Lydia Y. Chen
6 papers in the PaperMetrix corpus
Papers by this author
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Understanding the Dark Side of Big Data Clusters: An Analysis beyond Failures
2015
Motivated by the high system complexity of today's datacenters, a large body of related studies tries to understand workloads and resource utilization in datacenters. However, there is little work on exploring unsuccessful job and task …
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Contention detection by throttling: A black-box on-line approach
2015
Visualization technology powers up the cloud computing paradigm and inevitably raises concerns about performance isolation of collocated virtual machines (VM). It is imperative for public cloud providers to guarantee performance targets for tenants' VMs while …
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Workload-Adaptive Configuration Tuning for Hierarchical Cloud Schedulers
2019 · IEEE Transactions on Parallel and Distributed Systems
Cluster schedulers provide flexible resource sharing mechanism for best-effort cloud jobs, which occupy a majority in modern datacenters. Properly tuning a scheduler's configurations is the key to these jobs' performance because it decides how to …
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DTGAN: Differential Private Training for Tabular GANs
2021 · arXiv (Cornell University)
Tabular generative adversarial networks (TGAN) have recently emerged to cater to the need of synthesizing tabular data -- the most widely used data format. While synthetic tabular data offers the advantage of complying with privacy …
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BatMan-CLR: Making Few-shots Meta-Learners Resilient Against Label Noise
2023 · arXiv (Cornell University)
The negative impact of label noise is well studied in classical supervised learning yet remains an open research question in meta-learning. Meta-learners aim to adapt to unseen learning tasks by learning a good initial model …
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Go With The Flow: Churn-Tolerant Decentralized Training of Large Language Models
2025 · arXiv (Cornell University)
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 training framework for LLMs. Differently from existing distributed and federated …