From the 1 of 4 linked papers with an AI index.
1 citations · 1 across the 1 of their papers we have counts for
4 papers
The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication
Kumar Kshitij Patel, Margalit Glasgow, Ali Zindari +5
The paper analyzes the theoretical limits of Local SGD for distributed learning with heterogeneous data, showing existing heterogeneity assumptions are insufficient for proving its…
Learning When to Adapt
Ali Zindari, Xiaowen Jiang, Rotem Mulayoff +1
Low-rank adaptation (LoRA) is a widely used parameter-efficient fine-tuning method, yet its learned correction is static: the same low-rank update is applied to every input. This i…
LoRA vs. Full Fine-Tuning: A Theoretical Perspective
Ali Zindari, Rotem Mulayoff, Sebastian U. Stich
Fine-tuning adapts a pre-trained model to downstream tasks using a small amount of labeled data. Low-Rank Adaptation (LoRA) is an efficient fine-tuning method that reduces memory a…
Decoupled SGDA for Games with Intermittent Strategy Communication
Ali Zindari, Parham Yazdkhasti, Anton Rodomanov +2
We focus on reducing communication overhead in multiplayer games, where frequently exchanging strategies between players is not feasible and players have noisy or outdated strategi…