2 citations · 2 across the 4 of their papers we have counts for
5 papers
Energy and Latency First: How Deployment Constraints Reverse Time-Series Model Selection
Shruti Bothe, Illyyne Saffar, Ayush K. Varshney
AI models, and time-series models in particular are typically ranked by accuracy and deployed under constraints absent from that ranking. In this paper, we study a real setting whe…
Domain Adaptation Under Wireless Network Constraints: When Does It Become Green?
Illyyne Saffar, Aurélie Boisbunon, Shruti Bothe
The deployment of data-driven models in 6G wireless networks is increasingly challenged by frequent distribution shifts that degrade performance over time. Unsupervised Domain Adap…
Through the telecom lens: Are all training samples important?
Shruti Bothe, Illyyne Saffar, Aurelie Boisbunon +3
The rise of AI in telecommunications, from optimizing Radio Access Networks to managing user experience, has sharply increased data volumes and training demands. Telecom data is of…
Deep Unsupervised Domain Adaptation for Time Series Classification: a Benchmark
Hassan Ismail Fawaz, Ganesh Del Grosso, Tanguy Kerdoncuff +2
Unsupervised Domain Adaptation (UDA) aims to harness labeled source data to train models for unlabeled target data. Despite extensive research in domains like computer vision and n…
Knothe-Rosenblatt transport for Unsupervised Domain Adaptation
Aladin Virmaux, Illyyne Saffar, Jianfeng Zhang +1
Unsupervised domain adaptation (UDA) aims at exploiting related but different data sources to tackle a common task in a target domain. UDA remains a central yet challenging problem…