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Found 8 papers, 4 papers with code
Stable Nonconvex-Nonconcave Training via Linear Interpolation
This paper presents a theoretical analysis of linear interpolation as a principled method for stabilizing (large-scale) neural network training.
Federated Learning under Covariate Shifts with Generalization Guarantees
This paper addresses intra-client and inter-client covariate shifts in federated learning (FL) with a focus on the overall generalization performance.
Escaping limit cycles: Global convergence for constrained nonconvex-nonconcave minimax problems
This paper introduces a new extragradient-type algorithm for a class of nonconvex-nonconcave minimax problems.
Solving stochastic weak Minty variational inequalities without increasing batch size
This paper introduces a family of stochastic extragradient-type algorithms for a class of nonconvex-nonconcave problems characterized by the weak Minty variational inequality (MVI).
Revisiting adversarial training for the worst-performing class
Despite progress in adversarial training (AT), there is a substantial gap between the top-performing and worst-performing classes in many datasets.
Sifting through the noise: Universal first-order methods for stochastic variational inequalities
Our first result is that the algorithm achieves the optimal rates of convergence for cocoercive problems when the profile of the randomness is known to the optimizer: $\mathcal{O}(1/\sqrt{T})$ for absolute noise profiles, and $\mathcal{O}(1/T)$ for relative ones.
Subquadratic Overparameterization for Shallow Neural Networks
Overparameterization refers to the important phenomenon where the width of a neural network is chosen such that learning algorithms can provably attain zero loss in nonconvex training.
Protect the weak: Class focused online learning for adversarial training
In this work, we identify that the focus on the average accuracy metric can create vulnerabilities to the "weakest" class.
