Zobrazeno 1 - 10
of 188
pro vyhledávání: '"MAURYA, DEEPAK"'
Autor:
Maurya, Deepak, Honorio, Jean
This paper analyzes $\ell_1$ regularized linear regression under the challenging scenario of having only adversarially corrupted data for training. We use the primal-dual witness paradigm to provide provable performance guarantees for the support of
Externí odkaz:
http://arxiv.org/abs/2212.11209
In this work, we propose a robust framework that employs adversarially robust training to safeguard the machine learning models against perturbed testing data. We achieve this by incorporating the worst-case additive adversarial error within a fixed
Externí odkaz:
http://arxiv.org/abs/2208.09449
Publikováno v:
In Industrial Crops & Products 15 September 2024 216
Publikováno v:
In Cell Reports 28 May 2024 43(5)
Autor:
Maurya, Deepak, Ravindran, Balaraman
Link prediction in graphs is studied by modeling the dyadic interactions among two nodes. The relationships can be more complex than simple dyadic interactions and could require the user to model super-dyadic associations among nodes. Such interactio
Externí odkaz:
http://arxiv.org/abs/2102.04986
Identification of autoregressive models with exogenous input (ARX) is a classical problem in system identification. This article considers the errors-in-variables (EIV) ARX model identification problem, where input measurements are also corrupted wit
Externí odkaz:
http://arxiv.org/abs/2011.14645
Autor:
Maurya, Deepak, Ravindran, Balaraman
Hypergraphs have gained increasing attention in the machine learning community lately due to their superiority over graphs in capturing super-dyadic interactions among entities. In this work, we propose a novel approach for the partitioning of k-unif
Externí odkaz:
http://arxiv.org/abs/2011.07683
The paper is concerned with identifying transfer functions of individual input channels in minimal realization form of a Multi-Input Single Output (MISO) from the input-output data corrupted by the error in all the variables. Such a framework is comm
Externí odkaz:
http://arxiv.org/abs/2008.05150
This article is concerned with the identification of autoregressive with exogenous inputs (ARX) models. Most of the existing approaches like prediction error minimization and state-space framework are widely accepted and utilized for the estimation o
Externí odkaz:
http://arxiv.org/abs/2008.04779
Model identification is a crucial problem in chemical industries. In recent years, there has been increasing interest in learning data-driven models utilizing partial knowledge about the system of interest. Most techniques for model identification do
Externí odkaz:
http://arxiv.org/abs/2007.04030