arXiv research

Paper solves robust multi-dimensional scaling with accelerated projections.

Study on robust utility maximization with nonconcave utility functions under projective determinacy.

Paper establishes robust no-arbitrage conditions under projective determinacy.

New algorithm uses random projections for robust, sparse data classification.

Efficiently checks local robustness in neural networks using geometric projections.

While robust parameter estimation has been well studied in parametric density estimation, there has been little investigation into robust density estimation in the nonparametric setting. We present a robust version of the popular kernel density estimator (KDE). As with other estimators, a robust version of the KDE is u…

Paper presents efficient computation of robust Wasserstein distance using Riemannian optimization.

In this paper, we propose a new fast and robust recursive algorithm for near-separable nonnegative matrix factorization, a particular nonnegative blind source separation problem. This algorithm, which we refer to as the successive nonnegative projection algorithm (SNPA), is closely related to the popular successive pro…

RPE detects anomalies robustly in time-series data.

New method speeds up PGD for CV robustness evaluation.

A general duality proof for Wasserstein distributionally robust optimization.

This work improves understanding of projection robust optimal transport distances.

Paper extends multivariate rank tests for robust subspace detection.

This project improves model robustness to affine transformations.

Adversarial robustness in multi-index models is as easy as standard learning.

Accelerated optimization methods improve robustness and privacy in estimation.

Paper proposes PRWB and RPRWB for Wasserstein barycenters.

Paper improves SPA and its variants' robustness to noise.

FSPA bypasses eigenvalue estimation for quantum PCA, achieving optimal complexity and robustness.

The successive projection algorithm (SPA) is a fast algorithm to tackle separable nonnegative matrix factorization (NMF). Given a nonnegative data matrix $X$, SPA identifies an index set $\mathcal{K}$ such that there exists a nonnegative matrix $H$ with $X \approx X(:,\mathcal{K})H$. SPA has been successfully used as a…

Paper tackles robust optimal transport with improved computational complexity and barycenter approximation.

Bayesian approach to portfolio selection reduces pessimism in frequent trading.

PGD-trained models have a preferential direction in their gradients, which improves robustness.

New method for robustly interpreting ML models using quantile constraints and Wasserstein projections.

PDTS improves robustness in sequential decision-making.

The successive projection algorithm (SPA) has been known to work well for separable nonnegative matrix factorization (NMF) problems arising in applications, such as topic extraction from documents and endmember detection in hyperspectral images. One of the reasons is in that the algorithm is robust to noise. Gillis and…

Geometric technique determines exactness of SDP robustness certificate.

Paper quantifies distortion risk measures' robustness to distributional uncertainty.

Rare data in a large-scale database are called outliers that reveal significant information in the real world. The subspace-based outlier detection is regarded as a feasible approach in very high dimensional space. However, the outliers found in subspaces are only part of the true outliers in high dimensional space, in…

Develops PRPCA for smooth image recovery combining low-rank and smoothness.

In the past few years, Generative Adversarial Networks (GANs) have dramatically advanced our ability to represent and parameterize high-dimensional, non-linear image manifolds. As a result, they have been widely adopted across a variety of applications, ranging from challenging inverse problems like image completion, t…

s-OTDD compares datasets efficiently without training, robust to class variations.

Robust CG methods avoid data corruption and solve structured statistical estimation problems.

HD-BWDM improves clustering validation in high-dimensional data.

A new method solves the projection robust Wasserstein distance problem efficiently.

Motivated by vision tasks such as robust face and object recognition, we consider the following general problem: given a collection of low-dimensional linear subspaces in a high-dimensional ambient (image) space, and a query point (image), efficiently determine the nearest subspace to the query in $\ell^1$ distance. In…

Many machine learning systems are vulnerable to small perturbations made to inputs either at test time or at training time. This has received much recent interest on the empirical front due to applications where reliability and security are critical. However, theoretical understanding of algorithms that are robust to a…

We propose a fair principal component analysis method that balances reconstruction error and subgroup fairness.

This paper explores robust recovery of a superposition of $R$ distinct complex exponential functions from a few random Gaussian projections. We assume that the signal of interest is of $2N-1$ dimensional and $R<<2N-1$. This framework covers a large class of signals arising from real applications in biology, automation,…

SAP corrects model for label noise by identifying and removing noisy samples.

A variety of real-world tasks involve the classification of images into pre-determined categories. Designing image classification algorithms that exhibit robustness to acquisition noise and image distortions, particularly when the available training data are insufficient to learn accurate models, is a significant chall…

Study assesses CNN model robustness to noise in low-cost CT scans.

We address the problem of automatic generation of features for value function approximation. Bellman Error Basis Functions (BEBFs) have been shown to improve the error of policy evaluation with function approximation, with a convergence rate similar to that of value iteration. We propose a simple, fast and robust algor…

RieCUR improves Robust PCA by combining Riemannian optimization and CUR decompositions.

Efficiently learns distributions corrupted by both global and local adversarial modifications.

Concept Factorization (CF) and its variants may produce inaccurate representation and clustering results due to the sensitivity to noise, hard constraint on the reconstruction error and pre-obtained approximate similarities. To improve the representation ability, a novel unsupervised Robust Flexible Auto-weighted Local…

HiPPO framework optimizes memory compression for sequential data.

Paper analyzes robust matrix completion with efficient nonconvex method and leave-one-out analysis.