Aleksandrs Slivkins

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Robert KleinbergDepartment of Computer Science, Cornell UniversityVerified email at cs.cornell.edu
Zhiwei Steven WuCarnegie Mellon UniversityVerified email at andrew.cmu.edu
Jennifer Wortman VaughanSenior Principal Researcher, Microsoft Research, New York CityVerified email at microsoft.com
Moshe BabaioffHebrew University of JerusalemVerified email at mail.huji.ac.il
Ittai AbrahamIntelVerified email at cs.huji.ac.il
Jon KleinbergProfessor of Computer Science, Cornell UniversityVerified email at cs.cornell.edu
John LangfordMicrosoft Research New YorkVerified email at hunch.net
Nicole ImmorlicaMicrosoft ResearchVerified email at microsoft.com
Emin Gün SirerCornell UniversityVerified email at systems.cs.cornell.edu
Dr Bernard WongUniversity of SydneyVerified email at bawtech.com.au
Eli UpfalComputer Science Professor, Brown UniversityVerified email at brown.edu
Yishay MansourTel Aviv UniversityVerified email at tauex.tau.ac.il
Karthik Abinav SankararamanMeta/Facebook, University of Maryland, College ParkVerified email at fb.com
Ashwinkumar BadanidiyuruUberVerified email at uber.com
Siddhartha SenMicrosoft Research (NYC)Verified email at microsoft.com
Chien-Ju HoWashington University in St. LouisVerified email at wustl.edu
Vasilis SyrgkanisAssistant Professor, Stanford UniversityVerified email at stanford.edu
Omar AlonsoAmazonVerified email at amazon.com
Filip RadlinskiGoogleVerified email at google.com
Robert SchapireMicrosoft ResearchVerified email at microsoft.com

Aleksandrs Slivkins

Senior Principal Researcher, Microsoft Research NYC

Verified email at microsoft.com - Homepage

Algorithms machine learning theory algorithmic economics social network analysis


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Introduction to multi-armed bandits A Slivkins Foundations and Trends in Machine Learning 12 (1-2), 1-286, 2019	1116	2019
Bandits and experts in metric spaces R Kleinberg, A Slivkins, E Upfal Journal of the ACM (JACM) 66 (4), 30, 2019	643*	2019
Meridian: A lightweight network location service without virtual coordinates B Wong, A Slivkins, EG Sirer ACM SIGCOMM Computer Communication Review 35 (4), 85-96, 2005	584	2005
Bandits with knapsacks A Badanidiyuru, R Kleinberg, A Slivkins Journal of the ACM (JACM) 65 (3), 13, 2018	513	2018
Contextual bandits with similarity information A Slivkins The Journal of Machine Learning Research 15 (1), 2533-2568, 2014	483	2014
The best of both worlds: stochastic and adversarial bandits S Bubeck, A Slivkins Conference on Learning Theory, 42.1-42.23, 2012	269	2012
Triangulation and embedding using small sets of beacons J Kleinberg, A Slivkins, T Wexler Journal of the ACM (JACM) 56 (6), 32, 2009	229*	2009
Dynamic pricing with limited supply M Babaioff, S Dughmi, R Kleinberg, A Slivkins ACM Transactions on Economics and Computation (TEAC) 3 (1), 4, 2015	214	2015
Incentivizing high quality crowdwork CJ Ho, A Slivkins, S Suri, JW Vaughan Proceedings of the 24th International Conference on World Wide Web, 419-429, 2015	199	2015
Characterizing truthful multi-armed bandit mechanisms M Babaioff, Y Sharma, A Slivkins SIAM Journal on Computing 43 (1), 194-230, 2014	190*	2014
One practical algorithm for both stochastic and adversarial bandits Y Seldin, A Slivkins International Conference on Machine Learning, 1287-1295, 2014	186	2014
Adapting to a Changing Environment: the Brownian Restless Bandits. A Slivkins, E Upfal COLT, 343-354, 2008	172	2008
Bayesian incentive-compatible bandit exploration Y Mansour, A Slivkins, V Syrgkanis Operations Research 68 (4), 1132-1161, 2020	163*	2020
Ranked bandits in metric spaces: learning diverse rankings over large document collections A Slivkins, F Radlinski, S Gollapudi Journal of Machine Learning Research 14 (Feb), 399-436, 2013	161*	2013
Resourceful contextual bandits A Badanidiyuru, J Langford, A Slivkins Conference on Learning Theory, 1109-1134, 2014	150	2014
Making contextual decisions with low technical debt A Agarwal, S Bird, M Cozowicz, L Hoang, J Langford, S Lee, J Li, ... arXiv preprint arXiv:1606.03966, 2016	147*	2016
Adaptive contract design for crowdsourcing markets: Bandit algorithms for repeated principal-agent problems CJ Ho, A Slivkins, JW Vaughan Journal of Artificial Intelligence Research 55, 317-359, 2016	143	2016
Distance estimation and object location via rings of neighbors A Slivkins Distributed Computing 19 (4), 313-333, 2007	143	2007
Adversarial Bandits with Knapsacks N Immorlica, K Sankararaman, R Schapire, A Slivkins Journal of the ACM 69 (6), 1-47, 2022	124	2022
Corruption-robust exploration in episodic reinforcement learning T Lykouris, M Simchowitz, A Slivkins, W Sun Conference on Learning Theory, 3242-3245, 2021	124	2021

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