3 citations · 3 across the 3 of their papers we have counts for
5 papers
Beyond URLs: Metadata Diversity and Position for Efficient LLM Pretraining
Dongyang Fan, Diba Hashemi, Sai Praneeth Karimireddy +1
Incorporating metadata in Large Language Models (LLMs) pretraining has recently emerged as a promising approach to accelerate training. However prior work highlighted only one usef…
Apertus: Democratizing Open and Compliant LLMs for Global Language Environments
Project Apertus, Alejandro Hernández-Cano, Alexander Hägele +100
We present Apertus, a fully open suite of large language models (LLMs) designed to address two systemic shortcomings in today's open model ecosystem: data compliance and multilingu…
CoBo: Collaborative Learning via Bilevel Optimization
Diba Hashemi, Lie He, Martin Jaggi
Collaborative learning is an important tool to train multiple clients more effectively by enabling communication among clients. Identifying helpful clients, however, presents chall…
Weighted Matching in the Random-Order Streaming and Robust Communication Models
Diba Hashemi, Weronika Wrzos-Kaminska
We study the maximum weight matching problem in the random-order semi-streaming model and in the robust communication model. Unlike many other sublinear models, in these two framew…
Provably-Efficient and Internally-Deterministic Parallel Union-Find
Alexander Fedorov, Diba Hashemi, Giorgi Nadiradze +1
Determining the degree of inherent parallelism in classical sequential algorithms and leveraging it for fast parallel execution is a key topic in parallel computing, and detailed a…