Approximate Steepest Coordinate Descent

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Modern digital connectivity has necessitated the creation of robust methods for securely storing and transferring data. At the heart of all security infrastructure is the random number generator (RNG). While random numbers find use in a variety of applicat ...

We study generalization properties of random features (RF) regression in high dimensions optimized by stochastic gradient descent (SGD) in under-/overparameterized regime. In this work, we derive precise non-asymptotic error bounds of RF regression under b ...

Digitization and preservation of large heritage induce high maintenance costs to keep up with the technical standards and ensure sustainable access. Creating impactful usage is instrumental to justify the resources for long-term preservation. The Museum fü ...

The standard practice in Generative Adversarial Networks (GANs) discards the discriminator during sampling. However, this sampling method loses valuable information learned by the discriminator regarding the data distribution. In this work, we propose a co ...

The classical multivariate extreme-value theory concerns the modeling of extremes in a multivariate random sample, suggesting the use of max-stable distributions. In this work, the classical theory is extended to the case where aggregated data, such as max ...

Weighted least squares polynomial approximation uses random samples to determine projections of functions onto spaces of polynomials. It has been shown that, using an optimal distribution of sample locations, the number of samples required to achieve quasi ...

In the last decade, Compressive Sensing (CS) has emerged as the most promising, model-driven approach to accelerate MRI scans. CS relies on the key sparsity assumption and proposes random sampling for data acquisition. The practical CS approaches in MRI em ...

In this paper we present proofs for the new biases in RC4 which were experimentally found and listed out (without theoretical justifi- cations and proofs) in a paper by Vanhoef et al. in USENIX 2015. Their purpose was to exploit the vulnerabilities of RC4 ...

In this paper we present proofs for the new biases in RC4 which were experimentally found and listed out (without theoretical justifications and proofs) in a paper by Vanhoef et al. in USENIX 2015. Their purpose was to exploit the vulnerabilities of RC4 in ...

Background: Since goat was domesticated 10,000 years ago, many factors have contributed to the differentiation of goat breeds and these are classified mainly into two types: (i) adaptation to different breeding systems and/or purposes and (ii) adaptation t ...

We consider the problem of testing graph cluster structure: given access to a graph G = (V, E), can we quickly determine whether the graph can be partitioned into a few clusters with good inner conductance, or is far from any such graph? This is a generali ...

The present work addresses the question how sampling algorithms for commonly applied copula models can be adapted to account for quasi-random numbers. Besides sampling methods such as the conditional distribution method (based on a one-to-one transformatio ...

Coordinate descent methods employ random partial updates of decision variables in order to solve huge-scale convex optimization problems. In this work, we introduce new adaptive rules for the random selection of their updates. By adaptive, we mean that our ...

The study of seeded randomness extractors is a major line of research in theoretical computer science. The goal is to construct deterministic algorithms which can take a weak random source x with min-entropy k and a uniformly random seed Y of length d, and ...

Although stochastic programming problems were always believed to be computationally challenging, this perception has only recently received a theoretical justification by the seminal work of Dyer and Stougie (Mathematical Programming A, 106(3):423–432, 200 ...

We perform a general optimization of the parameters in the multilevel Monte Carlo (MLMC) discretization hierarchy based on uniform discretization methods with general approximation orders and computational costs. We optimize hierarchies with geometric and ...

Spectral clustering has become a popular technique due to its high performance in many contexts. It comprises three main steps: create a similarity graph between N objects to cluster, compute the first k eigenvectors of its Laplacian matrix to define a fea ...

In this work we discuss the dynamically orthogonal (DO) approximation of time dependent partial differential equations with random data. The approximate solution is expanded at each time instant on a time dependent orthonormal basis in the physical domain ...

In this work we consider quasi-optimal versions of the Stochastic Galerkin method for solving linear elliptic PDEs with stochastic coefficients. In particular, we consider the case of a finite number

N

of random inputs and an analytic dependence of the s ...

The efficiency of the simplest isomeric search procedure consisting in random generation of sets of atomic coordinates followed by density functional theory geometry optimization is tested on the silicon cluster series (Si-5-10,Si-15,Si-20). Criteria such ...

Approximate Steepest Coordinate Descent

Chattez avec Graph Search

Modelling and design of CMOS SPAD sensors for quantum random number generation

On the Double Descent of Random Features Models Trained with SGD

Automatic Content Curation of Visual Heritage

Collaborative Sampling in Generative Adversarial Networks

Multivariate extremes over a random number of observations

Multilevel weighted least squares polynomial approximation

Rethinking Sampling in Parallel MRI: A Data-Driven Approach

Some Proofs of Conditional and Joint Biases in RC4 Stream Cipher

Theoretical Understanding of Some Conditional and Joint Biases in RC4 Stream Cipher

Signatures of selection and environmental adaptation across the goat genome post-domestication

Testing Graph Clusterability: Algorithms and Lower Bounds

Quasi-random numbers for copula models

Faster Coordinate Descent via Adaptive Importance Sampling

Affine-malleable Extractors, Spectrum Doubling, and Application to Privacy Amplification

A Comment on “Computational Complexity of Stochastic Programming Problems”

Optimization of mesh hierarchies in multilevel Monte Carlo samplers

Compressive Spectral Clustering

Error Analysis of the Dynamically Orthogonal Approximation of Time Dependent Random PDEs

Convergence of quasi-optimal Stochastic Galerkin methods for a class of PDES with random coefficients

Efficiency of random search procedures along the silicon cluster series Sin (n=5-10, 15 and 20)