Faster Stochastic Variance Reduction Methods for Compositional MiniMax Optimization
This paper delves into the realm of stochastic optimization for compositional minimax
optimization—a pivotal challenge across various machine learning domains, including deep …
optimization—a pivotal challenge across various machine learning domains, including deep …
Stochastic Compositional Minimax Optimization with Provable Convergence Guarantees
Stochastic compositional minimax problems are prevalent in machine learning, yet there are
only limited established on the convergence of this class of problems. In this paper, we …
only limited established on the convergence of this class of problems. In this paper, we …
Stochastic methods for auc optimization subject to auc-based fairness constraints
As machine learning being used increasingly in making high-stakes decisions, an arising
challenge is to avoid unfair AI systems that lead to discriminatory decisions for protected …
challenge is to avoid unfair AI systems that lead to discriminatory decisions for protected …
Breaking the Complexity Barrier in Compositional Minimax Optimization
Compositional minimax optimization is a pivotal yet under-explored challenge across
machine learning, including distributionally robust training and policy evaluation for …
machine learning, including distributionally robust training and policy evaluation for …
Optimization Approaches for Fairness-Aware Machine Learning
Y Yao - 2024 - search.proquest.com
In recent years, artificial intelligence (AI) and machine learning (ML) technologies have been
used in high-stakes decision making systems like lending decision, employment screening …
used in high-stakes decision making systems like lending decision, employment screening …