{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T11:20:36Z","timestamp":1764588036905,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":67,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,10,15]],"date-time":"2019-10-15T00:00:00Z","timestamp":1571097600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100012659","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61625203, 61832013, 6187242"],"award-info":[{"award-number":["61625203, 61832013, 6187242"]}],"id":[{"id":"10.13039\/501100012659","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key R&D Program of China","award":["2017YFB0801701"],"award-info":[{"award-number":["2017YFB0801701"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,10,15]]},"DOI":"10.1145\/3343031.3350866","type":"proceedings-article","created":{"date-parts":[[2019,10,21]],"date-time":"2019-10-21T16:32:26Z","timestamp":1571675546000},"page":"2431-2439","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["PiTree"],"prefix":"10.1145","author":[{"given":"Zili","family":"Meng","sequence":"first","affiliation":[{"name":"Tsinghua University & Beijing National Research Center for Information Science and Technology, Beijing, China"}]},{"given":"Jing","family":"Chen","sequence":"additional","affiliation":[{"name":"Tsinghua University & Beijing National Research Center for Information Science and Technology, Beijing, China"}]},{"given":"Yaning","family":"Guo","sequence":"additional","affiliation":[{"name":"Tsinghua University & Beijing National Research Center for Information Science and Technology, Beijing, China"}]},{"given":"Chen","family":"Sun","sequence":"additional","affiliation":[{"name":"Tsinghua University & Beijing National Research Center for Information Science and Technology, Beijing, China"}]},{"given":"Hongxin","family":"Hu","sequence":"additional","affiliation":[{"name":"Clemson University, Clemson, SC, USA"}]},{"given":"Mingwei","family":"Xu","sequence":"additional","affiliation":[{"name":"Tsinghua University & Beijing National Research Center for Information Science and Technology, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2019,10,15]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Chrome devtools. https:\/\/developers.google.com\/web\/tools\/chrome-devtools\/.  Chrome devtools. https:\/\/developers.google.com\/web\/tools\/chrome-devtools\/."},{"key":"e_1_3_2_1_2_1","unstructured":"Cplex optimizer. https:\/\/www.ibm.com\/analytics\/cplex-optimizer.  Cplex optimizer. https:\/\/www.ibm.com\/analytics\/cplex-optimizer."},{"key":"e_1_3_2_1_3_1","unstructured":"Ec2 instance pricing -- amazon web services (aws). https:\/\/aws.amazon.com\/ec2\/pricing\/reserved-instances\/pricing\/.  Ec2 instance pricing -- amazon web services (aws). https:\/\/aws.amazon.com\/ec2\/pricing\/reserved-instances\/pricing\/."},{"key":"e_1_3_2_1_4_1","unstructured":"Mobile accounted for 62 percent of online video views. https:\/\/www.statista.com\/statistics\/444318\/mobile-device-video-views-share\/.  Mobile accounted for 62 percent of online video views. https:\/\/www.statista.com\/statistics\/444318\/mobile-device-video-views-share\/."},{"key":"e_1_3_2_1_5_1","unstructured":"Official youtube blog: With nearly 2 million concurrent viewers and over 3 million live watch hours first presidential debate breaks political record. https:\/\/youtube.googleblog.com\/2016\/09\/with-nearly-2-million-concurrent.html.  Official youtube blog: With nearly 2 million concurrent viewers and over 3 million live watch hours first presidential debate breaks political record. https:\/\/youtube.googleblog.com\/2016\/09\/with-nearly-2-million-concurrent.html."},{"key":"e_1_3_2_1_6_1","unstructured":"Tornado web server. https:\/\/www.tornadoweb.org\/.  Tornado web server. https:\/\/www.tornadoweb.org\/."},{"key":"e_1_3_2_1_7_1","unstructured":"tsenart\/vegeta: Http load testing tool and library. it's over 9000! https:\/\/github.com\/tsenart\/vegeta.  tsenart\/vegeta: Http load testing tool and library. it's over 9000! https:\/\/github.com\/tsenart\/vegeta."},{"key":"e_1_3_2_1_8_1","unstructured":"Youtube. https:\/\/www.youtube.com\/.  Youtube. https:\/\/www.youtube.com\/."},{"key":"e_1_3_2_1_9_1","volume-title":"- business of apps","author":"Youtube","year":"2018","unstructured":"Youtube revenue and usage statistics ( 2018 ) - business of apps . http:\/\/www.businessofapps.com\/data\/youtube-statistics\/. Youtube revenue and usage statistics (2018) - business of apps. http:\/\/www.businessofapps.com\/data\/youtube-statistics\/."},{"key":"e_1_3_2_1_10_1","volume-title":"https:\/\/www.fcc.gov\/reports-research\/reports\/measuring-broadband-america\/raw-data-measuring-broadband-america-2016","author":"Raw","year":"2016","unstructured":"Raw data - measuring broadband america. https:\/\/www.fcc.gov\/reports-research\/reports\/measuring-broadband-america\/raw-data-measuring-broadband-america-2016 , 2016 . Raw data - measuring broadband america. https:\/\/www.fcc.gov\/reports-research\/reports\/measuring-broadband-america\/raw-data-measuring-broadband-america-2016, 2016."},{"key":"e_1_3_2_1_11_1","volume-title":"https:\/\/github.com\/Dash-Industry-Forum\/dash.js","author":"Dash","year":"2018","unstructured":"Dash .js. https:\/\/github.com\/Dash-Industry-Forum\/dash.js , 2018 . Dash.js. https:\/\/github.com\/Dash-Industry-Forum\/dash.js, 2018."},{"volume-title":"Minify files with uglifyjs. https:\/\/github.com\/gruntjs\/grunt-contrib-uglify","year":"2019","key":"e_1_3_2_1_12_1","unstructured":"gruntjs\/grunt-contrib-uglify : Minify files with uglifyjs. https:\/\/github.com\/gruntjs\/grunt-contrib-uglify , 2019 . gruntjs\/grunt-contrib-uglify: Minify files with uglifyjs. https:\/\/github.com\/gruntjs\/grunt-contrib-uglify, 2019."},{"key":"e_1_3_2_1_13_1","volume-title":"USENIX OSDI","author":"Abadi M.","year":"2016","unstructured":"Abadi , M. , Barham , P. , Chen , J. , Chen , Z. , Davis , A. , Dean , J. , Devin , M. , Ghemawat , S. , Tensorflow: A system for large-scale machine learning . In USENIX OSDI ( 2016 ). Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., et al. Tensorflow: A system for large-scale machine learning. In USENIX OSDI (2016)."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3230543.3230558"},{"key":"e_1_3_2_1_15_1","volume-title":"Do deep nets really need to be deep? In NIPS","author":"Ba J.","year":"2014","unstructured":"Ba , J. , and Caruana , R . Do deep nets really need to be deep? In NIPS ( 2014 ). Ba, J., and Caruana, R. Do deep nets really need to be deep? In NIPS (2014)."},{"key":"e_1_3_2_1_16_1","volume-title":"NeurIPS","author":"Bastani O.","year":"2018","unstructured":"Bastani , O. , Pu , Y. , and Solar-Lezama , A. Verifiable reinforcement learning via policy extraction . In NeurIPS ( 2018 ). Bastani, O., Pu, Y., and Solar-Lezama, A. Verifiable reinforcement learning via policy extraction. In NeurIPS (2018)."},{"key":"e_1_3_2_1_17_1","unstructured":"Beben A. Wisniewski P. Batalla J. M. and Krawiec P. Abma  Beben A. Wisniewski P. Batalla J. M. and Krawiec P. Abma"},{"volume-title":"ACM MMSys","year":"2016","key":"e_1_3_2_1_18_1","unstructured":": lightweight and efficient algorithm for http adaptive streaming . In ACM MMSys ( 2016 ). : lightweight and efficient algorithm for http adaptive streaming. In ACM MMSys (2016)."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3240508.3240676"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3240508.3240589"},{"key":"e_1_3_2_1_21_1","volume-title":"Top-down induction of first-order logical decision trees. Artificial intelligence 101, 1--2","author":"Blockeel H.","year":"1998","unstructured":"Blockeel , H. , and De Raedt , L. Top-down induction of first-order logical decision trees. Artificial intelligence 101, 1--2 ( 1998 ), 285--297. Blockeel, H., and De Raedt, L. Top-down induction of first-order logical decision trees. Artificial intelligence 101, 1--2 (1998), 285--297."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/1150402.1150464"},{"key":"e_1_3_2_1_23_1","volume-title":"ICML","author":"Chen W.","year":"2015","unstructured":"Chen , W. , Wilson , J. , Tyree , S. , Weinberger , K. , and Chen , Y . Compressing neural networks with the hashing trick . In ICML ( 2015 ). Chen, W., Wilson, J., Tyree, S., Weinberger, K., and Chen, Y. Compressing neural networks with the hashing trick. In ICML (2015)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSSC.2017.2749425"},{"key":"e_1_3_2_1_25_1","volume-title":"Acm multimedia 2019 grand challenge - live video streaming. https:\/\/www.aitrans.online\/MMGC\/","author":"Cui Y.","year":"2019","unstructured":"Cui , Y. , Ooi , W. T. , Liu , J. , Zhang , X. , Bentaleb , A. , Zheng , K. , and Li , Y . Acm multimedia 2019 grand challenge - live video streaming. https:\/\/www.aitrans.online\/MMGC\/ , 2019 . Cui, Y., Ooi, W. T., Liu, J., Zhang, X., Bentaleb, A., Zheng, K., and Li, Y. Acm multimedia 2019 grand challenge - live video streaming. https:\/\/www.aitrans.online\/MMGC\/, 2019."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/PV.2013.6691442"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3304109.3306216"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/2018436.2018478"},{"key":"e_1_3_2_1_29_1","volume-title":"Classification and regression trees","author":"Friedman J. H.","year":"1984","unstructured":"Friedman , J. H. , Olshen , R. A. , Stone , C. J. , Classification and regression trees . Belmont, CA : Wadsworth & Brooks ( 1984 ). Friedman, J. H., Olshen, R. A., Stone, C. J., et al. Classification and regression trees. Belmont, CA: Wadsworth & Brooks (1984)."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/956750.956813"},{"key":"e_1_3_2_1_31_1","first-page":"131","volume-title":"USENIX NSDI","author":"Ganjam A.","year":"2015","unstructured":"Ganjam , A. , Siddiqui , F. , Zhan , J. , Liu , X. , Stoica , I. , Jiang , J. , Sekar , V. , and Zhang , H . C3: Internet-scale control plane for video quality optimization . In USENIX NSDI ( 2015 ), pp. 131 -- 144 . Ganjam, A., Siddiqui, F., Zhan, J., Liu, X., Stoica, I., Jiang, J., Sekar, V., and Zhang, H. C3: Internet-scale control plane for video quality optimization. In USENIX NSDI (2015), pp. 131--144."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3243734.3243792"},{"key":"e_1_3_2_1_33_1","volume-title":"Tiyuntsong: A self-play reinforcement learning approach for abr video streaming. arXiv:1811.06166","author":"Huang T.","year":"2018","unstructured":"Huang , T. , Yao , X. , Wu , C. , Zhang , R.-X. , and Sun , L . Tiyuntsong: A self-play reinforcement learning approach for abr video streaming. arXiv:1811.06166 ( 2018 ). Huang, T., Yao, X., Wu, C., Zhang, R.-X., and Sun, L. Tiyuntsong: A self-play reinforcement learning approach for abr video streaming. arXiv:1811.06166 (2018)."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/2398776.2398800"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/2619239.2626296"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3054912"},{"key":"e_1_3_2_1_37_1","volume-title":"White Paper","author":"Index C. V. N.","year":"2017","unstructured":"Index , C. V. N. Forecast and methodology, 2016--2021 . White Paper , June ( 2017 ). Index, C. V. N. Forecast and methodology, 2016--2021. White Paper, June (2017)."},{"key":"e_1_3_2_1_38_1","first-page":"137","volume-title":"USENIX NSDI","author":"Jiang J.","year":"2016","unstructured":"Jiang , J. , Sekar , V. , Milner , H. , Shepherd , D. , Stoica , I. , and Zhang , H . Cfa: A practical prediction system for video qoe optimization . In USENIX NSDI ( 2016 ), pp. 137 -- 150 . Jiang, J., Sekar, V., Milner, H., Shepherd, D., Stoica, I., and Zhang, H. Cfa: A practical prediction system for video qoe optimization. In USENIX NSDI (2016), pp. 137--150."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/2413176.2413189"},{"key":"e_1_3_2_1_40_1","volume-title":"NIPS","author":"Kakade S. M.","year":"2008","unstructured":"Kakade , S. M. , and Tewari , A . On the generalization ability of online strongly convex programming algorithms . In NIPS ( 2008 ). Kakade, S. M., and Tewari, A. On the generalization ability of online strongly convex programming algorithms. In NIPS (2008)."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3102980.3103008"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/2398776.2398799"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/2750858.2804262"},{"key":"e_1_3_2_1_44_1","volume-title":"ICLR","author":"Li H.","year":"2017","unstructured":"Li , H. , Kadav , A. , Durdanovic , I. , Samet , H. , and Graf , H. P . Pruning filters for efficient convnets . In ICLR ( 2017 ). Li, H., Kadav, A., Durdanovic, I., Samet, H., and Graf, H. P. Pruning filters for efficient convnets. In ICLR (2017)."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2014.140405"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2889999"},{"key":"e_1_3_2_1_47_1","volume-title":"NIPS","author":"Lundberg S. M.","year":"2017","unstructured":"Lundberg , S. M. , and Lee , S . -I. A unified approach to interpreting model predictions . In NIPS ( 2017 ). Lundberg, S. M., and Lee, S.-I. A unified approach to interpreting model predictions. In NIPS (2017)."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3098822.3098843"},{"key":"e_1_3_2_1_49_1","first-page":"417","volume-title":"USENIX ATC","author":"Netravali R.","year":"2015","unstructured":"Netravali , R. , Sivaraman , A. , Das , S. , Goyal , A. , Winstein , K. , Mickens , J. , and Balakrishnan , H . Mahimahi: Accurate record-and-replay for HTTP . In USENIX ATC ( 2015 ), pp. 417 -- 429 . Netravali, R., Sivaraman, A., Das, S., Goyal, A., Winstein, K., Mickens, J., and Balakrishnan, H. Mahimahi: Accurate record-and-replay for HTTP. In USENIX ATC (2015), pp. 417--429."},{"key":"e_1_3_2_1_50_1","first-page":"1","article-title":"Characteristics of mobile youtube traffic","volume":"21","author":"Ramos-Mu","year":"2014","unstructured":"Ramos-Mu noz, J. J., Prados-Garzon , J. , Ameigeiras , P. , Navarro-Ortiz , J. , and L\u00f3pez-Soler , J. M . Characteristics of mobile youtube traffic . IEEE Wireless Communications 21 , 1 ( 2014 ), 18--25. Ramos-Mu noz, J. J., Prados-Garzon, J., Ameigeiras, P., Navarro-Ortiz, J., and L\u00f3pez-Soler, J. M. Characteristics of mobile youtube traffic. IEEE Wireless Communications 21, 1 (2014), 18--25.","journal-title":"IEEE Wireless Communications"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/2483977.2483991"},{"key":"e_1_3_2_1_53_1","volume-title":"AISTATS","author":"Ross S.","year":"2011","unstructured":"Ross , S. , Gordon , G. , and Bagnell , D . A reduction of imitation learning and structured prediction to no-regret online learning . In AISTATS ( 2011 ). Ross, S., Gordon, G., and Bagnell, D. A reduction of imitation learning and structured prediction to no-regret online learning. In AISTATS (2011)."},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICNP.2018.00026"},{"key":"e_1_3_2_1_55_1","volume-title":"SysML","author":"Smilkov D.","year":"2019","unstructured":"Smilkov , D. , Thorat , N. , Assogba , Y. , Nicholson , C. , Kreeger , N. , Yu , P. , Cai , S. , Nielsen , E. , Tensorflow .js : Machine learning for the web and beyond (https:\/\/js.tensorflow.org\/) . In SysML ( 2019 ). Smilkov, D., Thorat, N., Assogba, Y., Nicholson, C., Kreeger, N., Yu, P., Cai, S., Nielsen, E., et al. Tensorflow.js: Machine learning for the web and beyond (https:\/\/js.tensorflow.org\/). In SysML (2019)."},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/3204949.3204953"},{"key":"e_1_3_2_1_57_1","volume-title":"Bola: Near-optimal bitrate adaptation for online videos","author":"Spiteri K.","year":"2016","unstructured":"Spiteri , K. , Urgaonkar , R. , and Sitaraman , R. K . Bola: Near-optimal bitrate adaptation for online videos . In IEEE INFOCOM ( 2016 ). Spiteri, K., Urgaonkar, R., and Sitaraman, R. K. Bola: Near-optimal bitrate adaptation for online videos. In IEEE INFOCOM (2016)."},{"key":"e_1_3_2_1_58_1","first-page":"133","volume-title":"ACM MMSys","author":"Stockhammer T.","year":"2011","unstructured":"Stockhammer , T. Dynamic adaptive streaming over http -- standards and design principles . In ACM MMSys ( 2011 ), pp. 133 -- 144 . Stockhammer, T. Dynamic adaptive streaming over http -- standards and design principles. In ACM MMSys (2011), pp. 133--144."},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/2934872.2934898"},{"key":"e_1_3_2_1_60_1","volume-title":"ICML","author":"Verma A.","year":"2018","unstructured":"Verma , A. , Murali , V. , Singh , R. , Kohli , P. , and Chaudhuri , S . Programmatically interpretable reinforcement learning . In ICML ( 2018 ). Verma, A., Murali, V., Singh, R., Kohli, P., and Chaudhuri, S. Programmatically interpretable reinforcement learning. In ICML (2018)."},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/3123266.3123284"},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/2910017.2910593"},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2014.2334592"},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1145\/3123266.3123390"},{"key":"e_1_3_2_1_65_1","volume-title":"USENIX OSDI","author":"Yeo H.","year":"2018","unstructured":"Yeo , H. , Jung , Y. , Kim , J. , Shin , J. , and Han , D . Neural adaptive content-aware internet video delivery . In USENIX OSDI ( 2018 ). Yeo, H., Jung, Y., Kim, J., Shin, J., and Han, D. Neural adaptive content-aware internet video delivery. In USENIX OSDI (2018)."},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1145\/2785956.2787486"},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1145\/3232565.3232569"}],"event":{"name":"MM '19: The 27th ACM International Conference on Multimedia","sponsor":["SIGMM ACM Special Interest Group on Multimedia"],"location":"Nice France","acronym":"MM '19"},"container-title":["Proceedings of the 27th ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3343031.3350866","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3343031.3350866","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:13:25Z","timestamp":1750202005000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3343031.3350866"}},"subtitle":["Practical Implementation of ABR Algorithms Using Decision Trees"],"short-title":[],"issued":{"date-parts":[[2019,10,15]]},"references-count":67,"alternative-id":["10.1145\/3343031.3350866","10.1145\/3343031"],"URL":"https:\/\/doi.org\/10.1145\/3343031.3350866","relation":{},"subject":[],"published":{"date-parts":[[2019,10,15]]},"assertion":[{"value":"2019-10-15","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}