no code implementations • 26 Apr 2023 • Anatoli Juditsky, Arkadi Nemirovski, Yao Xie, Chen Xu

We introduce a new computational framework for estimating parameters in generalized generalized linear models (GGLM), a class of models that extends the popular generalized linear models (GLM) to account for dependencies among observations in spatio-temporal data.

no code implementations • 1 Feb 2021 • Anatoli Juditsky, Arkadi Nemirovski

We demonstrate that given such a representation of the problem of interest, the latter can be reduced straightforwardly to a conic problem on a cone from K and thus can be solved by (any) solver capable to handle conic problems on cones from K (e. g., Mosek or SDPT3 in the case of semidefinite cones).

Optimization and Control 90C22, 90C25, 90C33

no code implementations • 29 Mar 2020 • Anatoli Juditsky, Arkadi Nemirovski, Liyan Xie, Yao Xie

In the proposed model, the probability of an event of a specific category to occur in a location may be influenced by past events at this and other locations.

no code implementations • 11 Jun 2018 • Zaid Harchaoui, Anatoli Juditsky, Arkadi Nemirovski, Dmitrii Ostrovskii

We discuss the problem of adaptive discrete-time signal denoising in the situation where the signal to be recovered admits a "linear oracle" -- an unknown linear estimate that takes the form of convolution of observations with a time-invariant filter.

1 code implementation • NeurIPS 2016 • Dmitry Ostrovsky, Zaid Harchaoui, Anatoli Juditsky, Arkadi Nemirovski

We consider the problem of recovering a signal observed in Gaussian noise.

Statistics Theory Statistics Theory

no code implementations • 10 Feb 2013 • Zaid Harchaoui, Anatoli Juditsky, Arkadi Nemirovski

Motivated by some applications in signal processing and machine learning, we consider two convex optimization problems where, given a cone $K$, a norm $\|\cdot\|$ and a smooth convex function $f$, we want either 1) to minimize the norm over the intersection of the cone and a level set of $f$, or 2) to minimize over the cone the sum of $f$ and a multiple of the norm.

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