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Proceedings of Machine Learning and Systems, Vol. 1 (2019), 1--13.","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177730391"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611976700.34"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972825.71"},{"key":"e_1_3_2_2_33_1","volume-title":"Kingma and Jimmy Ba","author":"Diederik","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba . 2015 . Adam : A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7--9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds .). Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7--9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.)."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMTT.2010.2049768"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5934"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1609\/aiide.v18i1.21959"},{"key":"e_1_3_2_2_37_1","volume-title":"Pareto optimality. Pareto optimality, game theory and equilibria","author":"Luc Dinh The","year":"2008","unstructured":"Dinh The Luc . 2008. Pareto optimality. Pareto optimality, game theory and equilibria ( 2008 ), 481--515. Dinh The Luc. 2008. Pareto optimality. Pareto optimality, game theory and equilibria (2008), 481--515."},{"key":"e_1_3_2_2_38_1","volume-title":"5th Berkeley Symp. Math. Statist. Probability. 281--297","author":"MacQueen J","year":"1967","unstructured":"J MacQueen . 1967 . Classification and analysis of multivariate observations . In 5th Berkeley Symp. Math. Statist. Probability. 281--297 . J MacQueen. 1967. Classification and analysis of multivariate observations. In 5th Berkeley Symp. Math. Statist. Probability. 281--297."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3005745.3005750"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5957"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10458-020-09455-w"},{"key":"e_1_3_2_2_42_1","volume-title":"Iterative ranking from pair-wise comparisons. Advances in neural information processing systems","author":"Negahban Sahand","year":"2012","unstructured":"Sahand Negahban , Sewoong Oh , and Devavrat Shah . 2012. Iterative ranking from pair-wise comparisons. Advances in neural information processing systems , Vol. 25 ( 2012 ). Sahand Negahban, Sewoong Oh, and Devavrat Shah. 2012. Iterative ranking from pair-wise comparisons. Advances in neural information processing systems, Vol. 25 (2012)."},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3052895"},{"key":"e_1_3_2_2_44_1","volume-title":"International conference on machine learning. PMLR, 4257--4266","author":"Raileanu Roberta","year":"2018","unstructured":"Roberta Raileanu , Emily Denton , Arthur Szlam , and Rob Fergus . 2018 . Modeling others using oneself in multi-agent reinforcement learning . In International conference on machine learning. PMLR, 4257--4266 . Roberta Raileanu, Emily Denton, Arthur Szlam, and Rob Fergus. 2018. Modeling others using oneself in multi-agent reinforcement learning. In International conference on machine learning. PMLR, 4257--4266."},{"key":"e_1_3_2_2_45_1","volume-title":"International conference on machine learning. PMLR, 4295--4304","author":"Rashid Tabish","year":"2018","unstructured":"Tabish Rashid , Mikayel Samvelyan , Christian Schroeder , Gregory Farquhar , Jakob Foerster , and Shimon Whiteson . 2018 . Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning . In International conference on machine learning. PMLR, 4295--4304 . Tabish Rashid, Mikayel Samvelyan, Christian Schroeder, Gregory Farquhar, Jakob Foerster, and Shimon Whiteson. 2018. Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning. In International conference on machine learning. PMLR, 4295--4304."},{"key":"e_1_3_2_2_46_1","volume-title":"Expected value, reward outcome, and temporal difference error representations in a probabilistic decision task. Cerebral cortex","author":"Rolls Edmund T","year":"2008","unstructured":"Edmund T Rolls , Ciara McCabe , and Jerome Redoute . 2008. Expected value, reward outcome, and temporal difference error representations in a probabilistic decision task. Cerebral cortex , Vol. 18 , 3 ( 2008 ), 652--663. Edmund T Rolls, Ciara McCabe, and Jerome Redoute. 2008. Expected value, reward outcome, and temporal difference error representations in a probabilistic decision task. Cerebral cortex, Vol. 18, 3 (2008), 652--663."},{"key":"e_1_3_2_2_47_1","volume-title":"Multivariate uncertainty in deep learning","author":"Russell Rebecca L","year":"2021","unstructured":"Rebecca L Russell and Christopher Reale . 2021. Multivariate uncertainty in deep learning . IEEE Transactions on Neural Networks and Learning Systems ( 2021 ). Rebecca L Russell and Christopher Reale. 2021. Multivariate uncertainty in deep learning. IEEE Transactions on Neural Networks and Learning Systems (2021)."},{"key":"e_1_3_2_2_48_1","volume-title":"High-dimensional continuous control using generalized advantage estimation. arXiv preprint arXiv:1506.02438","author":"Schulman John","year":"2015","unstructured":"John Schulman , Philipp Moritz , Sergey Levine , Michael Jordan , and Pieter Abbeel . 2015. High-dimensional continuous control using generalized advantage estimation. arXiv preprint arXiv:1506.02438 ( 2015 ). John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel. 2015. High-dimensional continuous control using generalized advantage estimation. arXiv preprint arXiv:1506.02438 (2015)."},{"key":"e_1_3_2_2_49_1","volume-title":"Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347","author":"Schulman John","year":"2017","unstructured":"John Schulman , Filip Wolski , Prafulla Dhariwal , Alec Radford , and Oleg Klimov . 2017. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 ( 2017 ). John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)."},{"key":"e_1_3_2_2_50_1","volume-title":"SODA","volume":"98","author":"Schwiegelshohn Uwe","year":"1998","unstructured":"Uwe Schwiegelshohn and Ramin Yahyapour . 1998 . Analysis of first-come-first-serve parallel job scheduling . In SODA , Vol. 98 . Citeseer, 629--638. Uwe Schwiegelshohn and Ramin Yahyapour. 1998. Analysis of first-come-first-serve parallel job scheduling. In SODA, Vol. 98. Citeseer, 629--638."},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10878-020-00607-y"},{"key":"e_1_3_2_2_52_1","volume-title":"Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan and Andrew Zisserman . 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 ( 2014 ). Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)."},{"key":"e_1_3_2_2_53_1","unstructured":"Sainbayar Sukhbaatar Rob Fergus etal 2016. Learning multiagent communication with backpropagation. Advances in neural information processing systems Vol. 29 (2016).  Sainbayar Sukhbaatar Rob Fergus et al. 2016. Learning multiagent communication with backpropagation. Advances in neural information processing systems Vol. 29 (2016)."},{"key":"e_1_3_2_2_54_1","volume-title":"Vinicius Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z Leibo, Karl Tuyls, et al.","author":"Sunehag Peter","year":"2017","unstructured":"Peter Sunehag , Guy Lever , Audrunas Gruslys , Wojciech Marian Czarnecki , Vinicius Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z Leibo, Karl Tuyls, et al. 2017 . Value-decomposition networks for cooperative multi-agent learning. arXiv preprint arXiv:1706.05296 (2017). Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinicius Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z Leibo, Karl Tuyls, et al. 2017. Value-decomposition networks for cooperative multi-agent learning. arXiv preprint arXiv:1706.05296 (2017)."},{"key":"e_1_3_2_2_55_1","volume-title":"Sequence to sequence learning with neural networks. Advances in neural information processing systems","author":"Sutskever Ilya","year":"2014","unstructured":"Ilya Sutskever , Oriol Vinyals , and Quoc V Le. 2014. Sequence to sequence learning with neural networks. Advances in neural information processing systems , Vol. 27 ( 2014 ). Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014. Sequence to sequence learning with neural networks. Advances in neural information processing systems, Vol. 27 (2014)."},{"key":"e_1_3_2_2_56_1","volume-title":"Reinforcement learning: An introduction","author":"Sutton Richard S","unstructured":"Richard S Sutton and Andrew G Barto . 2018. Reinforcement learning: An introduction . MIT press . Richard S Sutton and Andrew G Barto. 2018. Reinforcement learning: An introduction. MIT press."},{"key":"e_1_3_2_2_57_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2015.05.387"},{"key":"e_1_3_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/CCIS.2011.6045081"},{"key":"e_1_3_2_2_59_1","volume-title":"Attention is all you need. Advances in neural information processing systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani , Noam Shazeer , Niki Parmar , Jakob Uszkoreit , Llion Jones , Aidan N Gomez , \u0141ukasz Kaiser , and Illia Polosukhin . 2017. Attention is all you need. Advances in neural information processing systems , Vol. 30 ( 2017 ). Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_2_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/2523616.2523633"},{"key":"e_1_3_2_2_61_1","doi-asserted-by":"publisher","DOI":"10.5555\/1756006.1859891"},{"key":"e_1_3_2_2_62_1","volume-title":"Machine learning","author":"Watkins Christopher JCH","year":"1992","unstructured":"Christopher JCH Watkins and Peter Dayan . 1992. Q-learning. Machine learning , Vol. 8 , 3 ( 1992 ), 279--292. Christopher JCH Watkins and Peter Dayan. 1992. Q-learning. Machine learning, Vol. 8, 3 (1992), 279--292."},{"key":"e_1_3_2_2_63_1","volume-title":"19th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 22).","author":"Weng Qizhen","unstructured":"Qizhen Weng , Wencong Xiao , Yinghao Yu , Wei Wang , Cheng Wang , Jian He , Yong Li , Liping Zhang , Wei Lin , and Yu Ding . 2022. MLaaS in the Wild: Workload Analysis and Scheduling in Large-Scale Heterogeneous GPU Clusters . In 19th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 22). Qizhen Weng, Wencong Xiao, Yinghao Yu, Wei Wang, Cheng Wang, Jian He, Yong Li, Liping Zhang, Wei Lin, and Yu Ding. 2022. MLaaS in the Wild: Workload Analysis and Scheduling in Large-Scale Heterogeneous GPU Clusters. In 19th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 22)."},{"key":"e_1_3_2_2_64_1","volume-title":"Machine Learning: Proceedings of the Seventeenth International Conference (ICML'2000)","author":"Marco","unstructured":"Marco A Wiering et al. 2000. Multi-agent reinforcement learning for traffic light control . In Machine Learning: Proceedings of the Seventeenth International Conference (ICML'2000) . 1151--1158. Marco A Wiering et al. 2000. Multi-agent reinforcement learning for traffic light control. In Machine Learning: Proceedings of the Seventeenth International Conference (ICML'2000). 1151--1158."},{"key":"e_1_3_2_2_65_1","volume-title":"Gaussian processes for machine learning","author":"Williams Christopher KI","unstructured":"Christopher KI Williams and Carl Edward Rasmussen . 2006. Gaussian processes for machine learning . Vol. 2 . MIT press Cambridge , MA. Christopher KI Williams and Carl Edward Rasmussen. 2006. Gaussian processes for machine learning. Vol. 2. MIT press Cambridge, MA."},{"key":"e_1_3_2_2_66_1","volume-title":"Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine learning","author":"Williams Ronald J","year":"1992","unstructured":"Ronald J Williams . 1992. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine learning , Vol. 8 , 3 ( 1992 ), 229--256. Ronald J Williams. 1992. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine learning, Vol. 8, 3 (1992), 229--256."},{"key":"e_1_3_2_2_67_1","unstructured":"Andrew Gordon Wilson Zhiting Hu Ruslan Salakhutdinov and Eric P Xing. 2016a. Deep kernel learning. In Artificial intelligence and statistics. PMLR 370--378.  Andrew Gordon Wilson Zhiting Hu Ruslan Salakhutdinov and Eric P Xing. 2016a. Deep kernel learning. In Artificial intelligence and statistics. PMLR 370--378."},{"key":"e_1_3_2_2_68_1","volume-title":"Stochastic variational deep kernel learning. Advances in neural information processing systems","author":"Wilson Andrew G","year":"2016","unstructured":"Andrew G Wilson , Zhiting Hu , Russ R Salakhutdinov , and Eric P Xing . 2016b. Stochastic variational deep kernel learning. Advances in neural information processing systems , Vol. 29 ( 2016 ). Andrew G Wilson, Zhiting Hu, Russ R Salakhutdinov, and Eric P Xing. 2016b. Stochastic variational deep kernel learning. Advances in neural information processing systems, Vol. 29 (2016)."},{"key":"e_1_3_2_2_69_1","volume-title":"Wilcoxon signed-rank test","author":"Woolson Robert F","year":"2007","unstructured":"Robert F Woolson . 2007. Wilcoxon signed-rank test . Wiley encyclopedia of clinical trials ( 2007 ), 1--3. Robert F Woolson. 2007. Wilcoxon signed-rank test. Wiley encyclopedia of clinical trials (2007), 1--3."},{"key":"e_1_3_2_2_70_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICHI52183.2021.00022"},{"key":"e_1_3_2_2_71_1","volume-title":"14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20)","author":"Xiao Wencong","year":"2020","unstructured":"Wencong Xiao , Shiru Ren , Yong Li , Yang Zhang , Pengyang Hou , Zhi Li , Yihui Feng , Wei Lin , and Yangqing Jia . 2020 . {AntMan}: Dynamic Scaling on {GPU} Clusters for Deep Learning . In 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20) . 533--548. Wencong Xiao, Shiru Ren, Yong Li, Yang Zhang, Pengyang Hou, Zhi Li, Yihui Feng, Wei Lin, and Yangqing Jia. 2020. {AntMan}: Dynamic Scaling on {GPU} Clusters for Deep Learning. In 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20). 533--548."},{"key":"e_1_3_2_2_72_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467079"},{"key":"e_1_3_2_2_73_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/80"},{"key":"e_1_3_2_2_74_1","volume-title":"Analysis of Resource Management Methods Based on Reinforcement Learning. In 2021 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS). IEEE, 27--31","author":"Xing Mingzhe","year":"2021","unstructured":"Mingzhe Xing , Ziyun Wang , and Zhen Xiao . 2021 b. Analysis of Resource Management Methods Based on Reinforcement Learning. In 2021 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS). IEEE, 27--31 . 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The surprising effectiveness of ppo in cooperative, multi-agent games. arXiv preprint arXiv:2103.01955 ( 2021 ). Chao Yu, Akash Velu, Eugene Vinitsky, Yu Wang, Alexandre Bayen, and Yi Wu. 2021. The surprising effectiveness of ppo in cooperative, multi-agent games. arXiv preprint arXiv:2103.01955 (2021)."},{"key":"e_1_3_2_2_78_1","volume-title":"2nd USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 10)","author":"Zaharia Matei","year":"2010","unstructured":"Matei Zaharia , Mosharaf Chowdhury , Michael J Franklin , Scott Shenker , and Ion Stoica . 2010 . Spark: Cluster computing with working sets . In 2nd USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 10) . Matei Zaharia, Mosharaf Chowdhury, Michael J Franklin, Scott Shenker, and Ion Stoica. 2010. Spark: Cluster computing with working sets. 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