arXiv research

The paper proposes a novel MKL approach for OCC using $\ell_p$-norm constraints.

ScoreAG generates unrestricted adversarial images maintaining semantic integrity.

The paper proposes a new method for dictionary learning using $\ell_p$-norm maximization.

A statistical test detects adversarial examples with minimal input corruption.

In this paper, we propose $\ell_p$-norm regularized models to seek near-optimal sparse portfolios. These sparse solutions reduce the complexity of portfolio implementation and management. Theoretical results are established to guarantee the sparsity of the second-order KKT points of the $\ell_p$-norm regularized models…

A new algorithm solves the metric nearness problem efficiently.

A novel one-class classifier fusion method for robust anomaly detection.

Study improves image classifier robustness to random p-norm corruptions.

Unified algorithm for any $p$-norm experimental design problems.

In this paper, we study global existence and blow up properties to $L^p$ norm preserving non-local heat flows. We first study two kinds of $L^p$ norm preserving non-local flows and prove that these flows have the global solutions. Finally, we give a example to show that one kind of this heat flow may blow up in $L^{\in…

Study shows various pixel p-norm measures do not match human perception of adversarial attacks.

The paper refines and generalizes worst-case law invariant convex risk measures.

The Group-Lasso is a well-known tool for joint regularization in machine learning methods. While the l_{1,2} and the l_{1,\infty} version have been studied in detail and efficient algorithms exist, there are still open questions regarding other l_{1,p} variants. We characterize conditions for solutions of the l_{1,p} G…

This paper tackles robustness of ensemble stumps and trees under general ℓ_p norm perturbations.

Proposes a new regression method using $L_p$-norms for non-Gaussian noise.

This work presents a general framework for solving the low rank and/or sparse matrix minimization problems, which may involve multiple non-smooth terms. The Iteratively Reweighted Least Squares (IRLS) method is a fast solver, which smooths the objective function and minimizes it by alternately updating the variables an…

Improved algorithms solve $\ell_p$-norm regression problems efficiently.

The paper analyzes the performance of empirical risk minimization for $p$-norm linear regression.

Paper addresses concentration of distances for fractional quasi p-norms, identifying conditions for concentration and anti-concentration.

Machine-learning models for security-critical applications such as bot, malware, or spam detection, operate in constrained discrete domains. These applications would benefit from having provable guarantees against adversarial examples. The existing literature on provable adversarial robustness of models, however, exclu…

A new method for feature selection in high-dimensional data.

Semantify-NN verifies neural network robustness against semantic perturbations.

Max-convolution is an important problem closely resembling standard convolution; as such, max-convolution occurs frequently across many fields. Here we extend the method with fastest known worst-case runtime, which can be applied to nonnegative vectors by numerically approximating the Chebyshev norm $\| \cdot \|_\infty…

General lower bounds on neural network approximation in L^p norm.

CPML efficiently learns new metrics for categorical data.

The Schatten-$p$ norm ($0<p<1$) has been widely used to replace the nuclear norm for better approximating the rank function. However, existing methods are either 1) not scalable for large scale problems due to relying on singular value decomposition (SVD) in every iteration, or 2) specific to some $p$ values, e.g., $1/…

Improved estimation of concentration using half-spaces for adversarial vulnerability.

A new PCA method using T$\ell_1$-norm outperforms existing methods.

A new fast adversarial attack finds minimal perturbations to change class.

We investigate stability and local minimizing properties of the Riemannian functional defined by the L^p norm of the curvature tensor on the space of Riemannian metrics on a closed manifold. Riemannian metrics with constant curvature and products of such metrics are critical points of this functional. We prove that the…

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

We study stability and local minimizing properties of $L^p$- norms of Riemannian curvature tensor denoted by $\mathcal{R}_p$ by variational methods. We compute the Hessian of $\mathcal{R}_p$ at compact rank 1 symmetric spaces and prove that they are stable for $\mathcal{R}_p$ for certain values of p > 2. A similar resu…

Simple regional perturbations maintain model transferability while reducing adversarial example distortion.

Graph-based framework for provably robust adversarial training.

Example surfaces with curvature bounds but no Laplacian eigenvalue lower bound.

Given a sequence of complete Riemannian manifolds $(M_n)$ of the same dimension, we construct a complete Riemannian manifold $M$ such that for all $p \in (1,\infty)$ the $L^p$-norm of the Riesz transform on $M$ dominates the $L^p$-norm of the Riesz transform on $M_n$ for all $n$. Thus we establish the following dichoto…

We extend a result of the second author \cite[Theorem 1.1]{soggekaknik} to dimensions $d \geq 3$ which relates the size of $L^p$-norms of eigenfunctions for $2<p<\frac{2(d+1)}{d-1}$ to the amount of $L^2$-mass in shrinking tubes about unit-length geodesics. The proof uses bilinear oscillatory integral estimates of Lee …

Active sampling algorithm for linear regression with various norms and improved query complexity.

New nonconvex regularizers improve low-rank matrix recovery efficiency and accuracy.

We apply the integral formula of volumes to the family of graded linear series constructed from any test configuration. This solves the conjecture raised by Witt--Nyström so that the sequence of spectral measures for the induced $\mathbb{C}^*$-action on the central fiber converges to the canonical Duistermatt--Heckman …

In this paper we prove several results on the geometry of surfaces immersed in $\mathbf R^3$ with small or bounded $L^2$ norm of $|A|$. For instance, we prove that if the $L^2$ norm of $|A|$ and the $L^p$ norm of $H$, $p>2$, are sufficiently small, then such a surface is graphical away from its boundary. We also prove …

We propose practical algorithms for entrywise $\ell_p$-norm low-rank approximation, for $p = 1$ or $p = \infty$. The proposed framework, which is non-convex and gradient-based, is easy to implement and typically attains better approximations, faster, than state of the art. From a theoretical standpoint, we show that th…

We provide a necessary and sufficient condition that $L^p$-norms, $2<p<6$, of eigenfunctions of the square root of minus the Laplacian on 2-dimensional compact boundaryless Riemannian manifolds $M$ are small compared to a natural power of the eigenvalue $λ$. The condition that ensures this is that their $L^2$ norms ove…

Analyzes the complexity of linear hypothesis sets using Rademacher complexity.

The study of adversarial robustness has so far largely focused on perturbations bound in p-norms. However, state-of-the-art models turn out to be also vulnerable to other, more natural classes of perturbations such as translations and rotations. In this work, we thoroughly investigate the vulnerability of neural networ…

New framework for private convex optimization in arbitrary norms.

New method makes neural networks robust to various adversarial attacks.

We introduce a new framework for optimal transport using Schatten-p regularization to recover low-rank structures.