4 citations · 16 across the 11 of their papers we have counts for
8 papers · 1 filter
Process-BERT: A Framework for Representation Learning on Educational Process Data
Alexander Scarlatos, Christopher Brinton, Andrew Lan
Educational process data, i.e., logs of detailed student activities in computerized or online learning platforms, has the potential to offer deep insights into how students learn.…
Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?
Sheikh Shams Azam, Seyyedali Hosseinalipour, Qiang Qiu +1
In this paper, we question the rationale behind propagating large numbers of parameters through a distributed system during federated learning. We start by examining the rank chara…
Federated Learning Beyond the Star: Local D2D Model Consensus with Global Cluster Sampling
Frank Po-Chen Lin, Seyyedali Hosseinalipour, Sheikh Shams Azam +2
Federated learning has emerged as a popular technique for distributing model training across the network edge. Its learning architecture is conventionally a star topology between t…
Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregations
Frank Po-Chen Lin, Seyyedali Hosseinalipour, Sheikh Shams Azam +2
Federated learning has emerged as a popular technique for distributing machine learning (ML) model training across the wireless edge. In this paper, we propose two timescale hybrid…
A Fast Graph Neural Network-Based Method for Winner Determination in Multi-Unit Combinatorial Auctions
Mengyuan Lee, Seyyedali Hosseinalipour, Christopher G. Brinton +2
The combinatorial auction (CA) is an efficient mechanism for resource allocation in different fields, including cloud computing. It can obtain high economic efficiency and user fle…
Federated Learning with Communication Delay in Edge Networks
Frank Po-Chen Lin, Christopher G. Brinton, Nicolò Michelusi
Federated learning has received significant attention as a potential solution for distributing machine learning (ML) model training through edge networks. This work addresses an im…