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Andrew Chen

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2 papers
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2

JMLR Journal 2023 Journal Article

Scaling Up Models and Data with t5x and seqio

  • Adam Roberts
  • Hyung Won Chung
  • Gaurav Mishra
  • Anselm Levskaya
  • James Bradbury
  • Daniel Andor
  • Sharan Narang
  • Brian Lester

Scaling up training datasets and model parameters have benefited neural network-based language models, but also present challenges like distributed compute, input data bottlenecks and reproducibility of results. We introduce two simple and scalable software libraries that simplify these issues: t5x enables training large language models at scale, while seqio enables reproducible input and evaluation pipelines. These open-source libraries have been used to train models with hundreds of billions of parameters on multi-terabyte datasets. Configurations and instructions for T5-like and GPT-like models are also provided. The libraries can be found at https://github.com/google-research/t5x and https://github.com/google/seqio. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2023. ( edit, beta )

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STOC Conference 2022 Conference Paper

Deterministic graph coloring in the streaming model

  • Sepehr Assadi
  • Andrew Chen
  • Glenn Sun

Recent breakthroughs in graph streaming have led to design of semi-streaming algorithms for various graph coloring problems such as (Δ+1)-coloring, degeneracy-coloring, coloring triangle-free graphs, and others. These algorithms are all randomized in crucial ways and whether or not there is any deterministic analogue of them has remained an important open question in this line of work. We settle this fundamental question by proving that there is no deterministic single-pass semi-streaming algorithm that given a graph G with maximum degree Δ, can output a proper coloring of G using any number of colors which is sub-exponential in Δ. Our proof is based on analyzing the multi-party communication complexity of a related communication game, using random graph theory type arguments that may be of independent interest. We complement our lower bound by showing that one extra pass over the input allows one to recover an O (Δ 2 ) coloring via a deterministic semi-streaming algorithm. This result is extended to an O (Δ) coloring in O (logΔ) passes even in dynamic streams.

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