Scientific Program Cluster Details

Program -> Parallel Sessions -> Cluster List -> Details all clusters
Search talks included | sessions only


Please login, if you want to personalize your selection of clusters.

Cluster: Sparse optimization & compressed sensing

Monday


10:30 - 12:00, room: H 1028

Chair: Benjamin Recht
New models and algorithms in sparse optimization

Boumal Riemannian algorithms and estimation bounds for synchronization of rotations [...]
Davenport A simple framework for analog compressive sensing [...]
Recht Atomic norm denoising with applications to spectrum estimation and system identification [...]

 

13:15 - 14:45, room: H 1028

Chair: Gitta Kutyniok
Sparse optimization and generalized sparsity models

Saab Recovering compressively sampled signals using partial support information [...]
Candes PhaseLift: Exact phase retrieval via convex programming [...]
Kutyniok Clustered sparsity [...]

 

15:15 - 16:45, room: H 1028

Chair: Michel Baes
Global rate guarantees in sparse optimization

Yin Augmented L1 and nuclear-norm minimization with a globally linearly convergent algorithm [...]
Xiao A proximal-gradient homotopy method for the sparse least-squares problem [...]
Baes First-order methods for eigenvalue optimization [...]

 

 

Tuesday


10:30 - 12:00, room: H 1028

Chair: Mark Schmidt
Machine learning algorithms and implementations

Schmidt Linearly-convergent stochastic gradient methods [...]
Oh Statistical analysis of ranking from pairwise comparisons [...]

 

13:15 - 14:45, room: H 1028

Chair: Peter Richtarik
Coordinate descent methods for huge-scale optimization

Richtarik Parallel block coordinate descent methods for huge-scale partially separable problems [...]
Takac Distributed block coordinate descent method: Iteration complexity and efficient hybrid implementation [...]
Tappenden Block coordinate descent method for block-structured problems [...]

 

15:15 - 16:45, room: H 1028

Chair: Andreas Michael Tillmann
Algorithms for sparse optimization I

Tillmann Heuristic optimality check and computational solver comparison for basis pursuit [...]
Zikrin Sparse optimization techniques for solving multilinear least-squares problems with application to design of filter networks [...]
Demenkov Real-time linear inverse problem and control allocation in technical systems [...]

 

 

Wednesday


10:30 - 12:00, room: H 1028

Chair: Kimon Fountoulakis
Algorithms for sparse optimization II

Fountoulakis Matrix-free interior point method for compressed sensing problems [...]
Wang Linearized alternating direction methods for Dantzig selector [...]
Voronin Iteratively reweighted least squares methods for structured sparse regularization [...]

 

13:15 - 14:45, room: H 1028

Chair: Shiqian Ma
Efficient first-order methods for sparse optimization and its applications

Ma An alternating direction method for latent variable Gaussian graphical model selection [...]
Lu Sparse approximation via penalty decomposition methods [...]
Goldfarb An accelerated linearized Bregman method [...]

 

15:15 - 16:45, room: H 1028

Chair: John C. Duchi
Structured models in sparse optimization

Jenatton Proximal methods for hierarchical sparse coding and structured sparsity [...]
Pham Alternating linearization for structured regularization problems [...]
Duchi Adaptive subgradient methods for stochastic optimization and online learning [...]

 

 

Thursday


10:30 - 12:00, room: H 1028

Chair: Anatoli Juditsky
Computable bounds for sparse recovery

d'Aspremont High-dimensional geometry, sparse statistics and optimization [...]
Kilinc Karzan Verifiable sufficient conditions for l1-recovery of sparse signals [...]
Juditsky Accuracy guaranties and optimal l1-recovery of sparse signals [...]

 

13:15 - 14:45, room: H 1028

Chair: Wotao Yin
Nonconvex sparse optimization

Wen Alternating direction augmented Lagrangian methods for a few nonconvex problems [...]
Solombrino Linearly constrained nonsmooth and nonconvex minimization [...]
Lai On the Schatten p-quasi-norm minimization for low rank matrix recovery [...]

 

15:15 - 16:45, room: H 1028

Chair: Junfeng Yang
Variational signal processing -- algorithms and applications

Zhang On variational image decomposition model for blurred images with missing pixel values [...]
Yang Convergence of a class of stationary iterative methods for saddle point problems [...]

 

 

Friday


10:30 - 12:00, room: H 1028

Chair: Prateek Jain
Greedy algorithms for sparse optimization

Ravikumar Nearest neighbor based greedy coordinate descent [...]
Jain Orthogonal matching pursuit with replacement [...]

 

13:15 - 14:45, room: H 1028

Chair: Inderjit Dhillon
Structured matrix optimization

van den Berg Phase-retrieval using explicit low-rank matrix factorization [...]
Harchaoui Lifted coordinate descent for learning with Gauge regularization [...]
Dhillon Sparse inverse covariance matrix estimation using quadratic approximation [...]

 

 

Program -> Parallel Sessions -> Cluster List -> Details all clusters
Search talks included | sessions only
  There are three major facts that should be watched out for in all payday loans in the United States. Therefore, we can say that the active substances in its composition are more perfectly mixed. Vardenafil is not only present in the original Cheap Levitra, but also as part of its analogs.