{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T14:21:31Z","timestamp":1761402091115,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":64,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,11,4]],"date-time":"2020-11-04T00:00:00Z","timestamp":1604448000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Israel Science Foundation","award":["ISF-759\/19"],"award-info":[{"award-number":["ISF-759\/19"]}]},{"name":"Israel Science Foundation (ISF)","award":["ISF"],"award-info":[{"award-number":["ISF"]}]},{"name":"NSF-BSF grant","award":["BSF-2019798"],"award-info":[{"award-number":["BSF-2019798"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,11,4]]},"DOI":"10.1145\/3422604.3425940","type":"proceedings-article","created":{"date-parts":[[2020,10,30]],"date-time":"2020-10-30T00:50:36Z","timestamp":1604019036000},"page":"88-95","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Online Safety Assurance for Learning-Augmented Systems"],"prefix":"10.1145","author":[{"given":"Noga H.","family":"Rotman","sequence":"first","affiliation":[{"name":"Hebrew University of Jerusalem, Jerusalem, Israel"}]},{"given":"Michael","family":"Schapira","sequence":"additional","affiliation":[{"name":"Hebrew University of Jerusalem, Jerusalem, Israel"}]},{"given":"Aviv","family":"Tamar","sequence":"additional","affiliation":[{"name":"Technion, Haifa, Israel"}]}],"member":"320","published-online":{"date-parts":[[2020,11,4]]},"reference":[{"volume-title":"http:\/\/mediapm.edgesuite.net\/dash\/public\/nightly\/samples\/dash-if-reference-player\/index.html","year":"2016","key":"e_1_3_2_1_1_1","unstructured":"Dash industry form. http:\/\/mediapm.edgesuite.net\/dash\/public\/nightly\/samples\/dash-if-reference-player\/index.html , 2016 . Accessed : 2020. Dash industry form. http:\/\/mediapm.edgesuite.net\/dash\/public\/nightly\/samples\/dash-if-reference-player\/index.html, 2016. Accessed: 2020."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3230543.3230558"},{"key":"e_1_3_2_1_3_1","volume-title":"Variational autoencoder based anomaly detection using reconstruction probability. Special Lecture on IE, 2(1)","author":"An J.","year":"2015","unstructured":"J. An and S. Cho . Variational autoencoder based anomaly detection using reconstruction probability. Special Lecture on IE, 2(1) , 2015 . J. An and S. Cho. Variational autoencoder based anomaly detection using reconstruction probability. Special Lecture on IE, 2(1), 2015."},{"key":"e_1_3_2_1_4_1","volume-title":"Invariant risk minimization. arXiv preprint arXiv:1907.02893","author":"Arjovsky M.","year":"2019","unstructured":"M. Arjovsky , L. Bottou , I. Gulrajani , and D. Lopez-Paz . Invariant risk minimization. arXiv preprint arXiv:1907.02893 , 2019 . M. Arjovsky, L. Bottou, I. Gulrajani, and D. Lopez-Paz. Invariant risk minimization. arXiv preprint arXiv:1907.02893, 2019."},{"key":"e_1_3_2_1_5_1","volume-title":"Adaptive control","author":"\u00c5str\u00f6m K. J.","year":"2013","unstructured":"K. J. \u00c5str\u00f6m and B. Wittenmark . Adaptive control . Courier Corporation , 2013 . K. J. \u00c5str\u00f6m and B. Wittenmark. Adaptive control. Courier Corporation, 2013."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2006.883010"},{"key":"e_1_3_2_1_7_1","first-page":"1471","volume-title":"Advances in neural information processing systems","author":"Bellemare M.","year":"2016","unstructured":"M. Bellemare , S. Srinivasan , G. Ostrovski , T. Schaul , D. Saxton , and R. Munos . Unifying count-based exploration and intrinsic motivation . In Advances in neural information processing systems , pages 1471 -- 1479 , 2016 . M. Bellemare, S. Srinivasan, G. Ostrovski, T. Schaul, D. Saxton, and R. Munos. Unifying count-based exploration and intrinsic motivation. In Advances in neural information processing systems, pages 1471--1479, 2016."},{"key":"e_1_3_2_1_8_1","first-page":"137","volume-title":"Advances in neural information processing systems","author":"Ben-David S.","year":"2007","unstructured":"S. Ben-David , J. Blitzer , K. Crammer , and F. Pereira . Analysis of representations for domain adaptation . In Advances in neural information processing systems , pages 137 -- 144 , 2007 . S. Ben-David, J. Blitzer, K. Crammer, and F. Pereira. Analysis of representations for domain adaptation. In Advances in neural information processing systems, pages 137--144, 2007."},{"key":"e_1_3_2_1_9_1","volume-title":"Dynamic programming and optimal control","author":"Bertsekas D. P.","year":"1995","unstructured":"D. P. Bertsekas . Dynamic programming and optimal control , volume 1 . Athena scientific Belmont, MA , 1995 . D. P. Bertsekas. Dynamic programming and optimal control, volume 1. Athena scientific Belmont, MA, 1995."},{"key":"e_1_3_2_1_10_1","volume-title":"Exploration by random network distillation. arXiv preprint arXiv:1810.12894","author":"Burda Y.","year":"2018","unstructured":"Y. Burda , H. Edwards , A. Storkey , and O. Klimov . Exploration by random network distillation. arXiv preprint arXiv:1810.12894 , 2018 . Y. Burda, H. Edwards, A. Storkey, and O. Klimov. Exploration by random network distillation. arXiv preprint arXiv:1810.12894, 2018."},{"key":"e_1_3_2_1_11_1","volume-title":"Deep learning for anomaly detection: A survey. CoRR, abs\/1901.03407","author":"Chalapathy R.","year":"2019","unstructured":"R. Chalapathy and S. Chawla . Deep learning for anomaly detection: A survey. CoRR, abs\/1901.03407 , 2019 . R. Chalapathy and S. Chawla. Deep learning for anomaly detection: A survey. CoRR, abs\/1901.03407, 2019."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3230543.3230551"},{"key":"e_1_3_2_1_13_1","volume-title":"Off-policy deep reinforcement learning without exploration. arXiv preprint arXiv:1812.02900","author":"Fujimoto S.","year":"2018","unstructured":"S. Fujimoto , D. Meger , and D. Precup . Off-policy deep reinforcement learning without exploration. arXiv preprint arXiv:1812.02900 , 2018 . S. Fujimoto, D. Meger, and D. Precup. Off-policy deep reinforcement learning without exploration. arXiv preprint arXiv:1812.02900, 2018."},{"key":"e_1_3_2_1_14_1","first-page":"2298","volume-title":"Advances in Neural Information Processing Systems","author":"Ghavamzadeh M.","year":"2016","unstructured":"M. Ghavamzadeh , M. Petrik , and Y. Chow . Safe policy improvement by minimizing robust baseline regret . In Advances in Neural Information Processing Systems , pages 2298 -- 2306 , 2016 . M. Ghavamzadeh, M. Petrik, and Y. Chow. Safe policy improvement by minimizing robust baseline regret. In Advances in Neural Information Processing Systems, pages 2298--2306, 2016."},{"key":"e_1_3_2_1_15_1","volume-title":"On calibration of modern neural networks. arXiv preprint arXiv:1706.04599","author":"Guo C.","year":"2017","unstructured":"C. Guo , G. Pleiss , Y. Sun , and K. Q. Weinberger . On calibration of modern neural networks. arXiv preprint arXiv:1706.04599 , 2017 . C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger. On calibration of modern neural networks. arXiv preprint arXiv:1706.04599, 2017."},{"key":"e_1_3_2_1_16_1","volume-title":"Thirty-First AAAI Conference on Artificial Intelligence","author":"Hanna J. P.","year":"2017","unstructured":"J. P. Hanna , P. Stone , and S. Niekum . Bootstrapping with models: Confidence intervals for off-policy evaluation . In Thirty-First AAAI Conference on Artificial Intelligence , 2017 . J. P. Hanna, P. Stone, and S. Niekum. Bootstrapping with models: Confidence intervals for off-policy evaluation. In Thirty-First AAAI Conference on Artificial Intelligence, 2017."},{"key":"e_1_3_2_1_17_1","first-page":"1109","volume-title":"Advances in Neural Information Processing Systems","author":"Houthooft R.","year":"2016","unstructured":"R. Houthooft , X. Chen , Y. Duan , J. Schulman , F. De Turck , and P. Abbeel . Vime: Variational information maximizing exploration . In Advances in Neural Information Processing Systems , pages 1109 -- 1117 , 2016 . R. Houthooft, X. Chen, Y. Duan, J. Schulman, F. De Turck, and P. Abbeel. Vime: Variational information maximizing exploration. In Advances in Neural Information Processing Systems, pages 1109--1117, 2016."},{"key":"e_1_3_2_1_18_1","volume-title":"Adversarial attacks on neural network policies. arXiv preprint arXiv:1702.02284","author":"Huang S.","year":"2017","unstructured":"S. Huang , N. Papernot , I. Goodfellow , Y. Duan , and P. Abbeel . Adversarial attacks on neural network policies. arXiv preprint arXiv:1702.02284 , 2017 . S. Huang, N. Papernot, I. Goodfellow, Y. Duan, and P. Abbeel. Adversarial attacks on neural network policies. arXiv preprint arXiv:1702.02284, 2017."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/2619239.2626296"},{"key":"e_1_3_2_1_20_1","first-page":"3050","volume-title":"International Conference on Machine Learning","author":"Jay N.","year":"2019","unstructured":"N. Jay , N. Rotman , B. Godfrey , M. Schapira , and A. Tamar . A deep reinforcement learning perspective on internet congestion control . In International Conference on Machine Learning , pages 3050 -- 3059 , 2019 . N. Jay, N. Rotman, B. Godfrey, M. Schapira, and A. Tamar. A deep reinforcement learning perspective on internet congestion control. In International Conference on Machine Learning, pages 3050--3059, 2019."},{"key":"e_1_3_2_1_21_1","volume-title":"Obstacle tower: A generalization challenge in vision, control, and planning. arXiv preprint arXiv:1902.01378","author":"Juliani A.","year":"2019","unstructured":"A. Juliani , A. Khalifa , V.-P. Berges , J. Harper , E. Teng , H. Henry , A. Crespi , J. Togelius , and D. Lange . Obstacle tower: A generalization challenge in vision, control, and planning. arXiv preprint arXiv:1902.01378 , 2019 . A. Juliani, A. Khalifa, V.-P. Berges, J. Harper, E. Teng, H. Henry, A. Crespi, J. Togelius, and D. Lange. Obstacle tower: A generalization challenge in vision, control, and planning. arXiv preprint arXiv:1902.01378, 2019."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2020.3000415"},{"key":"e_1_3_2_1_23_1","volume-title":"On information and sufficiency. The annals of mathematical statistics, 22(1):79--86","author":"Kullback S.","year":"1951","unstructured":"S. Kullback and R. A. Leibler . On information and sufficiency. The annals of mathematical statistics, 22(1):79--86 , 1951 . S. Kullback and R. A. Leibler. On information and sufficiency. The annals of mathematical statistics, 22(1):79--86, 1951."},{"key":"e_1_3_2_1_24_1","first-page":"11761","volume-title":"Advances in Neural Information Processing Systems","author":"Kumar A.","year":"2019","unstructured":"A. Kumar , J. Fu , M. Soh , G. Tucker , and S. Levine . Stabilizing off-policy q-learning via bootstrapping error reduction . In Advances in Neural Information Processing Systems , pages 11761 -- 11771 , 2019 . A. Kumar, J. Fu, M. Soh, G. Tucker, and S. Levine. Stabilizing off-policy q-learning via bootstrapping error reduction. In Advances in Neural Information Processing Systems, pages 11761--11771, 2019."},{"key":"e_1_3_2_1_25_1","volume-title":"Offline reinforcement learning: Tutorial, review, and perspectives on open problems. arXiv preprint arXiv:2005.01643","author":"Levine S.","year":"2020","unstructured":"S. Levine , A. Kumar , G. Tucker , and J. Fu . Offline reinforcement learning: Tutorial, review, and perspectives on open problems. arXiv preprint arXiv:2005.01643 , 2020 . S. Levine, A. Kumar, G. Tucker, and J. Fu. Offline reinforcement learning: Tutorial, review, and perspectives on open problems. arXiv preprint arXiv:2005.01643, 2020."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3341302.3342221"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3098822.3098843"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3341302.3342080"},{"key":"e_1_3_2_1_29_1","first-page":"1928","volume-title":"International conference on machine learning","author":"Mnih V.","year":"2016","unstructured":"V. Mnih , A. P. Badia , M. Mirza , A. Graves , T. Lillicrap , T. Harley , D. Silver , and K. Kavukcuoglu . Asynchronous methods for deep reinforcement learning . In International conference on machine learning , pages 1928 -- 1937 , 2016 . V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu. Asynchronous methods for deep reinforcement learning. In International conference on machine learning, pages 1928--1937, 2016."},{"key":"e_1_3_2_1_30_1","first-page":"417","volume-title":"2015 USENIX Annual Technical Conference (USENIX ATC 15)","author":"Netravali R.","year":"2015","unstructured":"R. Netravali , A. Sivaraman , S. Das , A. Goyal , K. Winstein , J. Mickens , and H. Balakrishnan . Mahimahi: Accurate record-and-replay for http . In 2015 USENIX Annual Technical Conference (USENIX ATC 15) , pages 417 -- 429 , 2015 . R. Netravali, A. Sivaraman, S. Das, A. Goyal, K. Winstein, J. Mickens, and H. Balakrishnan. Mahimahi: Accurate record-and-replay for http. In 2015 USENIX Annual Technical Conference (USENIX ATC 15), pages 417--429, 2015."},{"key":"e_1_3_2_1_31_1","volume-title":"Gotta learn fast: A new benchmark for generalization in rl. arXiv preprint arXiv:1804.03720","author":"Nichol A.","year":"2018","unstructured":"A. Nichol , V. Pfau , C. Hesse , O. Klimov , and J. Schulman . Gotta learn fast: A new benchmark for generalization in rl. arXiv preprint arXiv:1804.03720 , 2018 . A. Nichol, V. Pfau, C. Hesse, O. Klimov, and J. Schulman. Gotta learn fast: A new benchmark for generalization in rl. arXiv preprint arXiv:1804.03720, 2018."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2019.2904350"},{"key":"e_1_3_2_1_33_1","first-page":"8617","volume-title":"Advances in Neural Information Processing Systems","author":"Osband I.","year":"2018","unstructured":"I. Osband , J. Aslanides , and A. Cassirer . Randomized prior functions for deep reinforcement learning . In Advances in Neural Information Processing Systems , pages 8617 -- 8629 , 2018 . I. Osband, J. Aslanides, and A. Cassirer. Randomized prior functions for deep reinforcement learning. In Advances in Neural Information Processing Systems, pages 8617--8629, 2018."},{"key":"e_1_3_2_1_34_1","first-page":"4026","volume-title":"Advances in neural information processing systems","author":"Osband I.","year":"2016","unstructured":"I. Osband , C. Blundell , A. Pritzel , and B. Van Roy . Deep exploration via bootstrapped dqn . In Advances in neural information processing systems , pages 4026 -- 4034 , 2016 . I. Osband, C. Blundell, A. Pritzel, and B. Van Roy. Deep exploration via bootstrapped dqn. In Advances in neural information processing systems, pages 4026--4034, 2016."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2006.890271"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.70"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.5555\/3237383.3238064"},{"key":"e_1_3_2_1_39_1","first-page":"63","volume-title":"Summer School on Machine Learning","author":"Rasmussen C. E.","year":"2003","unstructured":"C. E. Rasmussen . Gaussian processes in machine learning. In Summer School on Machine Learning , pages 63 -- 71 . Springer , 2003 . C. E. Rasmussen. Gaussian processes in machine learning. In Summer School on Machine Learning, pages 63--71. Springer, 2003."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/2483977.2483991"},{"key":"e_1_3_2_1_41_1","volume-title":"Safe policy learning from observations. arXiv preprint arXiv:1805.07805","author":"Sarafian E.","year":"2018","unstructured":"E. Sarafian , A. Tamar , and S. Kraus . Safe policy learning from observations. arXiv preprint arXiv:1805.07805 , 2018 . E. Sarafian, A. Tamar, and S. Kraus. Safe policy learning from observations. arXiv preprint arXiv:1805.07805, 2018."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAMD.2010.2056368"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1162\/089976601750264965"},{"key":"e_1_3_2_1_45_1","first-page":"1889","volume-title":"International conference on machine learning","author":"Schulman J.","year":"2015","unstructured":"J. Schulman , S. Levine , P. Abbeel , M. Jordan , and P. Moritz . Trust region policy optimization . In International conference on machine learning , pages 1889 -- 1897 , 2015 . J. Schulman, S. Levine, P. Abbeel, M. Jordan, and P. Moritz. Trust region policy optimization. In International conference on machine learning, pages 1889--1897, 2015."},{"key":"e_1_3_2_1_46_1","volume-title":"Linear regression analysis","author":"Seber G. A.","year":"2012","unstructured":"G. A. Seber and A. J. Lee . Linear regression analysis , volume 329 . John Wiley & Sons , 2012 . G. A. Seber and A. J. Lee. Linear regression analysis, volume 329. John Wiley & Sons, 2012."},{"key":"e_1_3_2_1_47_1","volume-title":"Planning to explore via self-supervised world models. arXiv preprint arXiv:2005.05960","author":"Sekar R.","year":"2020","unstructured":"R. Sekar , O. Rybkin , K. Daniilidis , P. Abbeel , D. Hafner , and D. Pathak . Planning to explore via self-supervised world models. arXiv preprint arXiv:2005.05960 , 2020 . R. Sekar, O. Rybkin, K. Daniilidis, P. Abbeel, D. Hafner, and D. Pathak. Planning to explore via self-supervised world models. arXiv preprint arXiv:2005.05960, 2020."},{"key":"e_1_3_2_1_48_1","volume-title":"Learning neural random fields with inclusive auxiliary generators. ArXiv, abs\/1806.00271","author":"Song Y.","year":"2018","unstructured":"Y. Song and Z. Ou . Learning neural random fields with inclusive auxiliary generators. ArXiv, abs\/1806.00271 , 2018 . Y. Song and Z. Ou. Learning neural random fields with inclusive auxiliary generators. ArXiv, abs\/1806.00271, 2018."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/2934872.2934898"},{"key":"e_1_3_2_1_50_1","volume-title":"Reinforcement learning: An introduction","author":"Sutton R. S.","year":"2018","unstructured":"R. S. Sutton and A. G. Barto . Reinforcement learning: An introduction . MIT press , 2018 . R. S. Sutton and A. G. Barto. Reinforcement learning: An introduction. MIT press, 2018."},{"key":"e_1_3_2_1_51_1","first-page":"2154","volume-title":"Advances in Neural Information Processing Systems","author":"Tamar A.","year":"2016","unstructured":"A. Tamar , Y. Wu , G. Thomas , S. Levine , and P. Abbeel . Value iteration networks . In Advances in Neural Information Processing Systems , pages 2154 -- 2162 , 2016 . A. Tamar, Y. Wu, G. Thomas, S. Levine, and P. Abbeel. Value iteration networks. In Advances in Neural Information Processing Systems, pages 2154--2162, 2016."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1023\/B:MACH.0000008084.60811.49"},{"key":"e_1_3_2_1_53_1","volume-title":"Transfer learning for reinforcement learning domains: A survey. Journal of Machine Learning Research, 10(Jul):1633--1685","author":"Taylor M. E.","year":"2009","unstructured":"M. E. Taylor and P. Stone . Transfer learning for reinforcement learning domains: A survey. Journal of Machine Learning Research, 10(Jul):1633--1685 , 2009 . M. E. Taylor and P. Stone. Transfer learning for reinforcement learning domains: A survey. Journal of Machine Learning Research, 10(Jul):1633--1685, 2009."},{"key":"e_1_3_2_1_54_1","first-page":"2139","volume-title":"International Conference on Machine Learning","author":"Thomas P.","year":"2016","unstructured":"P. Thomas and E. Brunskill . Data-efficient off-policy policy evaluation for reinforcement learning . In International Conference on Machine Learning , pages 2139 -- 2148 , 2016 . P. Thomas and E. Brunskill. Data-efficient off-policy policy evaluation for reinforcement learning. In International Conference on Machine Learning, pages 2139--2148, 2016."},{"key":"e_1_3_2_1_55_1","first-page":"2380","volume-title":"International Conference on Machine Learning","author":"Thomas P.","year":"2015","unstructured":"P. Thomas , G. Theocharous , and M. Ghavamzadeh . High confidence policy improvement . In International Conference on Machine Learning , pages 2380 -- 2388 , 2015 . P. Thomas, G. Theocharous, and M. Ghavamzadeh. High confidence policy improvement. In International Conference on Machine Learning, pages 2380--2388, 2015."},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.5555\/2888116.2888134"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/3152434.3152441"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2016.2601087"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"crossref","unstructured":"P. Virtanen R. Gommers T. E. Oliphant M. Haberland T. Reddy D. Cournapeau E. Burovski P. Peterson W. Weckesser J. Bright S. J. van der Walt M. Brett J. Wilson K. Jarrod Millman N. Mayorov A. R. J. Nelson E. Jones R. Kern E. Larson C. Carey . I. Polat Y. Feng E. W. Moore J. Vand erPlas D. Laxalde J. Perktold R. Cimrman I. Henriksen E. A. Quintero C. R. Harris A. M. Archibald A. H. Ribeiro F. Pedregosa P. van Mulbregt and S... Contributors. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods 17:261--272 2020.  P. Virtanen R. Gommers T. E. Oliphant M. Haberland T. Reddy D. Cournapeau E. Burovski P. Peterson W. Weckesser J. Bright S. J. van der Walt M. Brett J. Wilson K. Jarrod Millman N. Mayorov A. R. J. Nelson E. Jones R. Kern E. Larson C. Carey . I. Polat Y. Feng E. W. Moore J. Vand erPlas D. Laxalde J. Perktold R. Cimrman I. Henriksen E. A. Quintero C. R. Harris A. M. Archibald A. H. Ribeiro F. Pedregosa P. van Mulbregt and S... Contributors. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods 17:261--272 2020.","DOI":"10.1038\/s41592-020-0772-5"},{"key":"e_1_3_2_1_60_1","volume-title":"Safer classification by synthesis. CoRR, abs\/1711.08534","author":"Wang W.","year":"2017","unstructured":"W. Wang , A. Wang , A. Tamar , X. Chen , and P. Abbeel . Safer classification by synthesis. CoRR, abs\/1711.08534 , 2017 . W. Wang, A. Wang, A. Tamar, X. Chen, and P. Abbeel. Safer classification by synthesis. CoRR, abs\/1711.08534, 2017."},{"key":"e_1_3_2_1_61_1","first-page":"495","volume-title":"17th {USENIX} Symposium on Networked Systems Design and Implementation ($$NSDI$$ 20)","author":"Yan F. Y.","year":"2020","unstructured":"F. Y. Yan , H. Ayers , C. Zhu , S. Fouladi , J. Hong , K. Zhang , P. Levis , and K. Winstein . Learning in situ: a randomized experiment in video streaming . In 17th {USENIX} Symposium on Networked Systems Design and Implementation ($$NSDI$$ 20) , pages 495 -- 511 , 2020 . F. Y. Yan, H. Ayers, C. Zhu, S. Fouladi, J. Hong, K. Zhang, P. Levis, and K. Winstein. Learning in situ: a randomized experiment in video streaming. In 17th {USENIX} Symposium on Networked Systems Design and Implementation ($$NSDI$$ 20), pages 495--511, 2020."},{"key":"e_1_3_2_1_62_1","volume-title":"Pantheon: the training ground for internet congestion-control research. Measurement at http:\/\/pantheon. stanford. edu\/result\/1622","author":"Yan F. Y.","year":"2018","unstructured":"F. Y. Yan , J. Ma , G. Hill , D. Raghavan , R. S. Wahby , P. Levis , and K. Winstein . Pantheon: the training ground for internet congestion-control research. Measurement at http:\/\/pantheon. stanford. edu\/result\/1622 , 2018 . F. Y. Yan, J. Ma, G. Hill, D. Raghavan, R. S. Wahby, P. Levis, and K. Winstein. Pantheon: the training ground for internet congestion-control research. Measurement at http:\/\/pantheon. stanford. edu\/result\/1622, 2018."},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/2785956.2787486"},{"key":"e_1_3_2_1_64_1","volume-title":"A study on overfitting in deep reinforcement learning. arXiv preprint arXiv:1804.06893","author":"Zhang C.","year":"2018","unstructured":"C. Zhang , O. Vinyals , R. Munos , and S. Bengio . A study on overfitting in deep reinforcement learning. arXiv preprint arXiv:1804.06893 , 2018 . C. Zhang, O. Vinyals, R. Munos, and S. Bengio. A study on overfitting in deep reinforcement learning. arXiv preprint arXiv:1804.06893, 2018."}],"event":{"name":"HotNets '20: The 19th ACM Workshop on Hot Topics in Networks","sponsor":["SIGCOMM ACM Special Interest Group on Data Communication"],"location":"Virtual Event USA","acronym":"HotNets '20"},"container-title":["Proceedings of the 19th ACM Workshop on Hot Topics in Networks"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3422604.3425940","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3422604.3425940","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:31:29Z","timestamp":1750195889000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3422604.3425940"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,4]]},"references-count":64,"alternative-id":["10.1145\/3422604.3425940","10.1145\/3422604"],"URL":"https:\/\/doi.org\/10.1145\/3422604.3425940","relation":{},"subject":[],"published":{"date-parts":[[2020,11,4]]},"assertion":[{"value":"2020-11-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}