Image de couverture de livre

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

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Lucier, Brendan, Pattathil, Sarath, Slivkins, Aleksandrs, Zhang, Mengxiao
Format:	Preprint
Publié:	2023
Sujets:	Computer Science and Game Theory Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Budget Pacing in Repeated Auctions: Regret and Efficiency without Convergence
par: Gaitonde, Jason, et autres
Publié: (2022)

Impact of Decentralized Learning on Player Utilities in Stackelberg Games
par: Donahue, Kate, et autres
Publié: (2024)

Exploration and Persuasion
par: Slivkins, Aleksandrs
Publié: (2024)

No-Regret Algorithms in non-Truthful Auctions with Budget and ROI Constraints
par: Aggarwal, Gagan, et autres
Publié: (2024)

Adaptive Discretization against an Adversary: Lipschitz bandits, Dynamic Pricing, and Auction Tuning
par: Podimata, Chara, et autres
Publié: (2020)

Algorithmic Persuasion Through Simulation
par: Harris, Keegan, et autres
Publié: (2023)

Robust and Performance Incentivizing Algorithms for Multi-Armed Bandits with Strategic Agents
par: Esmaeili, Seyed A., et autres
Publié: (2023)

Online Learning under Budget and ROI Constraints via Weak Adaptivity
par: Castiglioni, Matteo, et autres
Publié: (2023)

Competing Bandits: The Perils of Exploration Under Competition
par: Aridor, Guy, et autres
Publié: (2020)

Feedback Control for Small Budget Pacing
par: Apparaju, Sreeja, et autres
Publié: (2025)

Clickbait vs. Quality: How Engagement-Based Optimization Shapes the Content Landscape in Online Platforms
par: Immorlica, Nicole, et autres
Publié: (2024)

Bandit Social Learning: Exploration under Myopic Behavior
par: Banihashem, Kiarash, et autres
Publié: (2023)

Incentivizing Exploration with Selective Data Disclosure
par: Immorlica, Nicole, et autres
Publié: (2018)

Exploration and Incentivizing Participation in Randomized Trials
par: Li, Yingkai, et autres
Publié: (2022)

Bandit Social Learning with Exploration Episodes
par: Banihashem, Kiarash, et autres
Publié: (2026)

Dynamic Regret Bounds for Online Omniprediction with Long Term Constraints
par: Bechavod, Yahav, et autres
Publié: (2025)

No-Regret Learning for Fair Multi-Agent Social Welfare Optimization
par: Zhang, Mengxiao, et autres
Publié: (2024)

No-Regret Learning in Bilateral Trade via Global Budget Balance
par: Bernasconi, Martino, et autres
Publié: (2023)

Better Regret Rates in Bilateral Trade via Sublinear Budget Violation
par: Lunghi, Anna, et autres
Publié: (2025)

Agentic Markets: Equilibrium Effects of Improving Consumer Search
par: Lucier, Brendan, et autres
Publié: (2026)

Constraint-Aware Generative Auto-bidding via Pareto-Prioritized Regret Optimization
par: Wu, Binglin, et autres
Publié: (2026)

A New Benchmark for Online Learning with Budget-Balancing Constraints
par: Braverman, Mark, et autres
Publié: (2025)

Pricing Query Complexity of Multiplicative Revenue Approximation
par: Tang, Wei, et autres
Publié: (2026)

Smart Fast Finish: Preventing Overdelivery via Daily Budget Pacing at DoorDash
par: Garg, Rohan, et autres
Publié: (2025)

Approximately Optimal Mechanism Design for Competing Sellers
par: Lucier, Brendan, et autres
Publié: (2025)

Online Combinatorial Allocations and Auctions with Few Samples
par: Dütting, Paul, et autres
Publié: (2024)

Learning not to Regret
par: Sychrovský, David, et autres
Publié: (2023)

Adaptive Bidding Policies for First-Price Auctions with Budget Constraints under Non-stationarity
par: Wang, Yige, et autres
Publié: (2025)

Data Pricing for Graph Neural Networks without Pre-purchased Inspection
par: Liu, Yiping, et autres
Publié: (2025)

No Coin Left Behind: Maximizing Strategic Surplus Against No-Regret Dynamics
par: Su, Yiheng, et autres
Publié: (2026)

Optimal Selection Using Algorithmic Rankings with Side Information
par: Donahue, Kate, et autres
Publié: (2025)

An Adaptable Budget Planner for Enhancing Budget-Constrained Auto-Bidding in Online Advertising
par: Duan, Zhijian, et autres
Publié: (2025)

Improved Regret Bounds for Online Fair Division with Bandit Learning
par: Schiffer, Benjamin, et autres
Publié: (2025)

Honor Among Bandits: No-Regret Learning for Online Fair Division
par: Procaccia, Ariel D., et autres
Publié: (2024)

Regret Analysis of Repeated Delegated Choice
par: Hajiaghayi, MohammadTaghi, et autres
Publié: (2023)

GPU-Accelerated Counterfactual Regret Minimization
par: Kim, Juho
Publié: (2024)

Full Swap Regret and Discretized Calibration
par: Fishelson, Maxwell, et autres
Publié: (2025)

Regret Analysis of Sleeping Competing Bandits
par: Uba, Shinnosuke, et autres
Publié: (2026)

p-Mean Regret for Stochastic Bandits
par: Krishna, Anand, et autres
Publié: (2024)

Barriers to Welfare Maximization with No-Regret Learning
par: Anagnostides, Ioannis, et autres
Publié: (2024)