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Aleksandrs Slivkins
Aleksandrs Slivkins
Senior Principal Researcher, Microsoft Research NYC
E-mail confirmado em microsoft.com - Página inicial
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Introduction to multi-armed bandits
A Slivkins
Foundations and Trends in Machine Learning 12 (1-2), 1-286, 2019
12012019
Bandits and experts in metric spaces
R Kleinberg, A Slivkins, E Upfal
Journal of the ACM (JACM) 66 (4), 30, 2019
652*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
5842005
Bandits with knapsacks
A Badanidiyuru, R Kleinberg, A Slivkins
Journal of the ACM (JACM) 65 (3), 13, 2018
5282018
Contextual bandits with similarity information
A Slivkins
The Journal of Machine Learning Research 15 (1), 2533-2568, 2014
4862014
The best of both worlds: stochastic and adversarial bandits
S Bubeck, A Slivkins
Conference on Learning Theory, 42.1-42.23, 2012
2762012
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
2192015
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
2022015
Characterizing truthful multi-armed bandit mechanisms
M Babaioff, Y Sharma, A Slivkins
SIAM Journal on Computing 43 (1), 194-230, 2014
193*2014
One practical algorithm for both stochastic and adversarial bandits
Y Seldin, A Slivkins
International Conference on Machine Learning, 1287-1295, 2014
1902014
Adapting to a Changing Environment: the Brownian Restless Bandits.
A Slivkins, E Upfal
COLT, 343-354, 2008
1722008
Bayesian incentive-compatible bandit exploration
Y Mansour, A Slivkins, V Syrgkanis
Operations Research 68 (4), 1132-1161, 2020
169*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
1542014
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
149*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
1492016
Distance estimation and object location via rings of neighbors
A Slivkins
Distributed Computing 19 (4), 313-333, 2007
1432007
Adversarial Bandits with Knapsacks
N Immorlica, K Sankararaman, R Schapire, A Slivkins
Journal of the ACM 69 (6), 1-47, 2022
1302022
Corruption-robust exploration in episodic reinforcement learning
T Lykouris, M Simchowitz, A Slivkins, W Sun
Conference on Learning Theory, 3242-3245, 2021
1292021
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