64 citations · 252 across the 64 of their papers we have counts for
7 papers · 2 filters
Hail to the Thief: Exploring Attacks and Defenses in Decentralised GRPO
Nikolay Blagoev, Oğuzhan Ersoy, Lydia Yiyu Chen
Group Relative Policy Optimization (GRPO) has demonstrated wide adoption in the post-training of Large Language Models (LLMs). In GRPO, prompts are answered by the model and prefer…
Go With The Flow: Churn-Tolerant Decentralized Training of Large Language Models
Nikolay Blagoev, Bart Cox, Jérémie Decouchant +1
Motivated by the emergence of large language models (LLMs) and the importance of democratizing their training, we propose GWTF, the first crash tolerant practical decentralized tra…
Enhancing Trust-Region Bayesian Optimization via Newton Methods
Quanlin Chen, Yiyu Chen, Jing Huo +3
Bayesian Optimization (BO) has been widely applied to optimize expensive black-box functions while retaining sample efficiency. However, scaling BO to high-dimensional spaces remai…
Match & Choose: Model Selection Framework for Fine-tuning Text-to-Image Diffusion Models
Basile Lewandowski, Robert Birke, Lydia Y. Chen
Text-to-image (T2I) models based on diffusion and transformer architectures advance rapidly. They are often pretrained on large corpora, and openly shared on a model platform, such…
TS-Inverse: A Gradient Inversion Attack Tailored for Federated Time Series Forecasting Models
Caspar Meijer, Jiyue Huang, Shreshtha Sharma +2
Federated learning (FL) for time series forecasting (TSF) enables clients with privacy-sensitive time series (TS) data to collaboratively learn accurate forecasting models, for exa…
WaveStitch: Flexible and Fast Conditional Time Series Generation with Diffusion Models
Aditya Shankar, Lydia Y. Chen, Arie van Deursen +1
Generating temporal data under conditions is crucial for forecasting, imputation, and generative tasks. Such data often has metadata and partially observed signals that jointly inf…
SkipPipe: Partial and Reordered Pipelining Framework for Training LLMs in Heterogeneous Networks
Nikolay Blagoev, Lydia Yiyu Chen, Oğuzhan Ersoy
Data and pipeline parallelism are ubiquitous for training of Large Language Models (LLM) on distributed nodes. Driven by the need for cost-effective training, recent work explores…