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Adaptive Optimization Algorithms for Machine Learning
Machine learning assumes a pivotal role in our data-driven world.
Sketch-and-Project Meets Newton Method: Global $\mathcal O(k^{-2})$ Convergence with Low-Rank Updates
In this paper, we propose the first sketch-and-project Newton method with fast $\mathcal O(k^{-2})$ global convergence rate for self-concordant functions.
Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes
As a special case, our theory allows us to show the convergence of First-Order Model-Agnostic Meta-Learning (FO-MAML) to the vicinity of a solution of Moreau objective.
Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation
Despite their high computation and communication costs, Newton-type methods remain an appealing option for distributed training due to their robustness against ill-conditioned convex problems.
ZeroSARAH: Efficient Nonconvex Finite-Sum Optimization with Zero Full Gradient Computation
Avoiding any full gradient computations (which are time-consuming steps) is important in many applications as the number of data samples $n$ usually is very large.
Lower Bounds and Optimal Algorithms for Personalized Federated Learning
Our first contribution is establishing the first lower bounds for this formulation, for both the communication complexity and the local oracle complexity.
