1 citations · 1 across the 3 of their papers we have counts for
4 papers
Democratizing LLM Training with the Edge: Challenges, Systems, and the Road Ahead
Ziyue Luo, Jiaxuan Cai, Cedric LeDenmat +27
The growing computational power of modern GPU data centers has let a few industrial giants train and deploy large language models (LLMs) on massive amounts of data. Yet the sheer s…
Fine-Tuning Diffusion Models via Intermediate Distribution Shaping
Gautham Govind Anil, Shaan Ul Haque, Nithish Kannen +3
Diffusion models are widely used for generative tasks across domains. Given a pre-trained diffusion model, it is often desirable to fine-tune it further either to correct for error…
Robust Multi-Agent Bandits Over Undirected Graphs
Daniel Vial, Sanjay Shakkottai, R. Srikant
We consider a multi-agent multi-armed bandit setting in which honest agents collaborate over a network to minimize regret but malicious agents can disrupt learning arbitrar…
Adaptive KL-UCB based Bandit Algorithms for Markovian and i.i.d. Settings
Arghyadip Roy, Sanjay Shakkottai, R. Srikant
In the regret-based formulation of Multi-armed Bandit (MAB) problems, except in rare instances, much of the literature focuses on arms with i.i.d. rewards. In this paper, we consid…