13 citations · 18 across the 9 of their papers we have counts for
9 papers
Revealing Combinatorial Reasoning of GNNs via Graph Concept Bottleneck Layer
Yue Niu, Zhaokai Sun, Jiayi Yang +5
Despite their success in various domains, the growing dependence on GNNs raises a critical concern about the nature of the combinatorial reasoning underlying their predictions, whi…
AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems
AgiBot World Contributors, AgiBot-World-Contributors, Qingwen Bu +50
We explore how scalable robot data can address real-world challenges for generalized robotic manipulation. Introducing AgiBot World, a large-scale platform comprising over 1 millio…
MobiZO: Enabling Efficient LLM Fine-Tuning at the Edge via Inference Engines
Lei Gao, Amir Ziashahabi, Yue Niu +2
Large Language Models (LLMs) are currently pre-trained and fine-tuned on large cloud servers. The next frontier is LLM personalization, where a foundation model can be fine-tuned w…
Embracing Federated Learning: Enabling Weak Client Participation via Partial Model Training
Sunwoo Lee, Tuo Zhang, Saurav Prakash +2
In Federated Learning (FL), clients may have weak devices that cannot train the full model or even hold it in their memory space. To implement large-scale FL applications, thus, it…
Ethos: Rectifying Language Models in Orthogonal Parameter Space
Lei Gao, Yue Niu, Tingting Tang +2
Language models (LMs) have greatly propelled the research on natural language processing. However, LMs also raise concerns regarding the generation of biased or toxic content and t…
Edge Private Graph Neural Networks with Singular Value Perturbation
Tingting Tang, Yue Niu, Salman Avestimehr +1
Graph neural networks (GNNs) play a key role in learning representations from graph-structured data and are demonstrated to be useful in many applications. However, the GNN trainin…
ATP: Enabling Fast LLM Serving via Attention on Top Principal Keys
Yue Niu, Saurav Prakash, Salman Avestimehr
We propose a new attention mechanism with linear complexity, ATP, that fixates \textbf{A}ttention on \textbf{T}op \textbf{P}rincipal keys, rather than on each individual token. Par…
All Rivers Run to the Sea: Private Learning with Asymmetric Flows
Yue Niu, Ramy E. Ali, Saurav Prakash +1
Data privacy is of great concern in cloud machine-learning service platforms, when sensitive data are exposed to service providers. While private computing environments (e.g., secu…
Lottery Aware Sparsity Hunting: Enabling Federated Learning on Resource-Limited Edge
Sara Babakniya, Souvik Kundu, Saurav Prakash +2
Edge devices can benefit remarkably from federated learning due to their distributed nature; however, their limited resource and computing power poses limitations in deployment. A…