arXiv research

New algorithm recovers model coefficients and supports from noisy data.

FROCC uses random projections for fast one-class classification.

Efficient algorithms solve large-scale DRSVM problems.

Using sparse-inducing norms to learn robust models has received increasing attention from many fields for its attractive properties. Projection-based methods have been widely applied to learning tasks constrained by such norms. As a key building block of these methods, an efficient operator for Euclidean projection ont…

Paper solves robust multi-dimensional scaling with accelerated projections.

Paper proposes a new method for efficient Pareto Front modeling.

Study optimizes portfolio to minimize relative drawdown duration, penalizing unfavorable performance states.

New algorithms improve machine learning performance with explicit regret bounds.

A new faster neural network training method using backprojection.

We present a structural clustering algorithm for large-scale datasets of small labeled graphs, utilizing a frequent subgraph sampling strategy. A set of representatives provides an intuitive description of each cluster, supports the clustering process, and helps to interpret the clustering results. The projection-based…

A faster Wasserstein k-means algorithm for histogram data reduces computation and maintains clustering quality.

Inferring the latent variable generating a given test sample is a challenging problem in Generative Adversarial Networks (GANs). In this paper, we propose InvGAN - a novel framework for solving the inference problem in GANs, which involves training an encoder network capable of inverting a pre-trained generator network…

Adapts POD basis for parametric ROMs using pGP.

We propose a randomized second-order method for optimization known as the Newton Sketch: it is based on performing an approximate Newton step using a randomly projected or sub-sampled Hessian. For self-concordant functions, we prove that the algorithm has super-linear convergence with exponentially high probability, wi…

Study efficient algorithms for nonconvex optimization with state-dependent Markov data.

New ARIMA framework improves forecast accuracy for economic and financial time series.

We develop necessary and sufficient conditions and a novel provably consistent and efficient algorithm for discovering topics (latent factors) from observations (documents) that are realized from a probabilistic mixture of shared latent factors that have certain properties. Our focus is on the class of topic models in …

New algorithm reduces online learning iterations by a factor of T^2/3.

In this paper, we study a family of non-convex and possibly non-smooth inf-projection minimization problems, where the target objective function is equal to minimization of a joint function over another variable. This problem include difference of convex (DC) functions and a family of bi-convex functions as special cas…

First private Bayesian optimization algorithm with provable performance.

Conditional gradients constitute a class of projection-free first-order algorithms for smooth convex optimization. As such, they are frequently used in solving smooth convex optimization problems over polytopes, for which the computational cost of orthogonal projections would be prohibitive. However, they do not enjoy …

New methods combine low and high-fidelity data for accurate surrogate modeling.

The paper introduces a new method for detecting financial data outliers.

PF-LaCG removes the need for knowing smoothness and strong convexity parameters for locally accelerated CG.

RPE detects anomalies robustly in time-series data.

Improved COCO algorithms with better constraint control.

Paper studies randomized spectral clustering for large-scale networks.

Linear regression is arguably the most prominent among statistical inference methods, popular both for its simplicity as well as its broad applicability. On par with data-intensive applications, the sheer size of linear regression problems creates an ever growing demand for quick and cost efficient solvers. Fortunately…

Randomized spectral co-clustering speeds up large-scale directed networks.

This study provides an independent, outside-in estimate of the cost and schedule risks of nuclear waste storage projects. Based on a reference class of 216 past, comparable projects, risk of cost overrun was found to be 202% or less, with 80% certainty, i.e., 20% risk of an overrun above 202%. Based on a reference clas…

Mathematical method based on a direct or indirect analysis of growth rates is described. It is shown how simple assumptions and a relatively easy analysis can be used to describe mathematically complicated trends and to predict growth. Only rudimentary knowledge of calculus is required. Projected trajectories based on …

A new method solves variational inequality problems with multiple constraints without needing optimal Lagrange multipliers.

Measuring conditional dependence is an important topic in statistics with broad applications including graphical models. Under a factor model setting, a new conditional dependence measure based on projection is proposed. The corresponding conditional independence test is developed with the asymptotic null distribution …

This work improves SINDy-type algorithms for system identification using score-guided dictionary selection.

Paper examines convergence rate of PGD for BP objective in inverse problems.

A new method slices and sums radial kernels faster.

We present the first sublinear memory sketch that can be queried to find the nearest neighbors in a dataset. Our online sketching algorithm compresses an N element dataset to a sketch of size $O(N^b \log^3 N)$ in $O(N^{(b+1)} \log^3 N)$ time, where $b < 1$. This sketch can correctly report the nearest neighbors of any …

New projection techniques reduce the frequency of projections in solving LCPs.

A new method for generating time-dependent densities efficiently.

The successive projection algorithm (SPA) can quickly solve a nonnegative matrix factorization problem under a separability assumption. Even if noise is added to the problem, SPA is robust as long as the perturbations caused by the noise are small. In particular, robustness against noise should be high when handling th…

This article presents results from the first statistically significant study of cost escalation in transportation infrastructure projects. Based on a sample of 258 transportation infrastructure projects worth US$90 billion and representing different project types, geographical regions, and historical periods, it is fou…

Review of modern computational optimal transport methods for biomedical applications.

In this paper, we propose the application of conditional generative adversarial networks to solve various phase retrieval problems. We show that including knowledge of the measurement process at training time leads to an optimization at test time that is more robust to initialization than existing approaches involving …

Over the past few decades, we have witnessed a large family of algorithms that have been designed to provide different solutions to the problem of dimensionality reduction (DR). The DR is an essential tool to excavate the important information from the high-dimensional data by mapping the data to a low-dimensional subs…

Improved tensor GLM estimation for complex data.

Consider a smooth, projective family of canonically polarized varieties over a smooth, quasi-projective base manifold Y, all defined over the complex numbers. It has been conjectured that the family is necessarily isotrivial if Y is special in the sense of Campana. We prove the conjecture when Y is a surface or threefo…

We propose a novel, projection based way to incorporate the conditional information into the discriminator of GANs that respects the role of the conditional information in the underlining probabilistic model. This approach is in contrast with most frameworks of conditional GANs used in application today, which use the …

Flora uses random projections to achieve high-rank updates with low memory usage.