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Applied Mathematics

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Applied Mathematics

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Research Frontiers in Stochastic Optimization Algorithms

Theory and algorithms for optimization under uncertainty with applications to large-scale machine learning and statistical inference.

Variance Reduction in Non-Convex Landscape Navigation
Adaptive Sampling Strategies for High-Dimensional Optimization
Implicit Regularization in Stochastic Gradient Descent Dynamics
Federated Learning Under Heterogeneous Data Distributions
Zeroth-Order Optimization in Derivative-Free Regimes
Acceleration Phenomena in Asynchronous Distributed Algorithms
Stochastic Algorithms on Non-Euclidean Manifolds
Convergence Guarantees for Min-Max Game Optimization
Online Learning in Adversarial and Markovian Environments
Noise-Induced Exploration in Black-Box Optimization

All Applied Mathematics PhD categories