{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:20:43Z","timestamp":1750220443780,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":51,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,3,8]],"date-time":"2021-03-08T00:00:00Z","timestamp":1615161600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,3,8]]},"DOI":"10.1145\/3437963.3441770","type":"proceedings-article","created":{"date-parts":[[2021,3,6]],"date-time":"2021-03-06T04:34:28Z","timestamp":1615005268000},"page":"76-84","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Beyond Point Estimate: Inferring Ensemble Prediction Variation from Neuron Activation Strength in Recommender Systems"],"prefix":"10.1145","author":[{"given":"Zhe","family":"Chen","sequence":"first","affiliation":[{"name":"Google, Inc., Mountain View, CA, USA"}]},{"given":"Yuyan","family":"Wang","sequence":"additional","affiliation":[{"name":"Google, Inc., Mountain View, CA, USA"}]},{"given":"Dong","family":"Lin","sequence":"additional","affiliation":[{"name":"Google, Inc., Mountain View, CA, USA"}]},{"given":"Derek Zhiyuan","family":"Cheng","sequence":"additional","affiliation":[{"name":"Google, Inc., Mountain View, CA, USA"}]},{"given":"Lichan","family":"Hong","sequence":"additional","affiliation":[{"name":"Google, Inc., Mountain View, CA, USA"}]},{"given":"Ed H.","family":"Chi","sequence":"additional","affiliation":[{"name":"Google, Inc., Mountain View, CA, USA"}]},{"given":"Claire","family":"Cui","sequence":"additional","affiliation":[{"name":"Google, Inc., Mountain View, CA, USA"}]}],"member":"320","published-online":{"date-parts":[[2021,3,8]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553380"},{"key":"e_1_3_2_1_2_1","volume-title":"Proceedings of KDD cup and workshop","volume":"2007","author":"Bennett James","year":"2007","unstructured":"James Bennett , Stan Lanning , 2007 . The netflix prize . In Proceedings of KDD cup and workshop , Vol. 2007 . Citeseer, 35. James Bennett, Stan Lanning, et al. 2007. The netflix prize. In Proceedings of KDD cup and workshop, Vol. 2007. Citeseer, 35."},{"key":"e_1_3_2_1_3_1","volume-title":"Mixmatch: A holistic approach to semi-supervised learning. In Advances in Neural Information Processing Systems. 5049--5059.","author":"Berthelot David","year":"2019","unstructured":"David Berthelot , Nicholas Carlini , Ian Goodfellow , Nicolas Papernot , Avital Oliver , and Colin A Raffel . 2019 . Mixmatch: A holistic approach to semi-supervised learning. In Advances in Neural Information Processing Systems. 5049--5059. David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Raffel. 2019. Mixmatch: A holistic approach to semi-supervised learning. In Advances in Neural Information Processing Systems. 5049--5059."},{"volume-title":"Pattern recognition and machine learning","author":"Bishop Christopher M","key":"e_1_3_2_1_4_1","unstructured":"Christopher M Bishop . 2006. Pattern recognition and machine learning . Springer . Christopher M Bishop. 2006. Pattern recognition and machine learning. Springer."},{"key":"e_1_3_2_1_5_1","volume-title":"Weight uncertainty in neural networks. arXiv preprint arXiv:1505.05424","author":"Blundell Charles","year":"2015","unstructured":"Charles Blundell , Julien Cornebise , Koray Kavukcuoglu , and Daan Wierstra . 2015. Weight uncertainty in neural networks. arXiv preprint arXiv:1505.05424 ( 2015 ). Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra. 2015. Weight uncertainty in neural networks. arXiv preprint arXiv:1505.05424 (2015)."},{"key":"e_1_3_2_1_6_1","volume-title":"Bagging predictors. Machine learning","author":"Breiman Leo","year":"1996","unstructured":"Leo Breiman . 1996. Bagging predictors. Machine learning , Vol. 24 , 2 ( 1996 ), 123--140. Leo Breiman. 1996. Bagging predictors. Machine learning, Vol. 24, 2 (1996), 123--140."},{"key":"e_1_3_2_1_7_1","unstructured":"Kurtland Chua Roberto Calandra Rowan McAllister and Sergey Levine. 2018. Deep reinforcement learning in a handful of trials using probabilistic dynamics models. In Advances in Neural Information Processing Systems. 4754--4765.  Kurtland Chua Roberto Calandra Rowan McAllister and Sergey Levine. 2018. Deep reinforcement learning in a handful of trials using probabilistic dynamics models. In Advances in Neural Information Processing Systems. 4754--4765."},{"key":"e_1_3_2_1_8_1","volume-title":"Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289","author":"Clevert Djork-Arn\u00e9","year":"2015","unstructured":"Djork-Arn\u00e9 Clevert , Thomas Unterthiner , and Sepp Hochreiter . 2015. Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289 ( 2015 ). Djork-Arn\u00e9 Clevert, Thomas Unterthiner, and Sepp Hochreiter. 2015. Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289 (2015)."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Jeffrey De Fauw Joseph R Ledsam Bernardino Romera-Paredes Stanislav Nikolov Nenad Tomasev Sam Blackwell Harry Askham Xavier Glorot Brendan O'Donoghue Daniel Visentin etal 2018. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature medicine Vol. 24 9 (2018) 1342--1350.  Jeffrey De Fauw Joseph R Ledsam Bernardino Romera-Paredes Stanislav Nikolov Nenad Tomasev Sam Blackwell Harry Askham Xavier Glorot Brendan O'Donoghue Daniel Visentin et al. 2018. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature medicine Vol. 24 9 (2018) 1342--1350.","DOI":"10.1038\/s41591-018-0107-6"},{"key":"e_1_3_2_1_10_1","volume-title":"Aleatory or epistemic? Does it matter? Structural safety","author":"Kiureghian Armen Der","year":"2009","unstructured":"Armen Der Kiureghian and Ove Ditlevsen . 2009. Aleatory or epistemic? Does it matter? Structural safety , Vol. 31 , 2 ( 2009 ), 105--112. Armen Der Kiureghian and Ove Ditlevsen. 2009. Aleatory or epistemic? Does it matter? Structural safety, Vol. 31, 2 (2009), 105--112."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.5555\/648054.743935"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3368555.3384457"},{"key":"e_1_3_2_1_13_1","volume-title":"Deep ensembles: A loss landscape perspective. arXiv preprint arXiv:1912.02757","author":"Fort Stanislav","year":"2019","unstructured":"Stanislav Fort , Huiyi Hu , and Balaji Lakshminarayanan . 2019. Deep ensembles: A loss landscape perspective. arXiv preprint arXiv:1912.02757 ( 2019 ). Stanislav Fort, Huiyi Hu, and Balaji Lakshminarayanan. 2019. Deep ensembles: A loss landscape perspective. arXiv preprint arXiv:1912.02757 (2019)."},{"key":"e_1_3_2_1_14_1","volume-title":"international conference on machine learning. 1050--1059","author":"Gal Yarin","year":"2016","unstructured":"Yarin Gal and Zoubin Ghahramani . 2016 . Dropout as a bayesian approximation: Representing model uncertainty in deep learning . In international conference on machine learning. 1050--1059 . Yarin Gal and Zoubin Ghahramani. 2016. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In international conference on machine learning. 1050--1059."},{"key":"e_1_3_2_1_15_1","volume-title":"Proceedings of the fourteenth international conference on artificial intelligence and statistics. 315--323","author":"Glorot Xavier","year":"2011","unstructured":"Xavier Glorot , Antoine Bordes , and Yoshua Bengio . 2011 . Deep sparse rectifier neural networks . In Proceedings of the fourteenth international conference on artificial intelligence and statistics. 315--323 . Xavier Glorot, Antoine Bordes, and Yoshua Bengio. 2011. Deep sparse rectifier neural networks. In Proceedings of the fourteenth international conference on artificial intelligence and statistics. 315--323."},{"key":"e_1_3_2_1_16_1","volume-title":"On calibration of modern neural networks. arXiv preprint arXiv:1706.04599","author":"Guo Chuan","year":"2017","unstructured":"Chuan Guo , Geoff Pleiss , Yu Sun , and Kilian Q Weinberger . 2017. On calibration of modern neural networks. arXiv preprint arXiv:1706.04599 ( 2017 ). Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger. 2017. On calibration of modern neural networks. arXiv preprint arXiv:1706.04599 (2017)."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_2_1_18_1","volume-title":"Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415","author":"Hendrycks Dan","year":"2016","unstructured":"Dan Hendrycks and Kevin Gimpel . 2016. Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415 ( 2016 ). Dan Hendrycks and Kevin Gimpel. 2016. Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415 (2016)."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/963770.963772"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/168304.168306"},{"key":"e_1_3_2_1_21_1","volume-title":"Snapshot ensembles: Train 1, get m for free. arXiv preprint arXiv:1704.00109","author":"Huang Gao","year":"2017","unstructured":"Gao Huang , Yixuan Li , Geoff Pleiss , Zhuang Liu , John E Hopcroft , and Kilian Q Weinberger . 2017. Snapshot ensembles: Train 1, get m for free. arXiv preprint arXiv:1704.00109 ( 2017 ). Gao Huang, Yixuan Li, Geoff Pleiss, Zhuang Liu, John E Hopcroft, and Kilian Q Weinberger. 2017. Snapshot ensembles: Train 1, get m for free. arXiv preprint arXiv:1704.00109 (2017)."},{"key":"e_1_3_2_1_22_1","unstructured":"Heinrich Jiang Been Kim Melody Guan and Maya Gupta. 2018. To trust or not to trust a classifier. In Advances in neural information processing systems. 5541--5552.  Heinrich Jiang Been Kim Melody Guan and Maya Gupta. 2018. To trust or not to trust a classifier. In Advances in neural information processing systems. 5541--5552."},{"key":"e_1_3_2_1_23_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"volume-title":"uncertainty and profit","author":"Knight Frank Hyneman","key":"e_1_3_2_1_24_1","unstructured":"Frank Hyneman Knight . 1921. Risk , uncertainty and profit . Vol. 31 . Houghton Mifflin . Frank Hyneman Knight. 1921. Risk, uncertainty and profit. Vol. 31. Houghton Mifflin."},{"key":"e_1_3_2_1_25_1","volume-title":"Beom Joon Kim, and Myunghee Cho Paik","author":"Kwon Yongchan","year":"2018","unstructured":"Yongchan Kwon , Joong-Ho Won , Beom Joon Kim, and Myunghee Cho Paik . 2018 . Uncertainty quantification using bayesian neural networks in classification: Application to ischemic stroke lesion segmentation. (2018). Yongchan Kwon, Joong-Ho Won, Beom Joon Kim, and Myunghee Cho Paik. 2018. Uncertainty quantification using bayesian neural networks in classification: Application to ischemic stroke lesion segmentation. (2018)."},{"key":"e_1_3_2_1_26_1","unstructured":"Balaji Lakshminarayanan Alexander Pritzel and Charles Blundell. 2017. Simple and scalable predictive uncertainty estimation using deep ensembles. In Advances in neural information processing systems. 6402--6413.  Balaji Lakshminarayanan Alexander Pritzel and Charles Blundell. 2017. Simple and scalable predictive uncertainty estimation using deep ensembles. In Advances in neural information processing systems. 6402--6413."},{"key":"e_1_3_2_1_27_1","volume-title":"Training confidence-calibrated classifiers for detecting out-of-distribution samples. arXiv preprint arXiv:1711.09325","author":"Lee Kimin","year":"2017","unstructured":"Kimin Lee , Honglak Lee , Kibok Lee , and Jinwoo Shin . 2017. Training confidence-calibrated classifiers for detecting out-of-distribution samples. arXiv preprint arXiv:1711.09325 ( 2017 ). Kimin Lee, Honglak Lee, Kibok Lee, and Jinwoo Shin. 2017. Training confidence-calibrated classifiers for detecting out-of-distribution samples. arXiv preprint arXiv:1711.09325 (2017)."},{"key":"e_1_3_2_1_28_1","volume-title":"Philipp Berens, and Siegfried Wahl.","author":"Leibig Christian","year":"2017","unstructured":"Christian Leibig , Vaneeda Allken , Murat Secc kin Ayhan , Philipp Berens, and Siegfried Wahl. 2017 . Leveraging uncertainty information from deep neural networks for disease detection. Scientific reports, Vol. 7 , 1 (2017), 1--14. Christian Leibig, Vaneeda Allken, Murat Secc kin Ayhan, Philipp Berens, and Siegfried Wahl. 2017. Leveraging uncertainty information from deep neural networks for disease detection. Scientific reports, Vol. 7, 1 (2017), 1--14."},{"key":"e_1_3_2_1_29_1","unstructured":"Jeremiah Liu John Paisley Marianthi-Anna Kioumourtzoglou and Brent Coull. 2019. Accurate uncertainty estimation and decomposition in ensemble learning. In Advances in Neural Information Processing Systems. 8952--8963.  Jeremiah Liu John Paisley Marianthi-Anna Kioumourtzoglou and Brent Coull. 2019. Accurate uncertainty estimation and decomposition in ensemble learning. In Advances in Neural Information Processing Systems. 8952--8963."},{"key":"e_1_3_2_1_30_1","volume-title":"Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness. arXiv preprint arXiv:2006.10108","author":"Liu Jeremiah Zhe","year":"2020","unstructured":"Jeremiah Zhe Liu , Zi Lin , Shreyas Padhy , Dustin Tran , Tania Bedrax-Weiss , and Balaji Lakshminarayanan . 2020. Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness. arXiv preprint arXiv:2006.10108 ( 2020 ). Jeremiah Zhe Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, and Balaji Lakshminarayanan. 2020. Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness. arXiv preprint arXiv:2006.10108 (2020)."},{"key":"e_1_3_2_1_31_1","volume-title":"Multiplicative normalizing flows for variational bayesian neural networks. arXiv preprint arXiv:1703.01961","author":"Louizos Christos","year":"2017","unstructured":"Christos Louizos and Max Welling . 2017. Multiplicative normalizing flows for variational bayesian neural networks. arXiv preprint arXiv:1703.01961 ( 2017 ). Christos Louizos and Max Welling. 2017. Multiplicative normalizing flows for variational bayesian neural networks. arXiv preprint arXiv:1703.01961 (2017)."},{"key":"e_1_3_2_1_32_1","volume-title":"Uncertainty Estimation with Infinitesimal Jackknife, Its Distribution and Mean-Field Approximation. arXiv preprint arXiv:2006.07584","author":"Lu Zhiyun","year":"2020","unstructured":"Zhiyun Lu , Eugene Ie , and Fei Sha . 2020. Uncertainty Estimation with Infinitesimal Jackknife, Its Distribution and Mean-Field Approximation. arXiv preprint arXiv:2006.07584 ( 2020 ). Zhiyun Lu, Eugene Ie, and Fei Sha. 2020. Uncertainty Estimation with Infinitesimal Jackknife, Its Distribution and Mean-Field Approximation. arXiv preprint arXiv:2006.07584 (2020)."},{"key":"e_1_3_2_1_33_1","volume-title":"A practical Bayesian framework for backpropagation networks. Neural computation","author":"MacKay David JC","year":"1992","unstructured":"David JC MacKay . 1992. A practical Bayesian framework for backpropagation networks. Neural computation , Vol. 4 , 3 ( 1992 ), 448--472. David JC MacKay. 1992. A practical Bayesian framework for backpropagation networks. Neural computation, Vol. 4, 3 (1992), 448--472."},{"key":"e_1_3_2_1_34_1","volume-title":"The Jackknife estimation method. arXiv preprint arXiv:1606.00497","author":"McIntosh Avery","year":"2016","unstructured":"Avery McIntosh . 2016. The Jackknife estimation method. arXiv preprint arXiv:1606.00497 ( 2016 ). Avery McIntosh. 2016. The Jackknife estimation method. arXiv preprint arXiv:1606.00497 (2016)."},{"key":"e_1_3_2_1_35_1","unstructured":"Vinod Nair and Geoffrey E Hinton. 2010. Rectified linear units improve restricted boltzmann machines. In ICML .  Vinod Nair and Geoffrey E Hinton. 2010. Rectified linear units improve restricted boltzmann machines. In ICML ."},{"volume-title":"Bayesian learning for neural networks","author":"Neal Radford M","key":"e_1_3_2_1_36_1","unstructured":"Radford M Neal . 2012. Bayesian learning for neural networks . Vol. 118 . Springer Science & Business Media . Radford M Neal. 2012. Bayesian learning for neural networks. Vol. 118. Springer Science & Business Media."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuron.2016.12.015"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICNN.1994.374138"},{"key":"e_1_3_2_1_39_1","volume-title":"Learning local error bars for nonlinear regression. Advances in neural information processing systems","author":"Nix David A","year":"1995","unstructured":"David A Nix and Andreas S Weigend . 1995. Learning local error bars for nonlinear regression. Advances in neural information processing systems ( 1995 ), 489--496. David A Nix and Andreas S Weigend. 1995. Learning local error bars for nonlinear regression. Advances in neural information processing systems (1995), 489--496."},{"key":"e_1_3_2_1_40_1","unstructured":"Yaniv Ovadia Emily Fertig Jie Ren Zachary Nado David Sculley Sebastian Nowozin Joshua Dillon Balaji Lakshminarayanan and Jasper Snoek. 2019. Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift. In Advances in Neural Information Processing Systems. 13991--14002.  Yaniv Ovadia Emily Fertig Jie Ren Zachary Nado David Sculley Sebastian Nowozin Joshua Dillon Balaji Lakshminarayanan and Jasper Snoek. 2019. Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift. In Advances in Neural Information Processing Systems. 13991--14002."},{"key":"e_1_3_2_1_41_1","volume-title":"Le","author":"Ramachandran Prajit","year":"2017","unstructured":"Prajit Ramachandran , Barret Zoph , and Quoc V . Le . 2017 . Searching for Activation Functions. CoRR , Vol. abs\/ 1710 .05941 (2017). Prajit Ramachandran, Barret Zoph, and Quoc V. Le. 2017. Searching for Activation Functions. CoRR, Vol. abs\/1710.05941 (2017)."},{"key":"e_1_3_2_1_42_1","volume-title":"Deep bayesian bandits showdown: An empirical comparison of bayesian deep networks for thompson sampling. arXiv preprint arXiv:1802.09127","author":"Riquelme Carlos","year":"2018","unstructured":"Carlos Riquelme , George Tucker , and Jasper Snoek . 2018. Deep bayesian bandits showdown: An empirical comparison of bayesian deep networks for thompson sampling. arXiv preprint arXiv:1802.09127 ( 2018 ). Carlos Riquelme, George Tucker, and Jasper Snoek. 2018. Deep bayesian bandits showdown: An empirical comparison of bayesian deep networks for thompson sampling. arXiv preprint arXiv:1802.09127 (2018)."},{"key":"e_1_3_2_1_43_1","volume-title":"Movie Recommender Systems: Implementation and Performance Evaluation. arXiv preprint arXiv:1909.12749","author":"Saadati Mojdeh","year":"2019","unstructured":"Mojdeh Saadati , Syed Shihab , and Mohammed Shaiqur Rahman . 2019. Movie Recommender Systems: Implementation and Performance Evaluation. arXiv preprint arXiv:1909.12749 ( 2019 ). Mojdeh Saadati, Syed Shihab, and Mohammed Shaiqur Rahman. 2019. Movie Recommender Systems: Implementation and Performance Evaluation. arXiv preprint arXiv:1909.12749 (2019)."},{"key":"e_1_3_2_1_44_1","volume-title":"Can you trust this prediction? Auditing pointwise reliability after learning. arXiv preprint arXiv:1901.00403","author":"Schulam Peter","year":"2019","unstructured":"Peter Schulam and Suchi Saria . 2019. Can you trust this prediction? Auditing pointwise reliability after learning. arXiv preprint arXiv:1901.00403 ( 2019 ). Peter Schulam and Suchi Saria. 2019. Can you trust this prediction? Auditing pointwise reliability after learning. arXiv preprint arXiv:1901.00403 (2019)."},{"key":"e_1_3_2_1_45_1","volume-title":"International conference on machine learning. 2171--2180","author":"Snoek Jasper","year":"2015","unstructured":"Jasper Snoek , Oren Rippel , Kevin Swersky , Ryan Kiros , Nadathur Satish , Narayanan Sundaram , Mostofa Patwary , Mr Prabhat , and Ryan Adams . 2015 . Scalable bayesian optimization using deep neural networks . In International conference on machine learning. 2171--2180 . Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Mostofa Patwary, Mr Prabhat, and Ryan Adams. 2015. Scalable bayesian optimization using deep neural networks. In International conference on machine learning. 2171--2180."},{"key":"e_1_3_2_1_46_1","volume-title":"Diverse ensembles improve calibration. arXiv preprint arXiv:2007.04206","author":"Stickland Asa Cooper","year":"2020","unstructured":"Asa Cooper Stickland and Iain Murray . 2020. Diverse ensembles improve calibration. arXiv preprint arXiv:2007.04206 ( 2020 ). Asa Cooper Stickland and Iain Murray. 2020. Diverse ensembles improve calibration. arXiv preprint arXiv:2007.04206 (2020)."},{"key":"e_1_3_2_1_47_1","volume-title":"Ying Yin Ting, and Jason Ansel","author":"Su Dongqi","year":"2018","unstructured":"Dongqi Su , Ying Yin Ting, and Jason Ansel . 2018 . Tight Prediction Intervals Using Expanded Interval Minimization . arXiv preprint arXiv:1806.11222 (2018). Dongqi Su, Ying Yin Ting, and Jason Ansel. 2018. Tight Prediction Intervals Using Expanded Interval Minimization. arXiv preprint arXiv:1806.11222 (2018)."},{"key":"e_1_3_2_1_48_1","unstructured":"Natasa Tagasovska and David Lopez-Paz. 2019. Single-model uncertainties for deep learning. In Advances in Neural Information Processing Systems. 6417--6428.  Natasa Tagasovska and David Lopez-Paz. 2019. Single-model uncertainties for deep learning. In Advances in Neural Information Processing Systems. 6417--6428."},{"key":"e_1_3_2_1_49_1","volume-title":"Eighth International Conference on Learning Representations (ICLR 2020)","author":"Wen Yeming","year":"2020","unstructured":"Yeming Wen , Dustin Tran , and Jimmy Ba . 2020 . BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning . Eighth International Conference on Learning Representations (ICLR 2020) (2020). Yeming Wen, Dustin Tran, and Jimmy Ba. 2020. BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning. Eighth International Conference on Learning Representations (ICLR 2020) (2020)."},{"key":"e_1_3_2_1_50_1","volume-title":"The psychometric function: II. Bootstrap-based confidence intervals and sampling. Perception & psychophysics","author":"Wichmann Felix A","year":"2001","unstructured":"Felix A Wichmann and N Jeremy Hill . 2001. The psychometric function: II. Bootstrap-based confidence intervals and sampling. Perception & psychophysics , Vol. 63 , 8 ( 2001 ), 1314--1329. Felix A Wichmann and N Jeremy Hill. 2001. The psychometric function: II. Bootstrap-based confidence intervals and sampling. Perception & psychophysics, Vol. 63, 8 (2001), 1314--1329."},{"key":"e_1_3_2_1_51_1","volume-title":"Neural Contextual Bandits with UCB-based Exploration. arxiv","author":"Zhou Dongruo","year":"1911","unstructured":"Dongruo Zhou , Lihong Li , and Quanquan Gu. 2019. Neural Contextual Bandits with UCB-based Exploration. arxiv : 1911 .04462 [cs.LG] Dongruo Zhou, Lihong Li, and Quanquan Gu. 2019. Neural Contextual Bandits with UCB-based Exploration. arxiv: 1911.04462 [cs.LG]"}],"event":{"name":"WSDM '21: The Fourteenth ACM International Conference on Web Search and Data Mining","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGIR ACM Special Interest Group on Information Retrieval"],"location":"Virtual Event Israel","acronym":"WSDM '21"},"container-title":["Proceedings of the 14th ACM International Conference on Web Search and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3437963.3441770","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3437963.3441770","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:47:35Z","timestamp":1750193255000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3437963.3441770"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,8]]},"references-count":51,"alternative-id":["10.1145\/3437963.3441770","10.1145\/3437963"],"URL":"https:\/\/doi.org\/10.1145\/3437963.3441770","relation":{},"subject":[],"published":{"date-parts":[[2021,3,8]]},"assertion":[{"value":"2021-03-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}