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Found 8 papers, 1 papers with code
Nonconvex-Nonconcave Min-Max Optimization with a Small Maximization Domain
For $0 \le k \le 2$ the surrogate function can be efficiently maximized in $y$; our general approximation result then leads to efficient algorithms for finding a near-stationary point in nonconvex-nonconcave min-max problems, for which we also provide convergence guarantees.
A Decentralized Adaptive Momentum Method for Solving a Class of Min-Max Optimization Problems
Min-max saddle point games have recently been intensely studied, due to their wide range of applications, including training Generative Adversarial Networks (GANs).
Efficient Algorithms for Estimating the Parameters of Mixed Linear Regression Models
When the additive noise in MLR model is Gaussian, Expectation-Maximization (EM) algorithm is a widely-used algorithm for maximum likelihood estimation of MLR parameters.
Solving a class of non-convex min-max games using adaptive momentum methods
Adaptive momentum methods have recently attracted a lot of attention for training of deep neural networks.
Solving Non-Convex Non-Differentiable Min-Max Games using Proximal Gradient Method
Min-max saddle point games appear in a wide range of applications in machine leaning and signal processing.
Training generative networks using random discriminators
This design helps us to avoid the min-max formulation and leads to an optimization problem that is stable and could be solved efficiently.
A Method for Robust Online Classification using Dictionary Learning: Development and Assessment for Monitoring Manual Material Handling Activities Using Wearable Sensors
Classification methods based on sparse estimation have drawn much attention recently, due to their effectiveness in processing high-dimensional data such as images.
On the Behavior of the Expectation-Maximization Algorithm for Mixture Models
Our numerical experiments show that our algorithm outperforms the Naive EM algorithm in almost all scenarios.
