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.
Sparse GCA finds linear relationships in multiple datasets, using gradient descent.
Sparse model for noisy datasets using hierarchical regularization.
Proposes a multivariate regression model for better analysis of multiple datasets.
We study the problem of estimating high dimensional models with underlying sparse structures while preserving the privacy of each training example. We develop a differentially private high-dimensional sparse learning framework using the idea of knowledge transfer. More specifically, we propose to distill the knowledge …
This paper investigates the computational complexity of sparse label propagation which has been proposed recently for processing network structured data. Sparse label propagation amounts to a convex optimization problem and might be considered as an extension of basis pursuit from sparse vectors to network structured d…
Develops SGP-VAE for efficient sparse GP inference in multi-dimensional datasets.
Faster, better sparse model estimation for large datasets.
New method speeds up sparse Gaussian processes for large datasets.
Stochastic Sparse Subspace Clustering improves subspace clustering by reducing over-segmentation through dropout.
Optimal multitask learning method for sparse heterogeneous datasets.
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) …
New PCA method handles multiple datasets and detects sparse patterns robustly.
Proposes GCCA for detecting latent relations in multiview data with sparse structures.
Sparse Markovian Gaussian processes improve probabilistic model inference for large datasets.
A variety of machine learning tasks---e.g., matrix factorization, topic modelling, and feature allocation---can be viewed as learning the parameters of a probability distribution over bipartite graphs. Recently, a new class of models for networks, the sparse exchangeable graphs, have been introduced to resolve some imp…
We study high-dimensional sparse estimation tasks in a robust setting where a constant fraction of the dataset is adversarially corrupted. Specifically, we focus on the fundamental problems of robust sparse mean estimation and robust sparse PCA. We give the first practically viable robust estimators for these problems.…
Sparse Convex Biclustering improves accuracy and robustness in high-dimensional datasets.
Study improves model robustness in noisy datasets.
This paper proposes a new method for estimating sparse precision matrices in the high dimensional setting. It has been popular to study fast computation and adaptive procedures for this problem. We propose a novel approach, called Sparse Column-wise Inverse Operator, to address these two issues. We analyze an adaptive …
Gaussian Processes improve data interpolation from diverse experiments.
Chromatic Learning reduces feature dimensions for sparse datasets.
New Bayesian method for sparse multidimensional item response theory.
Data-aware methods for dimensionality reduction and matrix decomposition aim to find low-dimensional structure in a collection of data. Classical approaches discover such structure by learning a basis that can efficiently express the collection. Recently, "self expression", the idea of using a small subset of data vect…
New algorithm optimizes AUC for sparse high-dimensional data in online learning.
Improves inference from sparse data with hybrid summary statistics.
Sparse GEMINI selects relevant features for clustering without assumptions.
SJS model predicts label shifts in multinomial datasets.
We study parameter estimation in Nonlinear Factor Analysis (NFA) where the generative model is parameterized by a deep neural network. Recent work has focused on learning such models using inference (or recognition) networks; we identify a crucial problem when modeling large, sparse, high-dimensional datasets -- underf…
Graph Convolutional Networks (GCNs) have proven to be successful tools for semi-supervised learning on graph-based datasets. For sparse graphs, linear and polynomial filter functions have yielded impressive results. For large non-sparse graphs, however, network training and evaluation becomes prohibitively expensive. B…
A new method selects features efficiently for high-dimensional data.
msPCA solves sparse PCA for multiple components efficiently.
Paper connects neural network hyperparameter optimization and NAS to structured sparse recovery.
Proposes a Monte-Carlo method for sparse signal reconstruction.
We consider the scenario where one observes an outcome variable and sets of features from multiple assays, all measured on the same set of samples. One approach that has been proposed for dealing with this type of data is ``sparse multiple canonical correlation analysis'' (sparse mCCA). All of the current sparse mCCA t…
Paper tightens variational GP approximations for large datasets.
In semi-supervised learning for classification, it is assumed that every ground truth class of data is present in the small labelled dataset. Many real-world sparsely-labelled datasets are plausibly not of this type. It could easily be the case that some classes of data are found only in the unlabelled dataset -- perha…
Exact Gaussian Processes for massive datasets using non-stationary sparsity-discovering kernels.
Sparse Hopfield model improves memory retrieval with fewer connections.
Gaussian Processes improve missing value imputation in datasets.
This paper considers the sparse eigenvalue problem, which is to extract dominant (largest) sparse eigenvectors with at most non-zero components. We propose a simple yet effective solution called truncated power method that can approximately solve the underlying nonconvex optimization problem. A strong sparse recove…
Machine learning has played an important role in information retrieval (IR) in recent times. In search engines, for example, query keywords are accepted and documents are returned in order of relevance to the given query; this can be cast as a multi-label ranking problem in machine learning. Generally, the number of ca…
In this paper, we will investigate the efficacy of IMAT (Iterative Method of Adaptive Thresholding) in recovering the sparse signal (parameters) for linear models with missing data. Sparse recovery rises in compressed sensing and machine learning problems and has various applications necessitating viable reconstruction…
Gaussian processes (GPs) provide a probabilistic nonparametric representation of functions in regression, classification, and other problems. Unfortunately, exact learning with GPs is intractable for large datasets. A variety of approximate GP methods have been proposed that essentially map the large dataset into a sma…
Sparse coding--that is, modelling data vectors as sparse linear combinations of basis elements--is widely used in machine learning, neuroscience, signal processing, and statistics. This paper focuses on the large-scale matrix factorization problem that consists of learning the basis set, adapting it to specific data. V…
New scheme for sparse feature selection in networked data.
Develops method to train classifiers on incomplete feature datasets.
We present a transductive deep learning-based formulation for the sparse representation-based classification (SRC) method. The proposed network consists of a convolutional autoencoder along with a fully-connected layer. The role of the autoencoder network is to learn robust deep features for classification. On the othe…
New approach improves cross-domain recommendation for sparse target domains.