:: Library Catalog

Omslagsbillede

Saved in:

Bibliografiske detaljer
Main Authors:	Brânzei, Simina, Peres, Yuval
Format:	Preprint
Udgivet:	2019
Fag:	Computer Science and Game Theory Machine Learning Theoretical Economics
Online adgang:	https://arxiv.org/abs/1908.01135
Tags:	Tilføj Tag Ingen Tags, Vær først til at tagge denne postø!

Lignende værker

Tit-for-Tat Dynamics and Market Volatility
af: Brânzei, Simina
Udgivet: (2019)

Competing Bandits: The Perils of Exploration Under Competition
af: Aridor, Guy, et al.
Udgivet: (2020)

Dueling Over Dessert, Mastering the Art of Repeated Cake Cutting
af: Brânzei, Simina, et al.
Udgivet: (2024)

Emergent Alignment via Competition
af: Collina, Natalie, et al.
Udgivet: (2025)

Deep Learning for Double Auction
af: Liu, Jiayin, et al.
Udgivet: (2025)

Randomized Truthful Auctions with Learning Agents
af: Aggarwal, Gagan, et al.
Udgivet: (2024)

Networked Information Aggregation via Machine Learning
af: Kearns, Michael, et al.
Udgivet: (2025)

Learning to Coordinate Bidders in Non-Truthful Auctions
af: Fu, Hu, et al.
Udgivet: (2025)

From No-Regret to Strategically Robust Learning in Repeated Auctions
af: Zhao, Junyao
Udgivet: (2026)

Learning to Play Multi-Follower Bayesian Stackelberg Games
af: Personnat, Gerson, et al.
Udgivet: (2025)

Persuading a Behavioral Agent: Approximately Best Responding and Learning
af: Chen, Yiling, et al.
Udgivet: (2023)

The Bounds of Algorithmic Collusion; $Q$-learning, Gradient Learning, and the Folk Theorem
af: Askenazi-Golan, Galit, et al.
Udgivet: (2024)

Federated Learning and Free-riding in a Competitive Market
af: Meng, Jiajun, et al.
Udgivet: (2024)

Response Time Enhances Alignment with Heterogeneous Preferences
af: Echenique, Federico, et al.
Udgivet: (2026)

A Linear Theory of Multi-Winner Voting
af: Xia, Lirong
Udgivet: (2025)

Welfare, Improvability, and Variance: A Principal-Agent Approach to Optimal Benchmark Item Aggregation
af: Haupt, Andreas, et al.
Udgivet: (2026)

How Humans Help LLMs: Assessing and Incentivizing Human Preference Annotators
af: Liu, Shang, et al.
Udgivet: (2025)

Revisiting Fairness Impossibility with Endogenous Behavior
af: Penn, Elizabeth Maggie, et al.
Udgivet: (2026)

Algorithmic Collusion Without Threats
af: Arunachaleswaran, Eshwar Ram, et al.
Udgivet: (2024)

Exploration and Persuasion
af: Slivkins, Aleksandrs
Udgivet: (2024)

Swim till You Sink: Computing the Limit of a Game
af: Hakim, Rashida, et al.
Udgivet: (2024)

Fair Allocation in Dynamic Mechanism Design
af: Fallah, Alireza, et al.
Udgivet: (2024)

Online Assortment and Price Optimization Under Contextual Choice Models
af: Erginbas, Yigit Efe, et al.
Udgivet: (2025)

Networked Information Aggregation for Binary Classification
af: Bateni, MohammadHossein, et al.
Udgivet: (2026)

"Calibeating": Beating Forecasters at Their Own Game
af: Foster, Dean P., et al.
Udgivet: (2022)

How to Sell High-Dimensional Data Optimally
af: Li, Andrew, et al.
Udgivet: (2025)

Competitive and Revenue-Optimal Pricing with Budgets
af: Finster, Simon, et al.
Udgivet: (2023)

Marketplace Operators Can Induce Competitive Pricing
af: Ding, Tiffany, et al.
Udgivet: (2025)

Competition Complexity in Multi-Item Auctions: Beyond VCG and Regularity
af: Beyhaghi, Hedyeh, et al.
Udgivet: (2025)

Construction of Compromise Values for Cooperative Games
af: Gilles, Robert P., et al.
Udgivet: (2025)

Opponent Modeling in Multiplayer Imperfect-Information Games
af: Ganzfried, Sam, et al.
Udgivet: (2022)

A New Solution for Cooperative Game with Public Externalities
af: Fan, Juanjuan, et al.
Udgivet: (2025)

A Geometric Nash Approach in Tuning the Learning Rate in Q-Learning Algorithm
af: Bonsu, Kwadwo Osei
Udgivet: (2024)

Using "Failure Costs" to Guarantee Execution Quality in Competitive and Permissionless Order Flow Auctions
af: Watts, Alex, et al.
Udgivet: (2025)

Contracting with a Learning Agent
af: Guruganesh, Guru, et al.
Udgivet: (2024)

Efficient Inverse Multiagent Learning
af: Goktas, Denizalp, et al.
Udgivet: (2025)

Generalized Principal-Agent Problem with a Learning Agent
af: Lin, Tao, et al.
Udgivet: (2024)

Contractual Reinforcement Learning: Pulling Arms with Invisible Hands
af: Wu, Jibang, et al.
Udgivet: (2024)

Statistical Impossibility and Possibility of Aligning LLMs with Human Preferences: From Condorcet Paradox to Nash Equilibrium
af: Liu, Kaizhao, et al.
Udgivet: (2025)

Paid with Models: Optimal Contract Design for Collaborative Machine Learning
af: Wang, Bingchen, et al.
Udgivet: (2024)