research
∙
06/14/2023
Provably Personalized and Robust Federated Learning
Clustering clients with similar objectives and learning a model per clus...
share
research
∙
05/30/2023
Shuffle SGD is Always Better than SGD: Improved Analysis of SGD with Arbitrary Data Orders
Stochastic Gradient Descent (SGD) algorithms are widely used in optimizi...
research
∙
05/29/2023
Collaborative Learning via Prediction Consensus
We consider a collaborative learning setting where each agent's goal is ...
research
∙
05/30/2022
On Avoiding Local Minima Using Gradient Descent With Large Learning Rates
It has been widely observed in training of neural networks that when app...
research
∙
04/13/2022
Data-heterogeneity-aware Mixing for Decentralized Learning
Decentralized learning provides an effective framework to train machine ...
research
∙
02/03/2022
Byzantine-Robust Decentralized Learning via Self-Centered Clipping
In this paper, we study the challenging task of Byzantine-robust decentr...
research
∙
02/09/2021
Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data
Decentralized training of deep learning models is a key element for enab...
research
∙
08/08/2020
Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning
Federated learning is a challenging optimization problem due to the hete...
research
∙
06/29/2020
Multi-Head Attention: Collaborate Instead of Concatenate
Attention layers are widely used in natural language processing (NLP) an...
research
∙
12/10/2019
Advances and Open Problems in Federated Learning
Federated learning (FL) is a machine learning setting where many clients...
research
∙
10/12/2019
Model Fusion via Optimal Transport
Combining different models is a widely used paradigm in machine learning...
research
∙
03/29/2019
SysML: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
research
∙
02/25/2019
Forecasting intracranial hypertension using multi-scale waveform metrics
Objective: Intracranial hypertension is an important risk factor of seco...
research
∙
01/28/2019
Error Feedback Fixes SignSGD and other Gradient Compression Schemes
Sign-based algorithms (e.g. signSGD) have been proposed as a biased grad...
research
∙
06/01/2018
Global linear convergence of Newton's method without strong-convexity or Lipschitz gradients
We show that Newton's method converges globally at a linear rate for obj...
research
∙
01/13/2018
