …
A new generator uses kernel distance to avoid GAN weaknesses.
Paper develops efficient estimator for Hawkes processes using representer theorem.
Spectral methods have greatly advanced the estimation of latent variable models, generating a sequence of novel and efficient algorithms with strong theoretical guarantees. However, current spectral algorithms are largely restricted to mixtures of discrete or Gaussian distributions. In this paper, we propose a kernel m…
A scalable factorized Gaussian process VAE for faster inference.
The study addresses negative transfer in multi-output Gaussian processes by proposing latent structures.
Scalable Gaussian processes with latent Kronecker structure for large datasets.
Recent studies identified that sequential Recommendation is improved by the attention mechanism. By following this development, we propose Relation-Aware Kernelized Self-Attention (RKSA) adopting a self-attention mechanism of the Transformer with augmentation of a probabilistic model. The original self-attention of Tra…
A scalable GPVAE method using local adjacencies to approximate GP inference.
Kernel Three-Pass Regression Filter improves forecasting efficiency for nonlinear dependencies.
Latent Dirichlet Allocation models discrete data as a mixture of discrete distributions, using Dirichlet beliefs over the mixture weights. We study a variation of this concept, in which the documents' mixture weight beliefs are replaced with squashed Gaussian distributions. This allows documents to be associated with e…
Physics Informed Deep Kernel Learning improves prediction accuracy and uncertainty quantification.
A latent force model is a Gaussian process with a covariance function inspired by a differential operator. Such covariance function is obtained by performing convolution integrals between Green's functions associated to the differential operators, and covariance functions associated to latent functions. In the classica…
Proxy methods adapt to distribution shifts without explicitly modeling latent confounders.
A new autoencoder method uses empirical beta copulas for generating data.
Multimodal learning aims to discover the relationship between multiple modalities. It has become an important research topic due to extensive multimodal applications such as cross-modal retrieval. This paper attempts to address the modality heterogeneity problem based on Gaussian process latent variable models (GPLVMs)…
Unsupervised learning on imbalanced data is challenging because, when given imbalanced data, current model is often dominated by the major category and ignores the categories with small amount of data. We develop a latent variable model that can cope with imbalanced data by dividing the latent space into a shared space…
MetaVRF learns adaptive kernels for fast few-shot learning.
Existing multi-view learning methods based on kernel function either require the user to select and tune a single predefined kernel or have to compute and store many Gram matrices to perform multiple kernel learning. Apart from the huge consumption of manpower, computation and memory resources, most of these models see…
We propose an unsupervised object matching method for relational data, which finds matchings between objects in different relational datasets without correspondence information. For example, the proposed method matches documents in different languages in multi-lingual document-word networks without dictionaries nor ali…
New method improves Gaussian process regression on complex, sparse point clouds.
In this paper we propose a family of tractable kernels that is dense in the family of bounded positive semi-definite functions (i.e. can approximate any bounded kernel with arbitrary precision). We start by discussing the case of stationary kernels, and propose a family of spectral kernels that extends existing approac…
Paper proposes a new method to learn distribution kernels via entropy maximization.
The analysis of data sets arising from multiple sensors has drawn significant research attention over the years. Traditional methods, including kernel-based methods, are typically incapable of capturing nonlinear geometric structures. We introduce a latent common manifold model underlying multiple sensor observations f…
This work improves fair tensor decomposition using a kernel criterion.
Paper presents variational estimates for EBLVMs without structural assumptions.
A simple and widely adopted approach to extend Gaussian processes (GPs) to multiple outputs is to model each output as a linear combination of a collection of shared, unobserved latent GPs. An issue with this approach is choosing the number of latent processes and their kernels. These choices are typically done manuall…
Improved forecasting of suicide attempts using LSGPs for patients with little data.
Method learns low-dim. state vars from noisy high-dim. data.
Bayesian TNKMs automatically infer model complexity and feature relevance.
We consider a Gaussian process formulation of the multiple kernel learning problem. The goal is to select the convex combination of kernel matrices that best explains the data and by doing so improve the generalisation on unseen data. Sparsity in the kernel weights is obtained by adopting a hierarchical Bayesian approa…
UT module refines VAE latent space, improving disentanglement and interpretability.
We present a multi-task learning formulation for Deep Gaussian processes (DGPs), through non-linear mixtures of latent processes. The latent space is composed of private processes that capture within-task information and shared processes that capture across-task dependencies. We propose two different methods for segmen…
Latent variable models improve RL by facilitating efficient learning and exploration.
We introduce a Bayesian Gaussian process latent variable model that explicitly captures spatial correlations in data using a parameterized spatial kernel and leveraging structure-exploiting algebra on the model covariance matrices for computational tractability. Inference is made tractable through a collapsed variation…
ABC learns context-aware representations for clustering.