23 citations · 26 across the 5 of their papers we have counts for
9 papers
Learning to Score: Tuning Cluster Schedulers through Reinforcement Learning
Martin Asenov, Qiwen Deng, Gingfung Yeung +1
Efficiently allocating incoming jobs to nodes in large-scale clusters can lead to substantial improvements in both cluster utilization and job performance. In order to allocate inc…
DISCO: Document Intelligence Suite for COmparative Evaluation
Kenza Benkirane, Dan Goldwater, Martin Asenov +1
Document intelligence requires accurate text extraction and reliable reasoning over document content. We introduce \textbf{DISCO}, a \emph{Document Intelligence Suite for COmparati…
Retrieval or Representation? Reassessing Benchmark Gaps in Multilingual and Visually Rich RAG
Martin Asenov, Kenza Benkirane, Dan Goldwater +1
Retrieval-augmented generation (RAG) is a common way to ground language models in external documents and up-to-date information. Classical retrieval systems relied on lexical metho…
Performance of Zero-Shot Time Series Foundation Models on Cloud Data
William Toner, Thomas L. Lee, Artjom Joosen +2
Time series foundation models (FMs) have emerged as a popular paradigm for zero-shot multi-domain forecasting. FMs are trained on numerous diverse datasets and claim to be effectiv…
Lightweight Online Adaption for Time Series Foundation Model Forecasts
Thomas L. Lee, William Toner, Rajkarn Singh +2
Foundation models (FMs) have emerged as a promising approach for time series forecasting. While effective, FMs typically remain fixed during deployment due to the high computationa…
Serverless Cold Starts and Where to Find Them
Artjom Joosen, Ahmed Hassan, Martin Asenov +5
This paper releases and analyzes a month-long trace of 85 billion user requests and 11.9 million cold starts from Huawei's serverless cloud platform. Our analysis spans workloads f…