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Found 7 papers, 1 papers with code
MQuinE: a cure for "Z-paradox" in knowledge graph embedding models
Knowledge graph embedding (KGE) models achieved state-of-the-art results on many knowledge graph tasks including link prediction and information retrieval.
A dual approach for federated learning
We study the federated optimization problem from a dual perspective and propose a new algorithm termed federated dual coordinate descent (FedDCD), which is based on a type of coordinate descent method developed by Necora et al.[Journal of Optimization Theory and Applications, 2017].
Fair and efficient contribution valuation for vertical federated learning
We show that VerFedSV not only satisfies many desirable properties for fairness but is also efficient to compute, and can be adapted to both synchronous and asynchronous vertical federated learning algorithms.
Improving Fairness for Data Valuation in Horizontal Federated Learning
The success of federated learning depends largely on the participation of data owners.
Fast convergence of stochastic subgradient method under interpolation
We prove that SSGD converges, respectively, with rates $O(1/\epsilon)$ and $O(\log(1/\epsilon))$ for convex and strongly-convex objectives when interpolation holds.
Online mirror descent and dual averaging: keeping pace in the dynamic case
Online mirror descent (OMD) and dual averaging (DA) -- two fundamental algorithms for online convex optimization -- are known to have very similar (and sometimes identical) performance guarantees when used with a fixed learning rate.
Hierarchical Information Quadtree: Efficient Spatial Temporal Image Search for Multimedia Stream
Massive amount of multimedia data that contain times- tamps and geographical information are being generated at an unprecedented scale in many emerging applications such as photo sharing web site and social networks applications.
