{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T14:04:40Z","timestamp":1778767480030,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":32,"publisher":"ACM","license":[{"start":{"date-parts":[[2017,12,11]],"date-time":"2017-12-11T00:00:00Z","timestamp":1512950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2017,12,11]]},"DOI":"10.1145\/3135974.3135994","type":"proceedings-article","created":{"date-parts":[[2017,11,30]],"date-time":"2017-11-30T17:01:06Z","timestamp":1512061266000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":22,"title":["HyperDrive"],"prefix":"10.1145","author":[{"given":"Jeff","family":"Rasley","sequence":"first","affiliation":[{"name":"Brown University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxiong","family":"He","sequence":"additional","affiliation":[{"name":"Microsoft"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Yan","sequence":"additional","affiliation":[{"name":"University of Nevada, Reno"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Olatunji","family":"Ruwase","sequence":"additional","affiliation":[{"name":"Microsoft"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rodrigo","family":"Fonseca","sequence":"additional","affiliation":[{"name":"Brown University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2017,12,11]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"2017. A high performance open-source universal RPC framework. https:\/\/grpc.io. (2017).  2017. A high performance open-source universal RPC framework. https:\/\/grpc.io. (2017)."},{"key":"e_1_3_2_1_2_1","unstructured":"2017. Checkpoint\/Restore In Userspace (CRIU). https:\/\/criu.org\/. (2017). Accessed: 2017-09-13.  2017. Checkpoint\/Restore In Userspace (CRIU). https:\/\/criu.org\/. (2017). Accessed: 2017-09-13."},{"key":"e_1_3_2_1_3_1","volume-title":"TensorFlow: A System for Large-Scale Machine Learning. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16)","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi , Paul Barham , Jianmin Chen , Zhifeng Chen , Andy Davis , Jeffrey Dean , Matthieu Devin , Sanjay Ghemawat , Geoffrey Irving , Michael Isard , Manjunath Kudlur , Josh Levenberg , Rajat Monga , Sherry Moore , Derek G. Murray , Benoit Steiner , Paul Tucker , Vijay Vasudevan , Pete Warden , Martin Wicke , Yuan Yu , and Xiaoqiang Zheng . 2016 . TensorFlow: A System for Large-Scale Machine Learning. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16) . USENIX Association, GA, 265--283. https:\/\/www.usenix.org\/conference\/osdi16\/technical-sessions\/presentation\/abadi Mart\u00edn Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2016. TensorFlow: A System for Large-Scale Machine Learning. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16). USENIX Association, GA, 265--283. https:\/\/www.usenix.org\/conference\/osdi16\/technical-sessions\/presentation\/abadi"},{"key":"e_1_3_2_1_4_1","volume-title":"Williams","author":"Asadi Kavosh","year":"2016","unstructured":"Kavosh Asadi and Jason D . Williams . 2016 . Sample-efficient Deep Reinforcement Learning for Dialog Control. CoRR abs\/1612.06000 (2016). http:\/\/arxiv.org\/abs\/1612.06000 Kavosh Asadi and Jason D. Williams. 2016. Sample-efficient Deep Reinforcement Learning for Dialog Control. CoRR abs\/1612.06000 (2016). http:\/\/arxiv.org\/abs\/1612.06000"},{"key":"e_1_3_2_1_5_1","unstructured":"The GPyOpt authors. 2016. GPyOpt: A Bayesian Optimization framework in python. http:\/\/github.com\/SheffieldML\/GPyOpt. (2016).  The GPyOpt authors. 2016. GPyOpt: A Bayesian Optimization framework in python. http:\/\/github.com\/SheffieldML\/GPyOpt. (2016)."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.25080\/Majora-92bf1922-003"},{"key":"e_1_3_2_1_7_1","volume-title":"Proceedings of the 30th International Conference on International Conference on Machine Learning -","volume":"28","author":"Bergstra J.","unstructured":"J. Bergstra , D. Yamins , and D. D. Cox . 2013. Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures . In Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28 (ICML '13). JMLR.org, I-115--I-123. http:\/\/dl.acm.org\/citation.cfm?id=3042817.3042832 J. Bergstra, D. Yamins, and D. D. Cox. 2013. Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures. In Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28 (ICML '13). JMLR.org, I-115--I-123. http:\/\/dl.acm.org\/citation.cfm?id=3042817.3042832"},{"key":"e_1_3_2_1_8_1","volume-title":"11th USENIX Symposium on Operating Systems Design and Implementation (OSDI 14)","author":"Chilimbi Trishul","year":"2014","unstructured":"Trishul Chilimbi , Yutaka Suzue , Johnson Apacible , and Karthik Kalyanaraman . 2014 . Project Adam: Building an Efficient and Scalable Deep Learning Training System . In 11th USENIX Symposium on Operating Systems Design and Implementation (OSDI 14) . USENIX Association, Broomfield, CO, 571--582. https:\/\/www.usenix.org\/conference\/osdi14\/technical-sessions\/presentation\/chilimbi Trishul Chilimbi, Yutaka Suzue, Johnson Apacible, and Karthik Kalyanaraman. 2014. Project Adam: Building an Efficient and Scalable Deep Learning Training System. In 11th USENIX Symposium on Operating Systems Design and Implementation (OSDI 14). USENIX Association, Broomfield, CO, 571--582. https:\/\/www.usenix.org\/conference\/osdi14\/technical-sessions\/presentation\/chilimbi"},{"key":"e_1_3_2_1_9_1","unstructured":"Fran\u00e7ois Chollet. 2015. Keras. https:\/\/github.com\/fchollet\/keras. (2015).  Fran\u00e7ois Chollet. 2015. Keras. https:\/\/github.com\/fchollet\/keras. (2015)."},{"key":"e_1_3_2_1_10_1","volume-title":"Torch: A Modular Machine Learning Software Library. Idiap-RR Idiap-RR-46-2002. IDIAP.","author":"Collobert Ronan","year":"2002","unstructured":"Ronan Collobert , Samy Bengio , and Johnny Mari\u00e9thoz . 2002 . Torch: A Modular Machine Learning Software Library. Idiap-RR Idiap-RR-46-2002. IDIAP. Ronan Collobert, Samy Bengio, and Johnny Mari\u00e9thoz. 2002. Torch: A Modular Machine Learning Software Library. Idiap-RR Idiap-RR-46-2002. IDIAP."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.5555\/2832581.2832731"},{"key":"e_1_3_2_1_12_1","first-page":"1079","article-title":"Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems","author":"Even-Dar Eyal","year":"2006","unstructured":"Eyal Even-Dar , Shie Mannor , and Yishay Mansour . 2006 . Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems . Journal of machine learning research 7 , Jun (2006), 1079 -- 1105 . Eyal Even-Dar, Shie Mannor, and Yishay Mansour. 2006. Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems. Journal of machine learning research 7, Jun (2006), 1079--1105.","journal-title":"Journal of machine learning research 7"},{"key":"e_1_3_2_1_13_1","volume-title":"Deep Learning with Apache Spark and TensorFlow. https:\/\/databricks.com\/blog\/2016\/01\/25\/deep-learning-with-apache-spark-and-tensorflow.html. (January","author":"Hunter Tim","year":"2016","unstructured":"Tim Hunter . 2016. Deep Learning with Apache Spark and TensorFlow. https:\/\/databricks.com\/blog\/2016\/01\/25\/deep-learning-with-apache-spark-and-tensorflow.html. (January 2016 ). Tim Hunter. 2016. Deep Learning with Apache Spark and TensorFlow. https:\/\/databricks.com\/blog\/2016\/01\/25\/deep-learning-with-apache-spark-and-tensorflow.html. (January 2016)."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-25566-3_40"},{"key":"e_1_3_2_1_15_1","volume-title":"Caffe: Convolutional Architecture for Fast Feature Embedding. arXiv preprint arXiv:1408.5093","author":"Jia Yangqing","year":"2014","unstructured":"Yangqing Jia , Evan Shelhamer , Jeff Donahue , Sergey Karayev , Jonathan Long , Ross Girshick , Sergio Guadarrama , and Trevor Darrell . 2014 . Caffe: Convolutional Architecture for Fast Feature Embedding. arXiv preprint arXiv:1408.5093 (2014). Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell. 2014. Caffe: Convolutional Architecture for Fast Feature Embedding. arXiv preprint arXiv:1408.5093 (2014)."},{"key":"e_1_3_2_1_16_1","volume-title":"Proc. of ICLR 17","author":"Klein Aaron","year":"2017","unstructured":"Aaron Klein , Stefan Falkner , Jost Tobias Springenberg , and Frank Hutter . 2017 . Learning curve prediction with Bayesian neural networks . Proc. of ICLR 17 (2017). Aaron Klein, Stefan Falkner, Jost Tobias Springenberg, and Frank Hutter. 2017. Learning curve prediction with Bayesian neural networks. Proc. of ICLR 17 (2017)."},{"key":"e_1_3_2_1_17_1","unstructured":"Oleg Klimov. 2017. LunarLander-v2. https:\/\/gym.openai.com\/envs\/LunarLander-v2. (2017).  Oleg Klimov. 2017. LunarLander-v2. https:\/\/gym.openai.com\/envs\/LunarLander-v2. (2017)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.25080\/Majora-14bd3278-006"},{"key":"e_1_3_2_1_19_1","unstructured":"Alex Krizhevsky. 2017. cuda-convnet. https:\/\/code.google.com\/p\/cuda-convnet\/. (2017).  Alex Krizhevsky. 2017. cuda-convnet. https:\/\/code.google.com\/p\/cuda-convnet\/. (2017)."},{"key":"e_1_3_2_1_20_1","volume-title":"Learning multiple layers of features from tiny images. (2009). Technical report","author":"Krizhevsky Alex","unstructured":"Alex Krizhevsky and Geoffrey Hinton . 2009. Learning multiple layers of features from tiny images. (2009). Technical report , University of Toronto . Alex Krizhevsky and Geoffrey Hinton. 2009. Learning multiple layers of features from tiny images. (2009). Technical report, University of Toronto."},{"key":"e_1_3_2_1_21_1","volume-title":"Proc. of ICLR 17","author":"Li Lisha","year":"2017","unstructured":"Lisha Li , Kevin Jamieson , Giulia DeSalvo , Afshin Rostamizadeh , and Ameet Talwalkar . 2017 . Hyperband: Bandit-based Configuration Evaluation for Hyperparameter Optimization . Proc. of ICLR 17 (2017). Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar. 2017. Hyperband: Bandit-based Configuration Evaluation for Hyperparameter Optimization. Proc. of ICLR 17 (2017)."},{"key":"e_1_3_2_1_22_1","volume-title":"Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent. In Advances in Neural Information Processing Systems 24","author":"Recht Benjamin","year":"2011","unstructured":"Benjamin Recht , Christopher Re , Stephen Wright , and Feng Niu . 2011 . Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent. In Advances in Neural Information Processing Systems 24 , J. Shawe-Taylor, R. S. Zemel, P. L. Bartlett, F. Pereira, and K. Q. Weinberger (Eds.). Curran Associates, Inc. , 693--701. Benjamin Recht, Christopher Re, Stephen Wright, and Feng Niu. 2011. Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent. In Advances in Neural Information Processing Systems 24, J. Shawe-Taylor, R. S. Zemel, P. L. Bartlett, F. Pereira, and K. Q. Weinberger (Eds.). Curran Associates, Inc., 693--701."},{"key":"e_1_3_2_1_23_1","volume-title":"Bidirectional Attention Flow for Machine Comprehension. arXiv CoRR abs\/1611.01603","author":"Seo Min Joon","year":"2016","unstructured":"Min Joon Seo , Aniruddha Kembhavi , Ali Farhadi , and Hannaneh Hajishirzi . 2016. Bidirectional Attention Flow for Machine Comprehension. arXiv CoRR abs\/1611.01603 ( 2016 ). http:\/\/arxiv.org\/abs\/1611.01603 Min Joon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2016. Bidirectional Attention Flow for Machine Comprehension. arXiv CoRR abs\/1611.01603 (2016). http:\/\/arxiv.org\/abs\/1611.01603"},{"key":"e_1_3_2_1_24_1","unstructured":"Jasper Snoek Hugo Larochelle and Ryan P Adams. 2012. Practical bayesian optimization of machine learning algorithms. In Advances in neural information processing systems. 2951--2959.   Jasper Snoek Hugo Larochelle and Ryan P Adams. 2012. Practical bayesian optimization of machine learning algorithms. In Advances in neural information processing systems. 2951--2959."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/2806777.2806945"},{"key":"e_1_3_2_1_26_1","volume-title":"Freeze-Thaw Bayesian Optimization. arXiv preprint arXiv:1406.3896","author":"Swersky Kevin","year":"2014","unstructured":"Kevin Swersky , Jasper Snoek , and Ryan Prescott Adams . 2014. Freeze-Thaw Bayesian Optimization. arXiv preprint arXiv:1406.3896 ( 2014 ). Kevin Swersky, Jasper Snoek, and Ryan Prescott Adams. 2014. Freeze-Thaw Bayesian Optimization. arXiv preprint arXiv:1406.3896 (2014)."},{"key":"e_1_3_2_1_27_1","volume-title":"Distributed Tensor-Flow Assembly on Apache Hadoop YARN. https:\/\/hortonworks.com\/blog\/distributed-tensorflow-assembly-hadoop-yarn\/. (March","author":"Tan Wangda","year":"2017","unstructured":"Wangda Tan and Vinod Kumar Vavilapalli . 2017. Distributed Tensor-Flow Assembly on Apache Hadoop YARN. https:\/\/hortonworks.com\/blog\/distributed-tensorflow-assembly-hadoop-yarn\/. (March 2017 ). Wangda Tan and Vinod Kumar Vavilapalli. 2017. Distributed Tensor-Flow Assembly on Apache Hadoop YARN. https:\/\/hortonworks.com\/blog\/distributed-tensorflow-assembly-hadoop-yarn\/. (March 2017)."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2487629"},{"key":"e_1_3_2_1_29_1","volume-title":"Advances in Neural Information Processing Systems 29","author":"Wen Wei","year":"2074","unstructured":"Wei Wen , Chunpeng Wu , Yandan Wang , Yiran Chen , and Hai Li. 2016. Learning Structured Sparsity in Deep Neural Networks . In Advances in Neural Information Processing Systems 29 , D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett (Eds.). Curran Associates, Inc. , 2074 --2082. http:\/\/papers.nips.cc\/paper\/6504-learning-structured-sparsity-in-deep-neural-networks.pdf Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li. 2016. Learning Structured Sparsity in Deep Neural Networks. In Advances in Neural Information Processing Systems 29, D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett (Eds.). Curran Associates, Inc., 2074--2082. http:\/\/papers.nips.cc\/paper\/6504-learning-structured-sparsity-in-deep-neural-networks.pdf"},{"key":"e_1_3_2_1_30_1","unstructured":"Lee Yang Jun Shi Bobbie Chern and Andy Feng. 2017. Open Sourcing TensorFlowOnSpark: Distributed Deep Learning on Big-Data Clusters. http:\/\/yahoohadoop.tumblr.com\/post\/157196317141\/open-sourcing-tensorflowonspark-distributed-deep. (February 2017).  Lee Yang Jun Shi Bobbie Chern and Andy Feng. 2017. Open Sourcing TensorFlowOnSpark: Distributed Deep Learning on Big-Data Clusters. http:\/\/yahoohadoop.tumblr.com\/post\/157196317141\/open-sourcing-tensorflowonspark-distributed-deep. (February 2017)."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2005.00532.x"},{"key":"e_1_3_2_1_33_1","volume-title":"Recurrent Neural Network Regularization. arXiv CoRR abs\/1409.2329","author":"Zaremba Wojciech","year":"2014","unstructured":"Wojciech Zaremba , Ilya Sutskever , and Oriol Vinyals . 2014. Recurrent Neural Network Regularization. arXiv CoRR abs\/1409.2329 ( 2014 ). http:\/\/arxiv.org\/abs\/1409.2329 Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals. 2014. Recurrent Neural Network Regularization. arXiv CoRR abs\/1409.2329 (2014). http:\/\/arxiv.org\/abs\/1409.2329"}],"event":{"name":"Middleware '17: 18th International Middleware Conference","location":"Las Vegas Nevada","acronym":"Middleware '17","sponsor":["ACM Association for Computing Machinery","USENIX Assoc USENIX Assoc","IFIP"]},"container-title":["Proceedings of the 18th ACM\/IFIP\/USENIX Middleware Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3135974.3135994","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3135974.3135994","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T02:26:45Z","timestamp":1750213605000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3135974.3135994"}},"subtitle":["exploring hyperparameters with POP scheduling"],"short-title":[],"issued":{"date-parts":[[2017,12,11]]},"references-count":32,"alternative-id":["10.1145\/3135974.3135994","10.1145\/3135974"],"URL":"https:\/\/doi.org\/10.1145\/3135974.3135994","relation":{},"subject":[],"published":{"date-parts":[[2017,12,11]]},"assertion":[{"value":"2017-12-11","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}