{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T01:05:47Z","timestamp":1740099947777,"version":"3.37.3"},"reference-count":53,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,12,10]],"date-time":"2020-12-10T00:00:00Z","timestamp":1607558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,12,10]],"date-time":"2020-12-10T00:00:00Z","timestamp":1607558400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,12,10]],"date-time":"2020-12-10T00:00:00Z","timestamp":1607558400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100010428","name":"Innovation and Technology Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010428","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,12,10]]},"DOI":"10.1109\/bigdata50022.2020.9378141","type":"proceedings-article","created":{"date-parts":[[2021,3,19]],"date-time":"2021-03-19T21:10:21Z","timestamp":1616188221000},"page":"310-319","source":"Crossref","is-referenced-by-count":2,"title":["Towards Self-Tuning Parameter Servers"],"prefix":"10.1109","author":[{"given":"Chris","family":"Liu","sequence":"first","affiliation":[]},{"given":"Pengfei","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Bo","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Hang","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Ziliang","family":"Lai","sequence":"additional","affiliation":[]},{"given":"Eric","family":"Lo","sequence":"additional","affiliation":[]},{"given":"Korris","family":"Chung","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","article-title":"Project adam: Building an efficient and scalable deep learning training system","author":"chilimbi","year":"2014","journal-title":"OSDI"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"article-title":"Kdd cup 2012, track 1","year":"2012","author":"dataset","key":"ref33"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3035918.3064042"},{"key":"ref31","article-title":"Convergence analysis of distributed stochastic gradient descent with shuffling","author":"meng","year":"2017","journal-title":"NIPS"},{"article-title":"Is query optimization a &#x201D;solved\" problem?","year":"2014","author":"lohman","key":"ref30"},{"key":"ref37","article-title":"Imagenet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"NIPS"},{"article-title":"Omnivore: An optimizer for multi-device deep learning on cpus and gpus","year":"2016","author":"hadjis","key":"ref36"},{"article-title":"Learning multiple layers of features from tiny images","year":"2009","author":"krizhevsky","key":"ref35"},{"article-title":"Criteo releases industry&#x2019;s largest-ever dataset for machine learning to academic community","year":"2015","author":"labs","key":"ref34"},{"journal-title":"An Introduction to Statistical Learning with Applications in R","year":"2014","author":"james","key":"ref28"},{"key":"ref27","article-title":"Accelerating stochastic gradient descent using predictive variance reduction","author":"johnson","year":"2013","journal-title":"NIPS"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"ref2","article-title":"Tensorflow: A system for large-scale machine learning","author":"abadi","year":"2016","journal-title":"OSDI"},{"key":"ref1","article-title":"Large scale distributed deep networks","author":"dean","year":"2012","journal-title":"NIPS"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783270"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/3206.001.0001"},{"key":"ref21","article-title":"Scaling distributed machine learning with the parameter server","author":"li","year":"2014","journal-title":"OSDI"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.14778\/1687627.1687767"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40988-2_15"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1613\/jair.301"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3035918.3064029"},{"key":"ref50","article-title":"Hyperband: A novel bandit-based approach to hyperparameter optimization","author":"li","year":"2017","journal-title":"JMLR"},{"article-title":"Bohb: Robust and efficient hyperparameter optimization at scale","year":"2018","author":"falkner","key":"ref51"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1145\/3035918.3035933"},{"key":"ref52","article-title":"Synchronous multi-gpu training for deep learning with low-precision communications: An empirical study","author":"grubic","year":"2018","journal-title":"EBDT"},{"key":"ref10","article-title":"Taming the wild: A unified analysis of hogwild-style algorithms","author":"sa","year":"2015","journal-title":"NIPS"},{"article-title":"Tupaq: An efficient planner for large-scale predictive analytic queries","year":"2015","author":"sparks","key":"ref11"},{"article-title":"Towards self-tuning parameter servers","year":"2018","author":"liu","key":"ref40"},{"key":"ref12","article-title":"Auto-weka 2.0: Automatic model selection and hyperparameter optimization in WEKA","author":"kotthoff","year":"2017","journal-title":"JMLR"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098043"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/337180.337646"},{"year":"2016","key":"ref15","article-title":"GPyOpt: A bayesian optimization framework in python"},{"year":"2014","key":"ref16","article-title":"auto-sklearn"},{"key":"ref17","article-title":"Improved SVRG for non-strongly-convex or sum-of-non-convex objectives","author":"zhu","year":"2016","journal-title":"ICML"},{"key":"ref18","article-title":"Universal value function approximators","author":"schaul","year":"2015","journal-title":"ICML"},{"key":"ref19","article-title":"Smooth and strong: Map inference with linear convergence","author":"meshi","year":"2015","journal-title":"NIPS"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.14778\/2732977.2733001"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3187009.3177734"},{"key":"ref6","article-title":"Practical bayesian optimization of machine learning algorithms","author":"snoek","year":"2012","journal-title":"NIPS"},{"key":"ref5","article-title":"A tutorial on bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning","author":"brochu","year":"2010","journal-title":"CoRR"},{"key":"ref8","article-title":"Cherrypick: Adaptively unearthing the best cloud configurations for big data analytics","author":"alipourfard","year":"2017","journal-title":"NSDI"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3187009.3177737"},{"key":"ref49","article-title":"A system for massively parallel hyperparameter tuning","author":"li","year":"2020","journal-title":"MLSys"},{"key":"ref9","article-title":"Hogwild: A lock-free approach to parallelizing stochastic gradient descent","author":"recht","year":"2011","journal-title":"NIPS"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1153"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1145\/2723372.2749432"},{"key":"ref48","article-title":"A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention","author":"guyon","year":"2016","journal-title":"Workshop on Automatic Machine Learning"},{"key":"ref47","article-title":"Hemingway: Modeling distributed optimization algorithms","author":"pan","year":"2017","journal-title":"CoRR"},{"key":"ref42","article-title":"Convergence analysis of two-layer neural networks with relu activation","author":"li","year":"2017","journal-title":"NIPS"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/2882903.2915205"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2015.2472014"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.14778\/1920841.1920931"}],"event":{"name":"2020 IEEE International Conference on Big Data (Big Data)","start":{"date-parts":[[2020,12,10]]},"location":"Atlanta, GA, USA","end":{"date-parts":[[2020,12,13]]}},"container-title":["2020 IEEE International Conference on Big Data (Big Data)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9377717\/9377728\/09378141.pdf?arnumber=9378141","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,27]],"date-time":"2022-06-27T15:42:58Z","timestamp":1656344578000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9378141\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,10]]},"references-count":53,"URL":"https:\/\/doi.org\/10.1109\/bigdata50022.2020.9378141","relation":{},"subject":[],"published":{"date-parts":[[2020,12,10]]}}}