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Found 9 papers, 0 papers with code
Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization
In recent years, federated minimax optimization has attracted growing interest due to its extensive applications in various machine learning tasks.
Achieving Linear Speedup in Non-IID Federated Bilevel Learning
However, several important properties in federated learning such as the partial client participation and the linear speedup for convergence (i. e., the convergence rate and complexity are improved linearly with respect to the number of sampled clients) in the presence of non-i. i. d.~datasets, still remain open.
Decentralized Stochastic Bilevel Optimization with Improved per-Iteration Complexity
Bilevel optimization recently has received tremendous attention due to its great success in solving important machine learning problems like meta learning, reinforcement learning, and hyperparameter optimization.
Efficiently Escaping Saddle Points in Bilevel Optimization
Moreover, we propose an inexact NEgative-curvature-Originated-from-Noise Algorithm (iNEON), a pure first-order algorithm that can escape saddle point and find local minimum of stochastic bilevel optimization.
On the Convergence of Projected Alternating Maximization for Equitable and Optimal Transport
This paper studies the equitable and optimal transport (EOT) problem, which has many applications such as fair division problems and optimal transport with multiple agents etc.
Projection Robust Wasserstein Barycenters
One of the popular solution methods for this task is to compute the barycenter of the probability measures under the Wasserstein metric.
Escaping Saddle Points for Nonsmooth Weakly Convex Functions via Perturbed Proximal Algorithms
We propose perturbed proximal algorithms that can provably escape strict saddles for nonsmooth weakly convex functions.
A Riemannian Block Coordinate Descent Method for Computing the Projection Robust Wasserstein Distance
We show that the complexity of arithmetic operations for RBCD to obtain an $\epsilon$-stationary point is $O(\epsilon^{-3})$.
Robust Low-rank Matrix Completion via an Alternating Manifold Proximal Gradient Continuation Method
This problem aims to decompose a partially observed matrix into the superposition of a low-rank matrix and a sparse matrix, where the sparse matrix captures the grossly corrupted entries of the matrix.
