In this note, we introduce a new algorithm to deal with finite dimensional clustering with errors in variables. The design of this algorithm is based on recent theoretical advances (see Loustau (2013a,b)) in statistical learning with errors in variables. As the previous mentioned papers, the algorithm mixes different tools from the inverse problem literature and the machine learning community. ...

Most incomplete DCOP algorithms generally do not provide any guarantees on the quality of the solutions. In this paper, we introduce a new incomplete DCOP algorithm that can provide the upper bounds of the absolute/relative errors of the solution, which can be obtained a priori/a posteriori, respectively. The evaluation results illustrate that this algorithm can obtain better quality solutions ...

DRAFT Last class, we saw a toy version of recovering from a mixture of two Reed-Solomon codewords the two polynomials in question. Now we turn to list decoding arbitrary received words with a bounded distance from the Reed-Solomon code using Sudan’s [6] algorithm. This algorithm decodes close to a fraction 1 of errors for low rates. Then we will see an improvement by Guruswami and Sudan [4] whi...

In a previous paper [7] we have identified a special class of linear hybrid automata, called Deterministic Transversal Linear Hybrid Automata, and shown that an ǫ-reach set up to a finite time, called a bounded ǫ-reach set, can be computed using infinite precision calculations. However, given the linearity of the system and the consequent presence of matrix exponentials, numerical errors are in...

this paper presents a probabilistic optimal power flow (popf) algorithm considering different uncertainties in a smart grid. different uncertainties such as variation of nodal load, change in system configuration, measuring errors, forecasting errors, and etc. can be considered in the proposed algorithm. by increasing the penetration of the renewable energies in power systems, it is more essent...

We give new methods for generating and using “strong trapdoors” in cryptographic lattices, which are simultaneously simple, efficient, easy to implement (even in parallel), and asymptotically optimal with very small hidden constants. Our methods involve a new kind of trapdoor, and include specialized algorithms for inverting LWE, randomly sampling SIS preimages, and securely delegating trapdoor...

Given a lattice L with the i-th successive minimum λi, its i-th gap λi λ1 often provides useful information for analyzing the security of cryptographic scheme related to L. This paper concerns short vectors for lattices with gaps. In the first part, a λ2-gap estimation of LWE lattices with cryptographic significance is given. For some γ′, a better reduction from BDDγ′ to uSV Pγ is obtained in t...

Decoding random linear codes is a well studied problem with many applications in complexity theory and cryptography. The security of almost all coding and LPN/LWE-based schemes relies on the assumption that it is hard to decode random linear codes. Recently, there has been progress in improving the running time of the best decoding algorithms for binary random codes. The ball collision techniqu...

In this paper, we investigate accelerated first-order methods for smooth convex optimization problems under inexact information on the gradient of objective. The noise in is considered to be additive with two possibilities: absolute bounded by a constant, and relative proportional norm gradient. We accumulation errors strongly settings main difference most previous works being that feasible set...

We study online active learning for classifying streaming instances within the framework of statistical theory. At each time, learner either queries label current instance or predicts based on past seen examples. The objective is to minimize number while constraining prediction errors over a horizon length $T$. develop disag...