Aleksandrs Slivkins

According to our database, Aleksandrs Slivkins authored at least 96 papers between 2005 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Generative AI as Economic Agents.

Nicole Immorlica, Brendan Lucier, Aleksandrs Slivkins

SIGecom Exch., 2024

Exploration and Incentives in Reinforcement Learning.

Max Simchowitz, Aleksandrs Slivkins

Oper. Res., 2024

Exploration and Persuasion.

Aleksandrs Slivkins

CoRR, 2024

Can large language models explore in-context?

Akshay Krishnamurthy, Keegan Harris, Dylan J. Foster, Cyril Zhang, Aleksandrs Slivkins

CoRR, 2024

Strategic Budget Selection in a Competitive Autobidding World.

Yiding Feng, Brendan Lucier, Aleksandrs Slivkins

Proceedings of the 56th Annual ACM Symposium on Theory of Computing, 2024

Incentivized Exploration via Filtered Posterior Sampling.

Yonatan Gur, Anand Kalvit, Aleksandrs Slivkins

Proceedings of the 25th ACM Conference on Economics and Computation, 2024

Impact of Decentralized Learning on Player Utilities in Stackelberg Games.

Kate Donahue, Nicole Immorlica, Meena Jagadeesan, Brendan Lucier, Aleksandrs Slivkins

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Autobidders with Budget and ROI Constraints: Efficiency, Regret, and Pacing Dynamics.

Brendan Lucier, Sarath Pattathil, Aleksandrs Slivkins, Mengxiao Zhang

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

Oracle-Efficient Pessimism: Offline Policy Optimization In Contextual Bandits.

Lequn Wang, Akshay Krishnamurthy, Alex Slivkins

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Content Filtering with Inattentive Information Consumers.

Ian Ball, James W. Bono, Justin Grana, Nicole Immorlica, Brendan Lucier, Aleksandrs Slivkins

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Greedy Algorithm Almost Dominates in Smoothed Contextual Bandits.

Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu

SIAM J. Comput., April, 2023

The Price of Incentivizing Exploration: A Characterization via Thompson Sampling and Sample Complexity.

Mark Sellke, Aleksandrs Slivkins

Oper. Res., 2023

Robust and Performance Incentivizing Algorithms for Multi-Armed Bandits with Strategic Agents.

Seyed A. Esmaeili, Suho Shin, Aleksandrs Slivkins

CoRR, 2023

Algorithmic Persuasion Through Simulation: Information Design in the Age of Generative AI.

Keegan Harris, Nicole Immorlica, Brendan Lucier, Aleksandrs Slivkins

CoRR, 2023

Bandit Social Learning: Exploration under Myopic Behavior.

Kiarash Banihashem, MohammadTaghi Hajiaghayi, Suho Shin, Aleksandrs Slivkins

CoRR, 2023

Bandit Social Learning under Myopic Behavior.

Kiarash Banihashem, MohammadTaghi Hajiaghayi, Suho Shin, Aleksandrs Slivkins

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Budget Pacing in Repeated Auctions: Regret and Efficiency Without Convergence.

Jason Gaitonde, Yingkai Li, Bar Light, Brendan Lucier, Aleksandrs Slivkins

Proceedings of the 14th Innovations in Theoretical Computer Science Conference, 2023

Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regression.

Aleksandrs Slivkins, Karthik Abinav Sankararaman, Dylan J. Foster

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

Adversarial Bandits with Knapsacks.

Nicole Immorlica, Karthik Abinav Sankararaman, Robert E. Schapire, Aleksandrs Slivkins

J. ACM, 2022

Bayesian Exploration: Incentivizing Exploration in Bayesian Games.

Yishay Mansour, Alex Slivkins, Vasilis Syrgkanis, Zhiwei Steven Wu

Oper. Res., 2022

Efficient Contextual Bandits with Knapsacks via Regression.

Aleksandrs Slivkins, Dylan J. Foster

CoRR, 2022

Incentivizing Participation in Clinical Trials.

Yingkai Li, Aleksandrs Slivkins

CoRR, 2022

Truthful Online Scheduling of Cloud Workloads under Uncertainty.

Moshe Babaioff, Ronny Lempel, Brendan Lucier, Ishai Menache, Aleksandrs Slivkins, Sam Chiu-wai Wong

Proceedings of the WWW '22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25, 2022

Incentivizing Combinatorial Bandit Exploration.

Xinyan Hu, Dung Daniel T. Ngo, Aleksandrs Slivkins, Zhiwei Steven Wu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Beating Greedy For Approximating Reserve Prices in Multi-Unit VCG Auctions.

Mahsa Derakhshan, David M. Pennock, Aleksandrs Slivkins

Proceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms, 2021

Bandits with Knapsacks beyond the Worst Case.

Karthik Abinav Sankararaman, Aleksandrs Slivkins

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Adaptive Discretization for Adversarial Lipschitz Bandits.

Chara Podimata, Alex Slivkins

Proceedings of the Conference on Learning Theory, 2021

Corruption-robust exploration in episodic reinforcement learning.

Thodoris Lykouris, Max Simchowitz, Alex Slivkins, Wen Sun

Proceedings of the Conference on Learning Theory, 2021

Sayer: Using Implicit Feedback to Optimize System Policies.

Mathias Lécuyer, Sang Hoon Kim, Mihir Nanavati, Junchen Jiang, Siddhartha Sen, Aleksandrs Slivkins, Amit Sharma

Proceedings of the SoCC '21: ACM Symposium on Cloud Computing, 2021

2020

Multidimensional Dynamic Pricing for Welfare Maximization.

Aaron Roth, Aleksandrs Slivkins, Jonathan R. Ullman, Zhiwei Steven Wu

ACM Trans. Economics and Comput., 2020

Book announcement: Introduction to Multi-Armed Bandits.

Aleksandrs Slivkins

SIGecom Exch., 2020

Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting.

Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang

J. Mach. Learn. Res., 2020

Bayesian Incentive-Compatible Bandit Exploration.

Yishay Mansour, Aleksandrs Slivkins, Vasilis Syrgkanis

Oper. Res., 2020

Competing Bandits: The Perils of Exploration Under Competition.

Guy Aridor, Yishay Mansour, Aleksandrs Slivkins, Zhiwei Steven Wu

CoRR, 2020

Adaptive Discretization for Adversarial Bandits with Continuous Action Spaces.

Chara Podimata, Aleksandrs Slivkins

CoRR, 2020

Sample Complexity of Incentivized Exploration.

Mark Sellke, Aleksandrs Slivkins

CoRR, 2020

Advances in Bandits with Knapsacks.

Karthik Abinav Sankararaman, Aleksandrs Slivkins

CoRR, 2020

Incentivizing Exploration with Selective Data Disclosure.

Nicole Immorlica, Jieming Mao, Aleksandrs Slivkins, Zhiwei Steven Wu

Proceedings of the EC '20: The 21st ACM Conference on Economics and Computation, 2020

Efficient Contextual Bandits with Continuous Actions.

Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Constrained episodic reinforcement learning in concave-convex and knapsack settings.

Kianté Brantley, Miroslav Dudík, Thodoris Lykouris, Sobhan Miryoosefi, Max Simchowitz, Aleksandrs Slivkins, Wen Sun

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Bandits and Experts in Metric Spaces.

Robert Kleinberg, Aleksandrs Slivkins, Eli Upfal

J. ACM, 2019

Introduction to Multi-Armed Bandits.

Aleksandrs Slivkins

Found. Trends Mach. Learn., 2019

Competing Bandits: The Perils of Exploration under Competition.

Guy Aridor, Kevin Liu, Aleksandrs Slivkins, Zhiwei Steven Wu

CoRR, 2019

Bayesian Exploration with Heterogeneous Agents.

Nicole Immorlica, Jieming Mao, Aleksandrs Slivkins, Zhiwei Steven Wu

Proceedings of the World Wide Web Conference, 2019

The Perils of Exploration under Competition: A Computational Modeling Approach.

Guy Aridor, Kevin Liu, Aleksandrs Slivkins, Zhiwei Steven Wu

Proceedings of the 2019 ACM Conference on Economics and Computation, 2019

2018

Bandits with Knapsacks.

Ashwinkumar Badanidiyuru, Robert Kleinberg, Aleksandrs Slivkins

J. ACM, 2018

Incentivizing Exploration with Unbiased Histories.

Nicole Immorlica, Jieming Mao, Aleksandrs Slivkins, Zhiwei Steven Wu

CoRR, 2018

Competing Bandits: Learning Under Competition.

Yishay Mansour, Aleksandrs Slivkins, Zhiwei Steven Wu

Proceedings of the 9th Innovations in Theoretical Computer Science Conference, 2018

The Externalities of Exploration and How Data Diversity Helps Exploitation.

Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu

Proceedings of the Conference On Learning Theory, 2018

Combinatorial Semi-Bandits with Knapsacks.

Karthik Abinav Sankararaman, Aleksandrs Slivkins

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Incentivizing exploration via information asymmetry.

Aleksandrs Slivkins

XRDS, 2017

Semi-Bandits with Knapsacks.

Karthik Abinav Sankararaman, Aleksandrs Slivkins

CoRR, 2017

A Polynomial Time Algorithm for Spatio-Temporal Security Games.

Soheil Behnezhad, Mahsa Derakhshan, Mohammad Taghi Hajiaghayi, Aleksandrs Slivkins

Proceedings of the 2017 ACM Conference on Economics and Computation, 2017

Harvesting Randomness to Optimize Distributed Systems.

Mathias Lécuyer, Joshua Lockerman, Lamont Nelson, Siddhartha Sen, Amit Sharma, Aleksandrs Slivkins

Proceedings of the 16th ACM Workshop on Hot Topics in Networks, Palo Alto, CA, USA, 2017

2016

Selection and influence in cultural dynamics.

David Kempe, Jon M. Kleinberg, Sigal Oren, Aleksandrs Slivkins

Netw. Sci., 2016

Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems.

Chien-Ju Ho, Aleksandrs Slivkins, Jennifer Wortman Vaughan

J. Artif. Intell. Res., 2016

A Multiworld Testing Decision Service.

Alekh Agarwal, Sarah Bird, Markus Cozowicz, Luong Hoang, John Langford, Stephen Lee, Jiaji Li, I. Dan Melamed, Gal Oshri, Oswaldo Ribas, Siddhartha Sen, Alex Slivkins

CoRR, 2016

How Many Workers to Ask?: Adaptive Exploration for Collecting High Quality Labels.

Ittai Abraham, Omar Alonso, Vasilis Kandylas, Rajesh Patel, Steven Shelford, Aleksandrs Slivkins

Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, 2016

2015

Dynamic Pricing with Limited Supply.

Moshe Babaioff, Shaddin Dughmi, Robert D. Kleinberg, Aleksandrs Slivkins

ACM Trans. Economics and Comput., 2015

Incentivizing high quality crowdwork.

Chien-Ju Ho, Aleksandrs Slivkins, Siddharth Suri, Jennifer Wortman Vaughan

SIGecom Exch., 2015

Low-Distortion Inference of Latent Similarities from a Multiplex Social Network.

Ittai Abraham, Shiri Chechik, David Kempe, Aleksandrs Slivkins

SIAM J. Comput., 2015

Truthful Mechanisms with Implicit Payment Computation.

Moshe Babaioff, Robert D. Kleinberg, Aleksandrs Slivkins

J. ACM, 2015

Contextual Dueling Bandits.

Miroslav Dudík, Katja Hofmann, Robert E. Schapire, Aleksandrs Slivkins, Masrour Zoghi

Proceedings of The 28th Conference on Learning Theory, 2015

2014

Characterizing Truthful Multi-armed Bandit Mechanisms.

Moshe Babaioff, Yogeshwer Sharma, Aleksandrs Slivkins

SIAM J. Comput., 2014

Contextual bandits with similarity information.

Aleksandrs Slivkins

J. Mach. Learn. Res., 2014

One Practical Algorithm for Both Stochastic and Adversarial Bandits.

Yevgeny Seldin, Aleksandrs Slivkins

Proceedings of the 31th International Conference on Machine Learning, 2014

Using Worker Quality Scores to Improve Stopping Rules.

Ittai Abraham, Omar Alonso, Vasilis Kandylas, Rajesh Patel, Steven Shelford, Aleksandrs Slivkins

Proceedings of the Seconf AAAI Conference on Human Computation and Crowdsourcing, 2014

Resourceful Contextual Bandits.

Ashwinkumar Badanidiyuru, John Langford, Aleksandrs Slivkins

Proceedings of The 27th Conference on Learning Theory, 2014

Robust Multi-objective Learning with Mentor Feedback.

Alekh Agarwal, Ashwinkumar Badanidiyuru, Miroslav Dudík, Robert E. Schapire, Aleksandrs Slivkins

Proceedings of The 27th Conference on Learning Theory, 2014

2013

Online decision making in crowdsourcing markets: theoretical challenges.

Aleksandrs Slivkins, Jennifer Wortman Vaughan

SIGecom Exch., 2013

Ranked bandits in metric spaces: learning diverse rankings over large document collections.

Aleksandrs Slivkins, Filip Radlinski, Sreenivas Gollapudi

J. Mach. Learn. Res., 2013

Online Decision Making in Crowdsourcing Markets: Theoretical Challenges (Position Paper).

Aleksandrs Slivkins, Jennifer Wortman Vaughan

CoRR, 2013

Dynamic Ad Allocation: Bandits with Budgets.

Aleksandrs Slivkins

CoRR, 2013

Multi-parameter mechanisms with implicit payment computation.

Moshe Babaioff, Robert Kleinberg, Aleksandrs Slivkins

Proceedings of the fourteenth ACM Conference on Electronic Commerce, 2013

Adaptive Crowdsourcing Algorithms for the Bandit Survey Problem.

Ittai Abraham, Omar Alonso, Vasilis Kandylas, Aleksandrs Slivkins

Proceedings of the COLT 2013, 2013

2012

The Best of Both Worlds: Stochastic and Adversarial Bandits.

Sébastien Bubeck, Aleksandrs Slivkins

Proceedings of the COLT 2012, 2012

2011

Monotone multi-armed bandit allocations.

Aleksandrs Slivkins

Proceedings of the COLT 2011, 2011

Multi-armed bandits on implicit metric spaces.

Aleksandrs Slivkins

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

2010

Parameterized Tractability of Edge-Disjoint Paths on Directed Acyclic Graphs.

Aleksandrs Slivkins

SIAM J. Discret. Math., 2010

Sharp Dichotomies for Regret Minimization in Metric Spaces.

Robert Kleinberg, Aleksandrs Slivkins

Proceedings of the Twenty-First Annual ACM-SIAM Symposium on Discrete Algorithms, 2010

Learning optimally diverse rankings over large document collections.

Aleksandrs Slivkins, Filip Radlinski, Sreenivas Gollapudi

Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

2009

Metric Embeddings with Relaxed Guarantees.

T.-H. Hubert Chan, Kedar Dhamdhere, Anupam Gupta, Jon M. Kleinberg, Aleksandrs Slivkins

SIAM J. Comput., 2009

Triangulation and embedding using small sets of beacons.

Jon M. Kleinberg, Aleksandrs Slivkins, Tom Wexler

J. ACM, 2009

Interleaving schemes on circulant graphs with two offsets.

Aleksandrs Slivkins, Jehoshua Bruck

Discret. Math., 2009

Characterizing truthful multi-armed bandit mechanisms: extended abstract.

Moshe Babaioff, Yogeshwer Sharma, Aleksandrs Slivkins

Proceedings of the Proceedings 10th ACM Conference on Electronic Commerce (EC-2009), 2009

Adapting to the Shifting Intent of Search Queries.

Umar Syed, Aleksandrs Slivkins, Nina Mishra

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

2008

Network Failure Detection and Graph Connectivity.

Jon M. Kleinberg, Mark Sandler, Aleksandrs Slivkins

SIAM J. Comput., 2008

Multi-armed bandits in metric spaces.

Robert Kleinberg, Aleksandrs Slivkins, Eli Upfal

Proceedings of the 40th Annual ACM Symposium on Theory of Computing, 2008

Adapting to a Changing Environment: the Brownian Restless Bandits.

Aleksandrs Slivkins, Eli Upfal

Proceedings of the 21st Annual Conference on Learning Theory, 2008

2007

Distance estimation and object location via rings of neighbors.

Aleksandrs Slivkins

Distributed Comput., 2007

Towards fast decentralized construction of locality-aware overlay networks.

Aleksandrs Slivkins

Proceedings of the Twenty-Sixth Annual ACM Symposium on Principles of Distributed Computing, 2007

2006

Embedding, Distance Estimation and Object Location in Networks.

Aleksandrs Slivkins

PhD thesis, 2006

Oscillations with TCP-Like Flow Control in Networks of Queues.

Matthew Andrews, Aleksandrs Slivkins

Proceedings of the INFOCOM 2006. 25th IEEE International Conference on Computer Communications, 2006

2005

Distributed approaches to triangulation and embedding.

Aleksandrs Slivkins

Proceedings of the Sixteenth Annual ACM-SIAM Symposium on Discrete Algorithms, 2005

Meridian: a lightweight network location service without virtual coordinates.

Bernard Wong, Aleksandrs Slivkins, Emin Gün Sirer

Proceedings of the ACM SIGCOMM 2005 Conference on Applications, 2005

Metric Embeddings with Relaxed Guarantees.

Ittai Abraham, Yair Bartal, T.-H. Hubert Chan, Kedar Dhamdhere, Anupam Gupta, Jon M. Kleinberg, Ofer Neiman, Aleksandrs Slivkins

Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2005), 2005