arXiv research

SpinSVAR estimates SVAR models with sparse input, improving accuracy and scalability.

A new method builds sparse polynomial chaos expansions for models with dependent inputs.

A new method warps inputs to learn nonstationary kernels efficiently.

Sparse codes improve optimal control tasks with correlated inputs.

Given two sets of variables, derived from a common set of samples, sparse Canonical Correlation Analysis (CCA) seeks linear combinations of a small number of variables in each set, such that the induced canonical variables are maximally correlated. Sparse CCA is NP-hard. We propose a novel combinatorial algorithm for s…

We present a novel method for exact hierarchical sparse polynomial regression. Our regressor is that degree $r$ polynomial which depends on at most $k$ inputs, counting at most $\ell$ monomial terms, which minimizes the sum of the squares of its prediction errors. The previous hierarchical sparse specification aligns w…

Sparse models help in selecting fewer variables for efficient predictions.

Sparse DNN challenge aims to improve graph data analysis.

Sparse neural networks can match dense models on Lipschitz functions.

Sparse Gaussian Processes improve scalability by learning inducing points from data.

EASIER-net uses sparse networks to improve prediction accuracy for high-dimensional data.

New algorithm speeds up regression tasks for large datasets.

NGRs merge sparse graph recovery with PGMs for efficient probabilistic inference.

Reduces function approximation dimensions from high to low with sparse data.

Applying machine learning techniques to the quickly growing data in science and industry requires highly-scalable algorithms. Large datasets are most commonly processed "data parallel" distributed across many nodes. Each node's contribution to the overall gradient is summed using a global allreduce. This allreduce is t…

While most classical approaches to Granger causality detection repose upon linear time series assumptions, many interactions in neuroscience and economics applications are nonlinear. We develop an approach to nonlinear Granger causality detection using multilayer perceptrons where the input to the network is the past t…

New method finds sparse groups of input variables for neural networks.

Self-attention prefers sparse functions of input sequences, reducing sample complexity.

The sparse pseudo-input Gaussian process (SPGP) is a new approximation method for speeding up GP regression in the case of a large number of data points N. The approximation is controlled by the gradient optimization of a small set of M `pseudo-inputs', thereby reducing complexity from N^3 to NM^2. One limitation of th…

The study develops a supervised and unsupervised WTA model for sparse binary projections.

The class of Gaussian Process (GP) methods for Temporal Difference learning has shown promise for data-efficient model-free Reinforcement Learning. In this paper, we consider a recent variant of the GP-SARSA algorithm, called Sparse Pseudo-input Gaussian Process SARSA (SPGP-SARSA), and derive recursive formulas for its…

New DRGP models improve prediction accuracy for sequential data.

Neural networks are usually not the tool of choice for nonparametric high-dimensional problems where the number of input features is much larger than the number of observations. Though neural networks can approximate complex multivariate functions, they generally require a large number of training observations to obtai…

Sparse GPs improved with nearest neighbor inducing variables.

Standard sparse pseudo-input approximations to the Gaussian process (GP) cannot handle complex functions well. Sparse spectrum alternatives attempt to answer this but are known to over-fit. We suggest the use of variational inference for the sparse spectrum approximation to avoid both issues. We model the covariance fu…

A simple 2-layer linear network outperforms neural networks in learning sparse targets.

RandNet learns from compressed image data, improving efficiency and accuracy.

Gaussian processes with differential privacy protect both inputs and outputs.

New method recovers kernel and sparse inputs from convolved data efficiently.

We consider the following multi-component sparse PCA problem: given a set of data points, we seek to extract a small number of sparse components with disjoint supports that jointly capture the maximum possible variance. These components can be computed one by one, repeatedly solving the single-component problem and def…

SANs use sparse activation functions to compress data representations.

Improves sparse recovery with non-linear Fourier features.

Sparse neural encoding can store more memories as targets become sparser.

Paper solves complex signal processing problem efficiently.

Sparse coding is an unsupervised learning algorithm that learns a succinct high-level representation of the inputs given only unlabeled data; it represents each input as a sparse linear combination of a set of basis functions. Originally applied to modeling the human visual cortex, sparse coding has also been shown to …

A heuristic method for determining input ranges for complex processes.

The paper uses TDA to select stocks for a sparse portfolio, improving performance across market scenarios.

New method speeds up sparse graph neural networks training on dense hardware.

Kernel regression is a popular non-parametric fitting technique. It aims at learning a function which estimates the targets for test inputs as precise as possible. Generally, the function value for a test input is estimated by a weighted average of the surrounding training examples. The weights are typically computed b…

RGAM builds more accurate models by preferring linear features over non-linear ones.

SigGPDE scales sparse Gaussian processes for sequential data.

LSDAT reduces query efficiency for decision-based adversarial attacks.

DFR reduces the computational cost of sparse-group lasso and adaptive sparse-group lasso.

Improved Frank-Wolfe algorithm speeds up training of differentially private LASSO models.

Sparse coding is a crucial subroutine in algorithms for various signal processing, deep learning, and other machine learning applications. The central goal is to learn an overcomplete dictionary that can sparsely represent a given input dataset. However, a key challenge is that storage, transmission, and processing of …

Sparse principal component analysis (SPCA) has emerged as a powerful technique for modern data analysis, providing improved interpretation of low-rank structures by identifying localized spatial structures in the data and disambiguating between distinct time scales. We demonstrate a robust and scalable SPCA algorithm b…

Bayesian framework predicts aerodynamic uncertainty from sparse measurements.

SGM combines deep learning and planning for robust long-horizon tasks.