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Found 9 papers, 3 papers with code
Fast Dual Variational Inference for Non-Conjugate LGMs
Latent Gaussian models (LGMs) are widely used in statistics and machine learning.
Quantum Algorithms for Structured Prediction
We introduce two quantum algorithms for solving structured prediction problems.
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.
Polar Deconvolution of Mixed Signals
The signal demixing problem seeks to separate a superposition of multiple signals into its constituent components.
NBIHT: An Efficient Algorithm for 1-bit Compressed Sensing with Optimal Error Decay Rate
The Binary Iterative Hard Thresholding (BIHT) algorithm is a popular reconstruction method for one-bit compressed sensing due to its simplicity and fast empirical convergence.
Information Theory Numerical Analysis Information Theory Numerical Analysis 94-XX
Improving Fairness for Data Valuation in Horizontal Federated Learning
The success of federated learning depends largely on the participation of data owners.
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.
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].
Knowledge-Injected Federated Learning
Federated learning is an emerging technique for training models from decentralized data sets.
