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Found 5 papers, 1 papers with code
Streamlining in the Riemannian Realm: Efficient Riemannian Optimization with Loopless Variance Reduction
These methods replace the outer loop with probabilistic gradient computation triggered by a coin flip in each iteration, ensuring simpler proofs, efficient hyperparameter selection, and sharp convergence guarantees.
Correlated Quantization for Faster Nonconvex Distributed Optimization
We analyze the forefront distributed non-convex optimization algorithm MARINA (Gorbunov et al., 2022) utilizing the proposed correlated quantizers and show that it outperforms the original MARINA and distributed SGD of Suresh et al. (2022) with regard to the communication complexity.
MAST: Model-Agnostic Sparsified Training
We introduce a novel optimization problem formulation that departs from the conventional way of minimizing machine learning model loss as a black-box function.
Graph-based Nearest Neighbor Search in Hyperbolic Spaces
From a theoretical perspective, we rigorously analyze the time and space complexity of graph-based NNS, assuming that an n-element dataset is uniformly distributed within a d-dimensional ball of radius R in the hyperbolic space of curvature -1.
Tight asymptotics of clique-chromatic numbers of dense random graphs
The clique chromatic number of a graph is the minimum number of colors required to assign to its vertex set so that no inclusion maximal clique is monochromatic.
Combinatorics 05C80, 05C15
