Search Results for author: Yiwen Zhu

Found 9 papers, 1 papers with code

Homotopy Based Reinforcement Learning with Maximum Entropy for Autonomous Air Combat

no code implementations1 Dec 2021 Yiwen Zhu, Zhou Fang, Yuan Zheng, Wenya Wei

In this paper, we propose a homotopy-based soft actor-critic method (HSAC) which focuses on addressing these problems via following the homotopy path between the original task with sparse reward and the auxiliary task with artificial prior experience reward.

reinforcement-learning

Phoebe: A Learning-based Checkpoint Optimizer

no code implementations5 Oct 2021 Yiwen Zhu, Matteo Interlandi, Abhishek Roy, Krishnadhan Das, Hiren Patel, Malay Bag, Hitesh Sharma, Alekh Jindal

To address these issues, we propose Phoebe, an efficient learning-based checkpoint optimizer.

MLOS: An Infrastructure for Automated Software Performance Engineering

1 code implementation1 Jun 2020 Carlo Curino, Neha Godwal, Brian Kroth, Sergiy Kuryata, Greg Lapinski, Si-Qi Liu, Slava Oks, Olga Poppe, Adam Smiechowski, Ed Thayer, Markus Weimer, Yiwen Zhu

In this paper we present: MLOS, an ML-powered infrastructure and methodology to democratize and automate Software Performance Engineering.

Vamsa: Automated Provenance Tracking in Data Science Scripts

no code implementations7 Jan 2020 Mohammad Hossein Namaki, Avrilia Floratou, Fotis Psallidas, Subru Krishnan, Ashvin Agrawal, Yinghui Wu, Yiwen Zhu, Markus Weimer

There has recently been a lot of ongoing research in the areas of fairness, bias and explainability of machine learning (ML) models due to the self-evident or regulatory requirements of various ML applications.

Fairness Recommendation Systems

Data Science through the looking glass and what we found there

no code implementations19 Dec 2019 Fotis Psallidas, Yiwen Zhu, Bojan Karlas, Matteo Interlandi, Avrilia Floratou, Konstantinos Karanasos, Wentao Wu, Ce Zhang, Subru Krishnan, Carlo Curino, Markus Weimer

The recent success of machine learning (ML) has led to an explosive growth both in terms of new systems and algorithms built in industry and academia, and new applications built by an ever-growing community of data science (DS) practitioners.

Griffon: Reasoning about Job Anomalies with Unlabeled Data in Cloud-based Platforms

no code implementations23 Aug 2019 Liqun Shao, Yiwen Zhu, Abhiram Eswaran, Kristin Lieber, Janhavi Mahajan, Minsoo Thigpen, Sudhir Darbha, SiQi Liu, Subru Krishnan, Soundar Srinivasan, Carlo Curino, Konstantinos Karanasos

In contrast, in Griffin we cast the problem to a corresponding regression one that predicts the runtime of a job, and show how the relative contributions of the features used to train our interpretable model can be exploited to rank the potential causes of job slowdowns.

Time Series Time Series Analysis

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