Aleksandrs Slivkins

ORCID

Publication Activity (10 Years)

Years Active: 2003-2024
Publications (10 Years): 78

Top Venues

CoRR

COLT

NeurIPS

Publications

Max Simchowitz, Aleksandrs Slivkins
Exploration and Incentives in Reinforcement Learning. Oper. Res. 72 (3) (2024)
Akshay Krishnamurthy, Keegan Harris, Dylan J. Foster, Cyril Zhang, Aleksandrs Slivkins
Can large language models explore in-context? CoRR (2024)
Brendan Lucier, Sarath Pattathil, Aleksandrs Slivkins, Mengxiao Zhang
Autobidders with Budget and ROI Constraints: Efficiency, Regret, and Pacing Dynamics. COLT (2024)
Anand Kalvit, Aleksandrs Slivkins, Yonatan Gur
Incentivized Exploration via Filtered Posterior Sampling. CoRR (2024)
Yiding Feng, Brendan Lucier, Aleksandrs Slivkins
Strategic Budget Selection in a Competitive Autobidding World. STOC (2024)
Kate Donahue, Nicole Immorlica, Meena Jagadeesan, Brendan Lucier, Aleksandrs Slivkins
Impact of Decentralized Learning on Player Utilities in Stackelberg Games. CoRR (2024)
Ian Ball, James W. Bono, Justin Grana, Nicole Immorlica, Brendan Lucier, Aleksandrs Slivkins
Content Filtering with Inattentive Information Consumers. AAAI (2024)
Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu
Greedy Algorithm Almost Dominates in Smoothed Contextual Bandits. SIAM J. Comput. 52 (2) (2023)
Jason Gaitonde, Yingkai Li, Bar Light, Brendan Lucier, Aleksandrs Slivkins
Budget Pacing in Repeated Auctions: Regret and Efficiency Without Convergence. ITCS (2023)
Brendan Lucier, Sarath Pattathil, Aleksandrs Slivkins, Mengxiao Zhang
Autobidders with Budget and ROI Constraints: Efficiency, Regret, and Pacing Dynamics. CoRR (2023)
Keegan Harris, Nicole Immorlica, Brendan Lucier, Aleksandrs Slivkins
Algorithmic Persuasion Through Simulation: Information Design in the Age of Generative AI. CoRR (2023)
Mark Sellke, Aleksandrs Slivkins
The Price of Incentivizing Exploration: A Characterization via Thompson Sampling and Sample Complexity. Oper. Res. 71 (5) (2023)
Kiarash Banihashem, MohammadTaghi Hajiaghayi, Suho Shin, Aleksandrs Slivkins
Bandit Social Learning under Myopic Behavior. NeurIPS (2023)
Kiarash Banihashem, MohammadTaghi Hajiaghayi, Suho Shin, Aleksandrs Slivkins
Bandit Social Learning: Exploration under Myopic Behavior. CoRR (2023)
Aleksandrs Slivkins, Karthik Abinav Sankararaman, Dylan J. Foster
Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regression. COLT (2023)
Seyed A. Esmaeili, Suho Shin, Aleksandrs Slivkins
Robust and Performance Incentivizing Algorithms for Multi-Armed Bandits with Strategic Agents. CoRR (2023)
Lequn Wang, Akshay Krishnamurthy, Aleksandrs Slivkins
Oracle-Efficient Pessimism: Offline Policy Optimization in Contextual Bandits. CoRR (2023)
Yiding Feng, Brendan Lucier, Aleksandrs Slivkins
Strategic Budget Selection in a Competitive Autobidding World. CoRR (2023)
Yingkai Li, Aleksandrs Slivkins
Incentivizing Participation in Clinical Trials. CoRR (2022)
Jason Gaitonde, Yingkai Li, Bar Light, Brendan Lucier, Aleksandrs Slivkins
Budget Pacing in Repeated Auctions: Regret and Efficiency without Convergence. CoRR (2022)
Aleksandrs Slivkins, Dylan J. Foster
Efficient Contextual Bandits with Knapsacks via Regression. CoRR (2022)
Moshe Babaioff, Ronny Lempel, Brendan Lucier, Ishai Menache, Aleksandrs Slivkins, Sam Chiu-Wai Wong
Truthful Online Scheduling of Cloud Workloads under Uncertainty. CoRR (2022)
Xinyan Hu, Dung Daniel T. Ngo, Aleksandrs Slivkins, Zhiwei Steven Wu
Incentivizing Combinatorial Bandit Exploration. CoRR (2022)
Xinyan Hu, Dung Daniel T. Ngo, Aleksandrs Slivkins, Zhiwei Steven Wu
Incentivizing Combinatorial Bandit Exploration. NeurIPS (2022)
Nicole Immorlica, Karthik Abinav Sankararaman, Robert E. Schapire, Aleksandrs Slivkins
Adversarial Bandits with Knapsacks. J. ACM 69 (6) (2022)
Moshe Babaioff, Ronny Lempel, Brendan Lucier, Ishai Menache, Aleksandrs Slivkins, Sam Chiu-Wai Wong
Truthful Online Scheduling of Cloud Workloads under Uncertainty. WWW (2022)
Mathias Lécuyer, Sang Hoon Kim, Mihir Nanavati, Junchen Jiang, Siddhartha Sen, Amit Sharma, Aleksandrs Slivkins
Sayer: Using Implicit Feedback to Optimize System Policies. CoRR (2021)
Max Simchowitz, Aleksandrs Slivkins
Exploration and Incentives in Reinforcement Learning. CoRR (2021)
Mark Sellke, Aleksandrs Slivkins
The Price of Incentivizing Exploration: A Characterization via Thompson Sampling and Sample Complexity. EC (2021)
Mathias Lécuyer, Sang Hoon Kim, Mihir Nanavati, Junchen Jiang, Siddhartha Sen, Aleksandrs Slivkins, Amit Sharma
Sayer: Using Implicit Feedback to Optimize System Policies. SoCC (2021)
Karthik Abinav Sankararaman, Aleksandrs Slivkins
Bandits with Knapsacks beyond the Worst Case. NeurIPS (2021)
Mahsa Derakhshan, David M. Pennock, Aleksandrs Slivkins
Beating Greedy For Approximating Reserve Prices in Multi-Unit VCG Auctions. SODA (2021)
Karthik Abinav Sankararaman, Aleksandrs Slivkins
Advances in Bandits with Knapsacks. CoRR (2020)
Aaron Roth, Aleksandrs Slivkins, Jonathan R. Ullman, Zhiwei Steven Wu
Multidimensional Dynamic Pricing for Welfare Maximization. ACM Trans. Economics and Comput. 8 (1) (2020)
Aleksandrs Slivkins
Book announcement: Introduction to Multi-Armed Bandits. SIGecom Exch. 18 (1) (2020)
Mahsa Derakhshan, David M. Pennock, Aleksandrs Slivkins
Beating Greedy For Approximating Reserve Prices in Multi-Unit VCG Auctions. CoRR (2020)
Kianté Brantley, Miroslav Dudík, Thodoris Lykouris, Sobhan Miryoosefi, Max Simchowitz, Aleksandrs Slivkins, Wen Sun
Constrained episodic reinforcement learning in concave-convex and knapsack settings. CoRR (2020)
Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins
Efficient Contextual Bandits with Continuous Actions. CoRR (2020)
Guy Aridor, Yishay Mansour, Aleksandrs Slivkins, Zhiwei Steven Wu
Competing Bandits: The Perils of Exploration Under Competition. CoRR (2020)
Nicole Immorlica, Jieming Mao, Aleksandrs Slivkins, Zhiwei Steven Wu
Incentivizing Exploration with Selective Data Disclosure. EC (2020)
Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins
Efficient Contextual Bandits with Continuous Actions. NeurIPS (2020)
Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting. J. Mach. Learn. Res. 21 (2020)
Kianté Brantley, Miroslav Dudík, Thodoris Lykouris, Sobhan Miryoosefi, Max Simchowitz, Aleksandrs Slivkins, Wen Sun
Constrained episodic reinforcement learning in concave-convex and knapsack settings. NeurIPS (2020)
Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu
Greedy Algorithm almost Dominates in Smoothed Contextual Bandits. CoRR (2020)
Yishay Mansour, Aleksandrs Slivkins, Vasilis Syrgkanis
Bayesian Incentive-Compatible Bandit Exploration. Oper. Res. 68 (4) (2020)
Mark Sellke, Aleksandrs Slivkins
Sample Complexity of Incentivized Exploration. CoRR (2020)
Chara Podimata, Aleksandrs Slivkins
Adaptive Discretization for Adversarial Bandits with Continuous Action Spaces. CoRR (2020)
Nicole Immorlica, Karthik Abinav Sankararaman, Robert E. Schapire, Aleksandrs Slivkins
Adversarial Bandits with Knapsacks. FOCS (2019)
Aleksandrs Slivkins
Introduction to Multi-Armed Bandits. CoRR (2019)
Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang
Contextual bandits with continuous actions: Smoothing, zooming, and adapting. COLT (2019)
Guy Aridor, Kevin Liu, Aleksandrs Slivkins, Zhiwei Steven Wu
Competing Bandits: The Perils of Exploration under Competition. CoRR (2019)
Robert Kleinberg, Aleksandrs Slivkins, Eli Upfal
Bandits and Experts in Metric Spaces. J. ACM 66 (4) (2019)
Thodoris Lykouris, Max Simchowitz, Aleksandrs Slivkins, Wen Sun
Corruption Robust Exploration in Episodic Reinforcement Learning. CoRR (2019)
Nicole Immorlica, Jieming Mao, Aleksandrs Slivkins, Zhiwei Steven Wu
Bayesian Exploration with Heterogeneous Agents. CoRR (2019)
Guy Aridor, Kevin Liu, Aleksandrs Slivkins, Zhiwei Steven Wu
The Perils of Exploration under Competition: A Computational Modeling Approach. EC (2019)
Aleksandrs Slivkins
Introduction to Multi-Armed Bandits. Found. Trends Mach. Learn. 12 (1-2) (2019)
Nicole Immorlica, Jieming Mao, Aleksandrs Slivkins, Zhiwei Steven Wu
Bayesian Exploration with Heterogeneous Agents. WWW (2019)
Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting. CoRR (2019)
Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu
The Externalities of Exploration and How Data Diversity Helps Exploitation. CoRR (2018)
Karthik Abinav Sankararaman, Aleksandrs Slivkins
Combinatorial Semi-Bandits with Knapsacks. AISTATS (2018)
Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu
The Externalities of Exploration and How Data Diversity Helps Exploitation. COLT (2018)
Nicole Immorlica, Karthik Abinav Sankararaman, Robert E. Schapire, Aleksandrs Slivkins
Adversarial Bandits with Knapsacks. CoRR (2018)
Nicole Immorlica, Jieming Mao, Aleksandrs Slivkins, Zhiwei Steven Wu
Incentivizing Exploration with Unbiased Histories. CoRR (2018)
Yishay Mansour, Aleksandrs Slivkins, Zhiwei Steven Wu
Competing Bandits: Learning Under Competition. ITCS (2018)
Ashwinkumar Badanidiyuru, Robert Kleinberg, Aleksandrs Slivkins
Bandits with Knapsacks. J. ACM 65 (3) (2018)
Yishay Mansour, Aleksandrs Slivkins, Zhiwei Steven Wu
Competing Bandits: Learning under Competition. CoRR (2017)
Soheil Behnezhad, Mahsa Derakhshan, MohammadTaghi Hajiaghayi, Aleksandrs Slivkins
A Polynomial Time Algorithm for Spatio-Temporal Security Games. CoRR (2017)
Mathias Lécuyer, Joshua Lockerman, Lamont Nelson, Siddhartha Sen, Amit Sharma, Aleksandrs Slivkins
Harvesting Randomness to Optimize Distributed Systems. HotNets (2017)
Karthik Abinav Sankararaman, Aleksandrs Slivkins
Semi-Bandits with Knapsacks. CoRR (2017)
Aaron Roth, Aleksandrs Slivkins, Jonathan R. Ullman, Zhiwei Steven Wu
Multidimensional Dynamic Pricing for Welfare Maximization. EC (2017)
Aleksandrs Slivkins
Incentivizing exploration via information asymmetry. XRDS 24 (1) (2017)
Soheil Behnezhad, Mahsa Derakhshan, Mohammad Taghi Hajiaghayi, Aleksandrs Slivkins
A Polynomial Time Algorithm for Spatio-Temporal Security Games. EC (2017)
Ittai Abraham, Omar Alonso, Vasilis Kandylas, Rajesh Patel, Steven Shelford, Aleksandrs Slivkins
How Many Workers to Ask?: Adaptive Exploration for Collecting High Quality Labels. SIGIR (2016)
David Kempe, Jon M. Kleinberg, Sigal Oren, Aleksandrs Slivkins
Selection and influence in cultural dynamics. Netw. Sci. 4 (1) (2016)
Yishay Mansour, Aleksandrs Slivkins, Vasilis Syrgkanis, Zhiwei Steven Wu
Bayesian Exploration: Incentivizing Exploration in Bayesian Games. EC (2016)
Yishay Mansour, Aleksandrs Slivkins, Vasilis Syrgkanis, Zhiwei Steven Wu
Bayesian Exploration: Incentivizing Exploration in Bayesian Games. CoRR (2016)
Chien-Ju Ho, Aleksandrs Slivkins, Jennifer Wortman Vaughan
Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems. J. Artif. Intell. Res. 55 (2016)
Aaron Roth, Aleksandrs Slivkins, Jonathan R. Ullman, Zhiwei Steven Wu
Multidimensional Dynamic Pricing for Welfare Maximization. CoRR (2016)
Chien-Ju Ho, Aleksandrs Slivkins, Siddharth Suri, Jennifer Wortman Vaughan
Incentivizing High Quality Crowdwork. CoRR (2015)
Chien-Ju Ho, Aleksandrs Slivkins, Siddharth Suri, Jennifer Wortman Vaughan
Incentivizing high quality crowdwork. SIGecom Exch. 14 (2) (2015)
Yishay Mansour, Aleksandrs Slivkins, Vasilis Syrgkanis
Bayesian Incentive-Compatible Bandit Exploration. EC (2015)
Moshe Babaioff, Robert D. Kleinberg, Aleksandrs Slivkins
Truthful Mechanisms with Implicit Payment Computation. J. ACM 62 (2) (2015)
Yishay Mansour, Aleksandrs Slivkins, Vasilis Syrgkanis
Bayesian Incentive-Compatible Bandit Exploration. CoRR (2015)
Miroslav Dudík, Katja Hofmann, Robert E. Schapire, Aleksandrs Slivkins, Masrour Zoghi
Contextual Dueling Bandits. CoRR (2015)
Miroslav Dudík, Katja Hofmann, Robert E. Schapire, Aleksandrs Slivkins, Masrour Zoghi
Contextual Dueling Bandits. COLT (2015)
Moshe Babaioff, Shaddin Dughmi, Robert D. Kleinberg, Aleksandrs Slivkins
Dynamic Pricing with Limited Supply. ACM Trans. Economics and Comput. 3 (1) (2015)
Chien-Ju Ho, Aleksandrs Slivkins, Siddharth Suri, Jennifer Wortman Vaughan
Incentivizing High Quality Crowdwork. WWW (2015)
Ittai Abraham, Shiri Chechik, David Kempe, Aleksandrs Slivkins
Low-Distortion Inference of Latent Similarities from a Multiplex Social Network. SIAM J. Comput. 44 (3) (2015)
Aleksandrs Slivkins
Contextual bandits with similarity information. J. Mach. Learn. Res. 15 (1) (2014)
Ashwinkumar Badanidiyuru, John Langford, Aleksandrs Slivkins
Resourceful Contextual Bandits. COLT (2014)
Chien-Ju Ho, Aleksandrs Slivkins, Jennifer Wortman Vaughan
Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems. CoRR (2014)
Ashwinkumar Badanidiyuru, John Langford, Aleksandrs Slivkins
Resourceful Contextual Bandits. CoRR (2014)
Yevgeny Seldin, Aleksandrs Slivkins
One Practical Algorithm for Both Stochastic and Adversarial Bandits. ICML (2014)
Chien-Ju Ho, Aleksandrs Slivkins, Jennifer Wortman Vaughan
Adaptive contract design for crowdsourcing markets: bandit algorithms for repeated principal-agent problems. EC (2014)
Moshe Babaioff, Yogeshwer Sharma, Aleksandrs Slivkins
Characterizing Truthful Multi-armed Bandit Mechanisms. SIAM J. Comput. 43 (1) (2014)
Ittai Abraham, Omar Alonso, Vasilis Kandylas, Rajesh Patel, Steven Shelford, Aleksandrs Slivkins
Using Worker Quality Scores to Improve Stopping Rules. CoRR (2014)
Ittai Abraham, Omar Alonso, Vasilis Kandylas, Rajesh Patel, Steven Shelford, Aleksandrs Slivkins
Using Worker Quality Scores to Improve Stopping Rules. HCOMP (2014)
Alekh Agarwal, Ashwinkumar Badanidiyuru, Miroslav Dudík, Robert E. Schapire, Aleksandrs Slivkins
Robust Multi-objective Learning with Mentor Feedback. COLT (2014)
Ittai Abraham, Shiri Chechik, David Kempe, Aleksandrs Slivkins
Low-distortion Inference of Latent Similarities from a Multiplex Social Network. SODA (2013)
Robert Kleinberg, Aleksandrs Slivkins, Eli Upfal
Bandits and Experts in Metric Spaces. CoRR (2013)

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Publication Activity (10 Years)

Top Topics

Top Venues

Aleksandrs Slivkins