Search Results for author:
Found 5 papers, 3 papers with code
NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language Descriptions
The Natural Language for Optimization (NL4Opt) Competition was created to investigate methods of extracting the meaning and formulation of an optimization problem based on its text description.
GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning
Federated learning (FL) is an effective technique to directly involve edge devices in machine learning training while preserving client privacy.
Augmenting Operations Research with Auto-Formulation of Optimization Models from Problem Descriptions
We describe an augmented intelligence system for simplifying and enhancing the modeling experience for operations research.
Anchor Sampling for Federated Learning with Partial Client Participation
The lack of inactive clients' updates in partial client participation makes it more likely for the model aggregation to deviate from the aggregation based on full client participation.
Sign Bit is Enough: A Learning Synchronization Framework for Multi-hop All-reduce with Ultimate Compression
Traditional one-bit compressed stochastic gradient descent can not be directly employed in multi-hop all-reduce, a widely adopted distributed training paradigm in network-intensive high-performance computing systems such as public clouds.
