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Found 11 papers, 1 papers with code
Accelerated stochastic approximation with state-dependent noise
However, to the best of our knowledge, none of the existing stochastic approximation algorithms for solving this class of problems attain optimality in terms of the dependence on accuracy, problem parameters, and mini-batch size.
Generalized generalized linear models: Convex estimation and online bounds
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.
Stochastic Mirror Descent for Large-Scale Sparse Recovery
In this paper we discuss an application of Stochastic Approximation to statistical estimation of high-dimensional sparse parameters.
On Well-Structured Convex-Concave Saddle Point Problems and Variational Inequalities with Monotone Operators
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
Sparse recovery by reduced variance stochastic approximation
In this paper, we discuss application of iterative Stochastic Optimization routines to the problem of sparse signal recovery from noisy observation.
Convex Parameter Recovery for Interacting Marked Processes
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.
Unifying mirror descent and dual averaging
We introduce and analyze a new family of first-order optimization algorithms which generalizes and unifies both mirror descent and dual averaging.
Adaptive Denoising of Signals with Local Shift-Invariant Structure
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.
Structure-Blind Signal Recovery
We consider the problem of recovering a signal observed in Gaussian noise.
Statistics Theory Statistics Theory
Conditional Gradient Algorithms for Norm-Regularized Smooth Convex Optimization
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.
On the accuracy of l1-filtering of signals with block-sparse structure
We discuss new methods for the recovery of signals with block-sparse structure, based on l1-minimization.
