6 citations · 7 across the 20 of their papers we have counts for
7 papers · 1 filter
Mixed updating in structured populations
David A. Brewster, Yichen Huang, Michael Mitzenmacher +1
Evolutionary graph theory (EGT) studies the effect of population structure on evolutionary dynamics. The vertices of the graph represent the individuals. The edges denote inter…
The Mixed Birth-death/death-Birth Moran Process
David A. Brewster, Yichen Huang, Michael Mitzenmacher +1
We study evolutionary dynamics on graphs in which each step consists of one birth and one death, also known as the Moran processes. There are two types of individuals: residents wi…
From Score Distributions to Balance: Plug-and-Play Mixture-of-Experts Routing
Rana Shahout, Colin Cai, Yilun Du +2
Mixture-of-Experts (MoE) models can scale parameter capacity by routing each token to a subset of experts through a learned gate function. While conditional routing reduces trainin…
Intra-request branch orchestration for efficient LLM reasoning
Weifan Jiang, Rana Shahout, Yilun Du +2
Large Language Models (LLMs) increasingly rely on inference-time reasoning algorithms such as chain-of-thought and multi-branch reasoning to improve accuracy on complex tasks. Thes…
Sketch Disaggregation Across Time and Space
Jonatan Langlet, Peiqing Chen, Michael Mitzenmacher +3
Streaming analytics are essential in a large range of applications, including databases, networking, and machine learning. To optimize performance, practitioners are increasingly o…
Queueing, Predictions, and LLMs: Challenges and Open Problems
Michael Mitzenmacher, Rana Shahout
Queueing systems present many opportunities for applying machine-learning predictions, such as estimated service times, to improve system performance. This integration raises numer…
HACK: Homomorphic Acceleration via Compression of the Key-Value Cache for Disaggregated LLM Inference
Zeyu Zhang, Haiying Shen, Shay Vargaftik +3
Disaggregated Large Language Model (LLM) inference has gained popularity as it separates the computation-intensive prefill stage from the memory-intensive decode stage, avoiding th…