2 citations · 7 across the 7 of their papers we have counts for
7 papers
Nonasymptotic Analysis of Accelerated Methods With Inexact Oracle Under Absolute Error Bound
Yin Liu, Sam Davanloo Tajbakhsh
Performance analysis of first-order algorithms with inexact oracles has gained recent attention due to various emerging applications in which obtaining exact gradients is impossibl…
Stochastic Optimization Algorithms for Problems with Controllable Biased Oracles
Yin Liu, Sam Davanloo Tajbakhsh
Motivated by emerging applications in machine learning, we consider an optimization problem in a general setting in which the gradient of the objective function is available via a…
Riemannian Stochastic Gradient Method for Nested Composition Optimization
Dewei Zhang, Sam Davanloo Tajbakhsh
This work considers optimization of composition of functions in a nested form over Riemannian manifolds where each function contains an expectation. This type of problems is gainin…
Fitting ARMA Time Series Models without Identification: A Proximal Approach
Yin Liu, Sam Davanloo Tajbakhsh
Fitting autoregressive moving average (ARMA) time series models requires model identification before parameter estimation. Model identification involves determining the order of th…
A first-order optimization algorithm for statistical learning with hierarchical sparsity structure
Dewei Zhang, Yin Liu, Sam Davanloo Tajbakhsh
In many statistical learning problems, it is desired that the optimal solution conforms to an a priori known sparsity structure represented by a directed acyclic graph. Inducing su…
Generalized Sparse Precision Matrix Selection for Fitting Multivariate Gaussian Random Fields to Large Data Sets
Sam Davanloo Tajbakhsh, Necdet Serhat Aybat, Enrique del Castillo
We present a new method for estimating multivariate, second-order stationary Gaussian Random Field (GRF) models based on the Sparse Precision matrix Selection (SPS) algorithm, prop…
On the Theoretical Guarantees for Parameter Estimation of Gaussian Random Field Models: A Sparse Precision Matrix Approach
Sam Davanloo Tajbakhsh, Necdet Serhat Aybat, Enrique Del Castillo
Iterative methods for fitting a Gaussian Random Field (GRF) model via maximum likelihood (ML) estimation requires solving a nonconvex optimization problem. The problem is aggravate…