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Found 8 papers, 5 papers with code
Learning in Cooperative Multiagent Systems Using Cognitive and Machine Models
Developing effective Multi-Agent Systems (MAS) is critical for many applications requiring collaboration and coordination with humans.
SpeedyIBL: A Comprehensive, Precise, and Fast Implementation of Instance-Based Learning Theory
Instance-Based Learning Theory (IBLT) is a comprehensive account of how humans make decisions from experience during dynamic tasks.
Block Alternating Bregman Majorization Minimization with Extrapolation
In this paper, we consider a class of nonsmooth nonconvex optimization problems whose objective is the sum of a block relative smooth function and a proper and lower semicontinuous block separable function.
A Framework of Inertial Alternating Direction Method of Multipliers for Non-Convex Non-Smooth Optimization
In this paper, we propose an algorithmic framework, dubbed inertial alternating direction methods of multipliers (iADMM), for solving a class of nonconvex nonsmooth multiblock composite optimization problems with linear constraints.
An Inertial Block Majorization Minimization Framework for Nonsmooth Nonconvex Optimization
In this paper, we introduce TITAN, a novel inerTIal block majorizaTion minimizAtioN framework for non-smooth non-convex optimization problems.
Stochastic DCA for minimizing a large sum of DC functions with application to Multi-class Logistic Regression
We consider the large sum of DC (Difference of Convex) functions minimization problem which appear in several different areas, especially in stochastic optimization and machine learning.
A DCA-Like Algorithm and its Accelerated Version with Application in Data Visualization
In the first variant, DCA-Like, we introduce a new technique to iteratively modify the decomposition of the objective function.
Stochastic DCA for the Large-sum of Non-convex Functions Problem and its Application to Group Variable Selection in Classification
In this paper, we present a stochastic version of DCA (Difference of Convex functions Algorithm) to solve a class of optimization problems whose objective function is a large sum of non-convex functions and a regularization term.
