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High-stakes applications rely on combining Artificial Intelligence (AI) and humans for responsive and reliable decision making.
An optimum character recognition system using decision functions
C. K. Chow · 1957
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On optimum recognition error and reject tradeoff
C. K. Chow · 1970
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Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks
L Tassiulas and A Ephremides · 1992
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Rate control for communication networks: shadow prices, proportional fairness and stability
Frank P Kelly, Aman K Maulloo, and David Kim Hong Tan · 1998
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Adversarial queuing theory
Allan Borodin, Jon Kleinberg, Prabhakar Raghavan, Madhu Sudan, and David P Williamson · 2001
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Minimizing regret with label efficient prediction
Nicolò Cesa-Bianchi, Gábor Lugosi, and Gilles Stoltz · 2005
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Maximizing queueing network utility subject to stability: Greedy primal-dual algorithm
Alexander L Stolyar · 2005
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A tutorial on cross-layer optimization in wireless networks
Xiaojun Lin, Ness B Shroff, and Rayadurgam Srikant · 2006
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Classification with a reject option using a hinge loss
Peter L. Bartlett and Marten H. Wegkamp · 2008
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Priority assignment under imperfect information on customer type identities
Nilay Tanık Argon and Serhan Ziya · 2009
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Robust bounds for classification via selective sampling
Nicolo Cesa-Bianchi, Claudio Gentile, and Francesco Orabona · 2009
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On the foundations of noise-free selective classification
Ran El-Yaniv and Yair Wiener · 2010
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A contextual-bandit approach to personalized news article recommendation
Lihong Li, Wei Chu, John Langford, and Robert E. Schapire · 2010
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Trading off mistakes and don’t-know predictions
Amin Sayedi, Morteza Zadimoghaddam, and Avrim Blum · 2010
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Improved algorithms for linear stochastic bandits
Yasin Abbasi-Yadkori, Dávid Pál, and Csaba Szepesvári · 2011
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Contextual bandits with linear payoff functions
Wei Chu, Lihong Li, Lev Reyzin, and Robert E. Schapire · 2011
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Efficient optimal learning for contextual bandits
Miroslav Dudík, Daniel J. Hsu, Satyen Kale, Nikos Karampatziakis, John Langford, Lev Reyzin, and Tong Zhang · 2011
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Delay reduction via lagrange multipliers in stochastic network optimization
Longbo Huang and Michael J. Neely · 2011
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Knows what it knows: a framework for self-aware learning
Lihong Li, Michael L. Littman, Thomas J. Walsh, and Alexander L. Strehl · 2011
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Better algorithms for selective sampling
Francesco Orabona and Nicolò Cesa-Bianchi · 2011
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Blind network revenue management
Omar Besbes and Assaf Zeevi · 2012
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Selective sampling and active learning from single and multiple teachers
Ofer Dekel, Claudio Gentile, and Karthik Sridharan · 2012
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Max weight learning algorithms for scheduling in unknown environments
Michael J. Neely, Scott Rager, and Thomas F. La Porta · 2012
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Concentration inequalities: A nonasymptotic theory of independence
Stéphane Boucheron, Gábor Lugosi, and Pascal Massart · 2013
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Online learning under delayed feedback
Pooria Joulani, András György, and Csaba Szepesvári · 2013
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Resourceful contextual bandits
Ashwinkumar Badanidiyuru, John Langford, and Aleksandrs Slivkins · 2014
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Modeling delayed feedback in display advertising
Olivier Chapelle · 2014
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Concentration in unbounded metric spaces and algorithmic stability
Aryeh Kontorovich · 2014
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Two queues with non-stochastic arrivals
Neil S. Walton · 2014
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Close the gaps: A learning-while-doing algorithm for single-product revenue management problems
Zizhuo Wang, Shiming Deng, and Yinyu Ye · 2014
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The queue method: Handling delay, heuristics, prior data, and evaluation in bandits
Travis Mandel, Yun-En Liu, Emma Brunskill, and Zoran Popovic · 2015
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Algorithms with logarithmic or sublinear regret for constrained contextual bandits
Huasen Wu, R. Srikant, Xin Liu, and Chong Jiang · 2015
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Linear contextual bandits with knapsacks
Shipra Agrawal and Nikhil R. Devanur · 2016
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An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectives
Shipra Agrawal, Nikhil R. Devanur, and Lihong Li · 2016
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Learning with rejection
Corinna Cortes, Giulia DeSalvo, and Mehryar Mohri · 2016
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The extended littlestone’s dimension for learning with mistakes and abstentions
Chicheng Zhang and Kamalika Chaudhuri · 2016
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What can machine learning do? workforce implications
Erik Brynjolfsson and Tom Mitchell · 2017
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Stochastic bandit models for delayed conversions
Claire Vernade, Olivier Cappé, and Vianney Perchet · 2017
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Bandits with knapsacks
Ashwinkumar Badanidiyuru, Robert Kleinberg, and Aleksandrs Slivkins · 2018
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Online learning with abstention
Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, and Scott Yang · 2018
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Automating inequality: How high-tech tools profile, police, and punish the poor
Virginia Eubanks · 2018
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Online network revenue management using thompson sampling
Kris Johnson Ferreira, David Simchi-Levi, and He Wang · 2018
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On learning the c μ \mu rule in single and parallel server networks
Subhashini Krishnasamy, Ari Arapostathis, Ramesh Johari, and Sanjay Shakkottai · 2018
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Human decisions and machine predictions
Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan · 2018
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Minimizing queue length regret under adversarial network models
Qingkai Liang and Eytan Modiano · 2018
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Predict responsibly: improving fairness and accuracy by learning to defer
David Madras, Toni Pitassi, and Richard Zemel · 2018
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On the capacity of information processing systems
Laurent Massoulié and Kuang Xu · 2018
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Bandits with global convex constraints and objective
Shipra Agrawal and Nikhil R Devanur · 2019
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Regret minimization for reinforcement learning with vectorial feedback and complex objectives
Wang Chi Cheung · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Online learning and pricing for service systems with reusable resources
Huiwen Jia, Cong Shi, and Siqian Shen · 2022
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To engage or not to engage with ai for critical judgments: How professionals deal with opacity when using ai for medical diagnosis
Sarah Lebovitz, Hila Lifshitz-Assaf, and Natalia Levina · 2022
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Human-ai collaboration in decision-making: Beyond learning to defer
Diogo Leitão, Pedro Saleiro, Mário A. T. Figueiredo, and Pedro Bizarro · 2022
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How our content abuse defense systems work to keep members safe, 2022
Sanket Modi · 2022
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Algorithmic assistance with recommendation-dependent preferences
Bryce McLaughlin and Jann Spiess · 2022
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Probability: theory and examples
Rick Durrett · 2019
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The algorithmic automation problem: Prediction, triage, and human effort
Maithra Raghu, Katy Blumer, Greg Corrado, Jon Kleinberg, Ziad Obermeyer, and Sendhil Mullainathan · 2019
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Behind the screen
Sarah T Roberts · 2019
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Introduction to multi-armed bandits
Aleksandrs Slivkins · 2019
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Learning algorithms for scheduling in wireless networks with unknown channel statistics
Thomas Stahlbuhk, Brooke Shrader, and Eytan H. Modiano · 2019
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Constrained episodic reinforcement learning in concave-convex and knapsack settings
Kianté Brantley, Miroslav Dudík, Thodoris Lykouris, Sobhan Miryoosefi, Max Simchowitz, Aleksandrs Slivkins, and Wen Sun · 2020
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On the fairness of machine-assisted human decisions
Bryce McLaughlin, Jann Spiess, and Talia Gillis · 2022
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Stochastic network optimization with application to communication and queueing systems
Michael Neely · 2022
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Exponential savings in agnostic active learning through abstention
Nikita Puchkin and Nikita Zhivotovskiy · 2022
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Feature-based priority queuing
Simrita Singh, Itai Gurvich, and Jan A Van Mieghem · 2022
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Thompson sampling with unrestricted delays
Han Wu and Stefan Wager · 2022
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Learning the scheduling policy in time-varying multiclass many server queues with abandonment
Yueyang Zhong, John R Birge, and Amy Ward · 2022
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Efficient active learning with abstention
Yinglun Zhu and Robert Nowak · 2022
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Dynamic placement in refugee resettlement
Narges Ahani, Paul Gölz, Ariel D Procaccia, Alexander Teytelboym, and Andrew C Trapp · 2023
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Saghar Adler and Vijay Subramanian · 2023
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Delay-adaptive learning in generalized linear contextual bandits
Jose Blanchet, Renyuan Xu, and Zhengyuan Zhou · 2023
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Online learning and optimization for queues with unknown demand curve and service distribution
Xinyun Chen, Yunan Liu, and Guiyu Hong · 2023
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Efficient decentralized multi-agent learning in asymmetric bipartite queueing systems
Daniel Freund, Thodoris Lykouris, and Wentao Weng · 2023
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The transient cost of learning in queueing systems
Daniel Freund, Thodoris Lykouris, and Wentao Weng · 2023
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Rethinking fairness for human-ai collaboration
Haosen Ge, Hamsa Bastani, and Osbert Bastani · 2023
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Github copilot, 2023
Github · 2023
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The price of anarchy of strategic queuing systems
Jason Gaitonde and Éva Tardos · 2023
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Queue scheduling with adversarial bandit learning
Jiatai Huang, Leana Golubchik, and Longbo Huang · 2023
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Experimenting under stochastic congestion
Shuangning Li, Ramesh Johari, Stefan Wager, and Kuang Xu · 2023
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Facebook community standards, 2023
Meta Platforms, Inc · 2023
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Principled approaches for learning to defer with multiple experts
Anqi Mao, Mehryar Mohri, and Yutao Zhong · 2023
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Learning to schedule in non-stationary wireless networks with unknown statistics
Quang Minh Nguyen and Eytan Modiano · 2023
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Lawyer uses chatgpt in federal court and it goes horribly wrong, 2023
Matt Novak · 2023
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Insurance business, 2023
Ping An Insurance · 2023
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Automoderator, 2023
Reddit Inc · 2023
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Contextual bandits with packing and covering constraints: A modular lagrangian approach via regression
Aleksandrs Slivkins, Karthik Abinav Sankararaman, and Dylan J. Foster · 2023
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Learning to defer to multiple experts: Consistent surrogate losses, confidence calibration, and conformal ensembles
Rajeev Verma, Daniel Barrejón, and Eric T. Nalisnick · 2023
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Promises and pitfalls of threshold-based auto-labeling
Harit Vishwakarma, Heguang Lin, Frederic Sala, and Ramya Korlakai Vinayak · 2023
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The X rules, 2023
X Corp · 2023
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Will ai augment or replace workers?, 2023
Guy Yehiav · 2023
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Learning while scheduling in multi-server systems with unknown statistics: Maxweight with discounted ucb
Zixian Yang, R Srikant, and Lei Ying · 2023
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On the importance of uncertainty in decision-making with large language models
Nicolò Felicioni, Lucas Maystre, Sina Ghiassian, and Kamil Ciosek · 2024
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Design and scheduling of an ai-based queueing system
Jiung Lee, Hongseok Namkoong, and Yibo Zeng · 2024
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How meta prioritizes content for review, 2022
Meta Platforms, Inc · 2024
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Jigsaw unintended bias in toxicity classification, 2019
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Toxic comment classification challenge, 2017
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Scheduling with uncertain holding costs and its application to content moderation
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Vlm as policy: Common-law content moderation framework for short video platform
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The silent partner cleaning up facebook for $ 500 million a year
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