Search Results for author:
Found 8 papers, 5 papers with code
Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm
Data markets help with identifying valuable training data: model consumers pay to train a model, the market uses that budget to identify data and train the model (the budget allocation problem), and finally the market compensates data providers according to their data contribution (revenue allocation problem).
Latent Multimodal Functional Graphical Model Estimation
Joint multimodal functional data acquisition, where functional data from multiple modes are measured simultaneously from the same subject, has emerged as an exciting modern approach enabled by recent engineering breakthroughs in the neurological and biological sciences.
L-SVRG and L-Katyusha with Adaptive Sampling
Stochastic gradient-based optimization methods, such as L-SVRG and its accelerated variant L-Katyusha (Kovalev et al., 2020), are widely used to train machine learning models. The theoretical and empirical performance of L-SVRG and L-Katyusha can be improved by sampling observations from a non-uniform distribution (Qian et al., 2021).
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback
As a result, client sampling plays an important role in FL systems as it affects the convergence rate of optimization algorithms used to train machine learning models.
High-dimensional Functional Graphical Model Structure Learning via Neighborhood Selection Approach
We propose a neighborhood selection approach to estimate the structure of Gaussian functional graphical models, where we first estimate the neighborhood of each node via a function-on-function regression and subsequently recover the entire graph structure by combining the estimated neighborhoods.
Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques
We investigate the optimization aspects of personalized Federated Learning (FL).
FuDGE: A Method to Estimate a Functional Differential Graph in a High-Dimensional Setting
We first define a functional differential graph that captures the differences between two functional graphical models and formally characterize when the functional differential graph is well defined.
Direct Estimation of Differential Functional Graphical Models
We consider the problem of estimating the difference between two functional undirected graphical models with shared structures.
