arXiv research

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

Sparsity-constrained optimization has wide applicability in machine learning, statistics, and signal processing problems such as feature selection and compressive Sensing. A vast body of work has studied the sparsity-constrained optimization from theoretical, algorithmic, and application aspects in the context of spars…

Sparse RSP routing improves graph exploration and classification.

Novel confidence intervals improve convergence rates for sparse kernel-based models.

Proposes a robust and sparse portfolio selection model to reduce estimation errors and transaction costs.

New method reduces regret for sparse adversarial SSP problems.

We consider the minimum cost intervention design problem: Given the essential graph of a causal graph and a cost to intervene on a variable, identify the set of interventions with minimum total cost that can learn any causal graph with the given essential graph. We first show that this problem is NP-hard. We then prove…

Origin-destination (OD) matrices are often used in urban planning, where a city is partitioned into regions and an element (i, j) in an OD matrix records the cost (e.g., travel time, fuel consumption, or travel speed) from region i to region j. In this paper, we partition a day into multiple intervals, e.g., 96 15-min …

In presence of sparse noise we propose kernel regression for predicting output vectors which are smooth over a given graph. Sparse noise models the training outputs being corrupted either with missing samples or large perturbations. The presence of sparse noise is handled using appropriate use of $\ell_1$-norm along-wi…

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…

ADAPT method controls balance between sparse generator and discriminator in GANs.

The nearest-centroid classifier is a simple linear-time classifier based on computing the centroids of the data classes in the training phase, and then assigning a new datum to the class corresponding to its nearest centroid. Thanks to its very low computational cost, the nearest-centroid classifier is still widely use…

New methods reduce computational cost for Gaussian Markov Random Fields with sparse constraints.

New research challenges the idea that counterfactual explanations should be sparse.

SnAp approximates RTRL for online training of sparse recurrent networks.

A new framework compresses neural networks using sparse optimization.

We address the sparse signal recovery problem in the context of multiple measurement vectors (MMV) when elements in each nonzero row of the solution matrix are temporally correlated. Existing algorithms do not consider such temporal correlations and thus their performance degrades significantly with the correlations. I…

Online method learns sparse models efficiently in large scale settings.

SSVI efficiently trains sparse Bayesian neural networks with minimal compression and performance loss.

Proposes an efficient method for sparse index tracking with $\ell_0$-norm constraints.

We propose an inference method to estimate sparse interactions and biases according to Boltzmann machine learning. The basis of this method is $L_1$ regularization, which is often used in compressed sensing, a technique for reconstructing sparse input signals from undersampled outputs. $L_1$ regularization impedes the …

Efficient ANN search for sparse embeddings in ads targeting.

Meta-learn sparse Gaussian process inference for faster predictions.

Learning sparse combinations is a frequent theme in machine learning. In this paper, we study its associated optimization problem in the distributed setting where the elements to be combined are not centrally located but spread over a network. We address the key challenges of balancing communication costs and optimizat…

Incorporating sparsity priors in learning tasks can give rise to simple, and interpretable models for complex high dimensional data. Sparse models have found widespread use in structure discovery, recovering data from corruptions, and a variety of large scale unsupervised and supervised learning problems. Assuming the …

Using a Bayesian approach, we consider the problem of recovering sparse signals under additive sparse and dense noise. Typically, sparse noise models outliers, impulse bursts or data loss. To handle sparse noise, existing methods simultaneously estimate the sparse signal of interest and the sparse noise of no interest.…

A novel method for learning Bayesian network structures from decentralized data, balancing privacy and efficiency.

This letter proposes a sparse diffusion steepest-descent algorithm for one bit compressed sensing in wireless sensor networks. The approach exploits the diffusion strategy from distributed learning in the one bit compressed sensing framework. To estimate a common sparse vector cooperatively from only the sign of measur…

Telemonitoring of electroencephalogram (EEG) through wireless body-area networks is an evolving direction in personalized medicine. Among various constraints in designing such a system, three important constraints are energy consumption, data compression, and device cost. Conventional data compression methodologies, al…

In this thesis we discuss machine learning methods performing automated variable selection for learning sparse predictive models. There are multiple reasons for promoting sparsity in the predictive models. By relying on a limited set of input variables the models naturally counteract the overfitting problem ubiquitous …

High dimensional sparse learning has imposed a great computational challenge to large scale data analysis. In this paper, we are interested in a broad class of sparse learning approaches formulated as linear programs parametrized by a {\em regularization factor}, and solve them by the parametric simplex method (PSM). O…

A parallel algorithm learns efficient Kronecker product dictionaries.

The sizes of deep neural networks (DNNs) are rapidly outgrowing the capacity of hardware to store and train them. Research over the past few decades has explored the prospect of sparsifying DNNs before, during, and after training by pruning edges from the underlying topology. The resulting neural network is known as a …

Paper introduces MTGP for engineering tasks with sparse data.

A fast MCMC sampler for sparse Bayesian inference.

Recurrent neural networks (RNNs) are omnipresent in sequence modeling tasks. Practical models usually consist of several layers of hundreds or thousands of neurons which are fully connected. This places a heavy computational and memory burden on hardware, restricting adoption in practical low-cost and low-power devices…

Paper develops a new kernel expansion method using entropic optimal features for sparse and efficient kernel approximation.

Unified framework for sparse logistic regression with nonconvex regularization.

Integrates Fourier features for faster Gaussian process regression.

New method reduces computational cost of Gaussian process regression.

Optimizes sparse mean-reverting portfolios for higher returns.

New algorithms minimize regret in SSP with optimal sparse updates.

SmartDeal reduces energy and storage costs for deep neural networks.

Sparse GPs improved with nearest neighbor inducing variables.

Efficient algorithm reduces communication costs in sparse regression.

RO-TD learns sparse value functions efficiently.

The sizes of deep neural networks (DNNs) are rapidly outgrowing the capacity of hardware to store and train them. Research over the past few decades has explored the prospect of sparsifying DNNs before, during, and after training by pruning edges from the underlying topology. The resulting neural network is known as a …

New algorithm reduces high-dimensional data processing costs and achieves true sparsity.