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Aleksandrs Slivkins

28 papers · 2009–2025 · 6 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (15) 🐝 Cross-Pollinator (14) πŸƒ Academic Marathon (16) 🌍 Conference Polyglot (6) 🌈 Renaissance Researcher (6) πŸŒ‰ Interdisciplinary Bridge 🐣 Hot Topic Early Bird 🧭 Keyword Pioneer 🌟 Keyword Trendsetter Combo (3) 🐺 Lone Wolf (4) πŸ”¬ Deep Specialist (16) πŸ† Keyword Champion (3) 🌱 Topic Pioneer 🧬 Topic Evolution πŸ“ˆ Trend Setter ⚑ Prolific Year (5) πŸ—ƒοΈ Keyword Collector (105) ❓ The Questioner πŸ’Ž Century Club (28) πŸš€ Conference Pioneer πŸ”₯ Unstoppable (8)

Conferences

COLT (11) NIPS (8) JMLR (4) AAAI (2) ICML (2) AISTATS (1)

Top co-authors

John Langford (4) Karthik Abinav Sankararaman (4) Akshay Krishnamurthy (4) Brendan Lucier (3) Chicheng Zhang (3) Robert E. Schapire (2) Nicole Immorlica (2) Dylan J Foster (2) Miroslav DudΓ­k (2) Dylan J. Foster (2)

Research topics

Methods (1)

Keywords

multi-armed bandit (16) regret bound (12) contextual bandit (11) online learning (6) online algorithm (5) knapsack problem (3) bandit algorithm (3) continuous action (3) packing constraint (2) regression oracle (2) vanishing regret (2) budget constraint (2) mechanism design (2) covering constraint (2) adaptive algorithm (2) metric space (2) combinatorial optimization (2) greedy algorithm (2) decision making (2) lagrangian approach (2)

Papers

Robust Performance Incentivizing Algorithms for Multi-Armed Bandits with Strategic Agents AAAI 2025
Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regression JMLR 2024
Can large language models explore in-context? NIPS 2024
Autobidders with Budget and ROI Constraints: Efficiency, Regret, and Pacing Dynamics COLT 2024
Impact of Decentralized Learning on Player Utilities in Stackelberg Games ICML 2024
Content Filtering with Inattentive Information Consumers AAAI 2024
Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regression COLT 2023
Bandit Social Learning under Myopic Behavior NIPS 2023
Incentivizing Combinatorial Bandit Exploration NIPS 2022
Bandits with Knapsacks beyond the Worst Case NIPS 2021
Efficient Contextual Bandits with Continuous Actions NIPS 2020
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting JMLR 2020
Constrained episodic reinforcement learning in concave-convex and knapsack settings NIPS 2020
Contextual bandits with continuous actions: Smoothing, zooming, and adapting COLT 2019
Combinatorial Semi-Bandits with Knapsacks AISTATS 2018
The Externalities of Exploration and How Data Diversity Helps Exploitation COLT 2018
Contextual Dueling Bandits COLT 2015
Resourceful Contextual Bandits COLT 2014
Robust Multi-objective Learning with Mentor Feedback COLT 2014
One Practical Algorithm for Both Stochastic and Adversarial Bandits ICML 2014
Contextual Bandits with Similarity Information JMLR 2014
Adaptive Crowdsourcing Algorithms for the Bandit Survey Problem COLT 2013
Ranked Bandits in Metric Spaces: Learning Diverse Rankings over Large Document Collections JMLR 2013
The Best of Both Worlds: Stochastic and Adversarial Bandits COLT 2012
Multi-armed bandits on implicit metric spaces NIPS 2011
Monotone multi-armed bandit allocations COLT 2011
Contextual Bandits with Similarity Information COLT 2011
Adapting to the Shifting Intent of Search Queries NIPS 2009