8 citations · 14 across the 6 of their papers we have counts for
6 papers
Efficient Language Model Architectures for Differentially Private Federated Learning
Jae Hun Ro, Srinadh Bhojanapalli, Zheng Xu +2
Cross-device federated learning (FL) is a technique that trains a model on data distributed across typically millions of edge devices without data leaving the devices. SGD is the s…
Block Verification Accelerates Speculative Decoding
Ziteng Sun, Uri Mendlovic, Yaniv Leviathan +4
Speculative decoding is an effective method for lossless acceleration of large language models during inference. It uses a fast model to draft a block of tokens which are then veri…
SpecTr: Fast Speculative Decoding via Optimal Transport
Ziteng Sun, Ananda Theertha Suresh, Jae Hun Ro +3
Autoregressive sampling from large language models has led to state-of-the-art results in several natural language tasks. However, autoregressive sampling generates tokens one at a…
Scaling Language Model Size in Cross-Device Federated Learning
Jae Hun Ro, Theresa Breiner, Lara McConnaughey +4
Most studies in cross-device federated learning focus on small models, due to the server-client communication and on-device computation bottlenecks. In this work, we leverage vario…
Correlated quantization for distributed mean estimation and optimization
Ananda Theertha Suresh, Ziteng Sun, Jae Hun Ro +1
We study the problem of distributed mean estimation and optimization under communication constraints. We propose a correlated quantization protocol whose leading term in the error…
Transformer-based Models of Text Normalization for Speech Applications
Jae Hun Ro, Felix Stahlberg, Ke Wu +1
Text normalization, or the process of transforming text into a consistent, canonical form, is crucial for speech applications such as text-to-speech synthesis (TTS). In TTS, the sy…