Convex Optimization
864 papers
Papers per year
1
1
11
17
10
16
23
22
35
59
54
39
34
27
35
48
63
52
74
51
88
83
21
'5
'10
'15
'20
'25
Papers
Coevolutionary Latent Feature Processes for Continuous-Time User-Item Interactions
NeurIPS 2016
CVXPY: A Python-Embedded Modeling Language for Convex Optimization
JMLR 2016
Designing smoothing functions for improved worst-case competitive ratio in online optimization
NeurIPS 2016
Epigraph projections for fast general convex programming
ICML 2016
Estimation from Indirect Supervision with Linear Moments
ICML 2016
Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling
ICML 2016
Fast Algorithms for Linear and Kernel SVM+
CVPR 2016
Faster Convex Optimization: Simulated Annealing with an Efficient Universal Barrier
ICML 2016
Finite Sample Prediction and Recovery Bounds for Ordinal Embedding
NeurIPS 2016
Iterative Hessian Sketch: Fast and Accurate Solution Approximation for Constrained Least-Squares
JMLR 2016
Iterative Regularization for Learning with Convex Loss Functions
JMLR 2016
Large-Scale Semantic 3D Reconstruction: An Adaptive Multi-Resolution Model for Multi-Class Volumetric Labeling
CVPR 2016
MOCCA: Mirrored Convex/Concave Optimization for Nonconvex Composite Functions
JMLR 2016
On Lower and Upper Bounds in Smooth and Strongly Convex Optimization
JMLR 2016
On the Statistical Limits of Convex Relaxations
ICML 2016
Optimistic Bandit Convex Optimization
NeurIPS 2016
Primary Object Segmentation in Videos via Alternate Convex Optimization of Foreground and Background Distributions
CVPR 2016
SDP Relaxation with Randomized Rounding for Energy Disaggregation
NeurIPS 2016
Stochastic Three-Composite Convex Minimization
NeurIPS 2016
Stochastic Variance Reduction Methods for Saddle-Point Problems
NeurIPS 2016
Structure-Blind Signal Recovery
NeurIPS 2016
Sub-sampled Newton Methods with Non-uniform Sampling
NeurIPS 2016
Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization
CVPR 2016
The non-convex Burer-Monteiro approach works on smooth semidefinite programs
NeurIPS 2016
Tight Complexity Bounds for Optimizing Composite Objectives
NeurIPS 2016