18 citations · 18 across the 2 of their papers we have counts for
4 papers
FedSynth: Gradient Compression via Synthetic Data in Federated Learning
Shengyuan Hu, Jack Goetz, Kshitiz Malik +3
Model compression is important in federated learning (FL) with large models to reduce communication cost. Prior works have been focusing on sparsification based compression that co…
Convex Latent Effect Logit Model via Sparse and Low-rank Decomposition
Hongyuan Zhan, Kamesh Madduri, Venkataraman Shankar
In this paper, we propose a convex formulation for learning logistic regression model (logit) with latent heterogeneous effect on sub-population. In transportation, logistic regres…
Evaluating Lottery Tickets Under Distributional Shifts
Shrey Desai, Hongyuan Zhan, Ahmed Aly
The Lottery Ticket Hypothesis suggests large, over-parameterized neural networks consist of small, sparse subnetworks that can be trained in isolation to reach a similar (or better…
Efficient Online Hyperparameter Optimization for Kernel Ridge Regression with Applications to Traffic Time Series Prediction
Hongyuan Zhan, Gabriel Gomes, Xiaoye S. Li +2
Computational efficiency is an important consideration for deploying machine learning models for time series prediction in an online setting. Machine learning algorithms adjust mod…