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.
The paper introduces a method to probabilistically select inducing points in sparse Gaussian processes.
New method combines spectral and sparse methods for Gaussian processes.
Sparse Gaussian Processes improve scalability by learning inducing points from data.
New method optimizes Gaussian process allocation for BO.
Sparse GPs improved with nearest neighbor inducing variables.
New method improves stability of Gaussian process approximations.
A new method selects inducing points to optimize high-throughput Bayesian optimisation.
VNNGP uses nearest neighbors to approximate GPs, improving scalability and performance.
Paper tightens variational GP approximations for large datasets.
Sparse Markovian Gaussian processes improve probabilistic model inference for large datasets.
New method DDVI improves posterior inference for deep Gaussian processes.
The paper proposes a method to detect credit card fraud using sparse Gaussian approximations.
Improved sparse Gaussian processes using structured scaling matrices and Power-EP framework.
To answer the existence of optimal swimmer learning/teaching strategies, this work introduces a two-level clustering in order to analyze temporal dynamics of motor learning in breaststroke swimming. Each level have been performed through Sparse Fisher-EM, a unsupervised framework which can be applied efficiently on lar…
Kernel methods on discrete domains have shown great promise for many challenging data types, for instance, biological sequence data and molecular structure data. Scalable kernel methods like Support Vector Machines may offer good predictive performances but do not intrinsically provide uncertainty estimates. In contras…
Sparse Gaussian process hyperparameters optimized using MCMC.
We introduce a new interpretation of sparse variational approximations for Gaussian processes using inducing points, which can lead to more scalable algorithms than previous methods. It is based on decomposing a Gaussian process as a sum of two independent processes: one spanned by a finite basis of inducing points and…
Gaussian Processes (GPs) are powerful kernelized methods for non-parameteric regression used in many applications. However, their use is limited to a few thousand of training samples due to their cubic time complexity. In order to scale GPs to larger datasets, several sparse approximations based on so-called inducing p…
SKI accelerates GP inference with sparse grids to handle higher dimensions.
The variational framework for learning inducing variables (Titsias, 2009a) has had a large impact on the Gaussian process literature. The framework may be interpreted as minimizing a rigorously defined Kullback-Leibler divergence between the approximating and posterior processes. To our knowledge this connection has th…
Stochastic Sparse Subspace Clustering improves subspace clustering by reducing over-segmentation through dropout.
Bayesian approach improves performance in Gaussian process models.
The use of Gaussian process models is typically limited to datasets with a few tens of thousands of observations due to their complexity and memory footprint. The two most commonly used methods to overcome this limitation are 1) the variational sparse approximation which relies on inducing points and 2) the state-space…
VAR-GPs solve continual learning by updating posteriors sequentially.
Exact Gaussian Processes for massive datasets using non-stationary sparsity-discovering kernels.
In this paper, a sparse Markov decision process (MDP) with novel causal sparse Tsallis entropy regularization is proposed.The proposed policy regularization induces a sparse and multi-modal optimal policy distribution of a sparse MDP. The full mathematical analysis of the proposed sparse MDP is provided.We first analyz…
New method speeds up sparse Gaussian processes for large datasets.
Sparse optimization refers to an optimization problem involving the zero-norm in objective or constraints. In this paper, nonconvex approximation approaches for sparse optimization have been studied with a unifying point of view in DC (Difference of Convex functions) programming framework. Considering a common DC appro…
Gaussian processes (GPs) are flexible models that can capture complex structure in large-scale dataset due to their non-parametric nature. However, the usage of GPs in real-world application is limited due to their high computational cost at inference time. In this paper, we introduce a new framework, \textit{kernel di…
Learning new representations of input observations in machine learning is often tackled using a factorization of the data. For many such problems, including sparse coding and matrix completion, learning these factorizations can be difficult, in terms of efficiency and to guarantee that the solution is a global minimum.…
This paper addresses the problem of sparsity penalized least squares for applications in sparse signal processing, e.g. sparse deconvolution. This paper aims to induce sparsity more strongly than L1 norm regularization, while avoiding non-convex optimization. For this purpose, this paper describes the design and use of…
Gaussian process (GP) models form a core part of probabilistic machine learning. Considerable research effort has been made into attacking three issues with GP models: how to compute efficiently when the number of data is large; how to approximate the posterior when the likelihood is not Gaussian and how to estimate co…
Bayesian approach improves sparse PCE for high-dimensional problems.
Early alignment in neural networks leads to sparse representations but hinders convergence.
Develops SGP-VAE for efficient sparse GP inference in multi-dimensional datasets.
Inducing sparseness while training neural networks has been shown to yield models with a lower memory footprint but similar effectiveness to dense models. However, sparseness is typically induced starting from a dense model, and thus this advantage does not hold during training. We propose techniques to enforce sparsen…
Scalable algorithm for sampling Gaussian processes using sparse grids and preconditioners.
We design simple screening tests to automatically discard data samples in empirical risk minimization without losing optimization guarantees. We derive loss functions that produce dual objectives with a sparse solution. We also show how to regularize convex losses to ensure such a dual sparsity-inducing property, and p…
We study the problem of learning high dimensional regression models regularized by a structured-sparsity-inducing penalty that encodes prior structural information on either input or output sides. We consider two widely adopted types of such penalties as our motivating examples: 1) overlapping group lasso penalty, base…
A new method for efficient Gaussian process inference using sparse approximations.
This paper proposes a method to reveal task relationships in multi-task learning models using sparse graphs.
Sparse Subspace Clustering (SSC) is a popular unsupervised machine learning method for clustering data lying close to an unknown union of low-dimensional linear subspaces; a problem with numerous applications in pattern recognition and computer vision. Even though the behavior of SSC for complete data is by now well-un…
The paper analyzes uncertainty quantification in sparse Gaussian process regression with a Brownian motion prior.
SVGP KAN integrates sparse variational GP with KANs for scalable probabilistic inference.
New sparse GP model learns compositional kernels efficiently.
Sparse estimation methods are aimed at using or obtaining parsimonious representations of data or models. They were first dedicated to linear variable selection but numerous extensions have now emerged such as structured sparsity or kernel selection. It turns out that many of the related estimation problems can be cast…
Paper improves robustness of PINNs by smoothing and quantifying uncertainty.
A method to improve sequential learning by keeping past data errors in check.