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MINIMAX OPTIMIZATION WITH SMOOTH ALGORITHMIC ADVERSARIES
Tanner Fiez
, Lillian J. Ratliff
,
Chi Jin
, Praneeth Netrapalli
Electrical and Computer Engineering
Princeton Language and Intelligence (PLI)
Research output
:
Contribution to conference
›
Paper
›
peer-review
2
Scopus citations
Overview
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Keyphrases
Adversary
100%
Computable
33%
Computational Budget
33%
Existing Algorithms
33%
Generative Adversarial Networks
33%
Limit Cycle
33%
Machine Learning Paradigms
33%
Minimax Optimization
100%
Nonconcave
100%
Nonconvex
66%
NP-hard
33%
Number of Iterations
33%
Optimization Problem
33%
Smoothing Algorithm
66%
Stationary Point
33%
Stochastic Gradient Descent
33%
Computer Science
Experimental Result
100%
Generative Adversarial Networks
100%
Machine Learning
100%
Optimization Problem
100%
Starting Point
100%
Stationary Point
100%
Theoretical Framework
100%
Engineering
Experimental Result
50%
Limit Cycle
50%
Max
100%
Maximization
100%
Optimality
50%
Optimisation Problem
50%
Starting Point
50%
Stationary Point
50%
Mathematics
Limit Cycle
33%
Minimax
100%
Optimality
33%
Polynomial
33%
Starting Point
33%
Stationary Point
33%
Stochastics
33%