:: Library Catalog

Imagen de Portada

Guardado en:

Detalles Bibliográficos
Autores principales:	Mortazavi, Ali, Lin, Junhao, Mehta, Nishant A.
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning Computer Science and Game Theory
Acceso en línea:	https://arxiv.org/abs/2404.05155
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

No-regret incentive-compatible online learning under exact truthfulness with non-myopic experts
por: Komiyama, Junpei, et al.
Publicado: (2025)

Fairness in two-player zero-sum games with bandit feedback
por: Akash, S, et al.
Publicado: (2026)

Bayes correlated equilibria, no-regret dynamics in Bayesian games, and the price of anarchy
por: Fujii, Kaito
Publicado: (2023)

Optimal No-regret Learning in Repeated First-price Auctions
por: Han, Yanjun, et al.
Publicado: (2020)

Stochastic contextual bandits with graph feedback: from independence number to MAS number
por: Wen, Yuxiao, et al.
Publicado: (2024)

Strategizing against No-regret Learners
por: Deng, Yuan, et al.
Publicado: (2019)

A survey on multi-player bandits
por: Boursier, Etienne, et al.
Publicado: (2022)

Last iterate convergence in no-regret learning: constrained min-max optimization for convex-concave landscapes
por: Lei, Qi, et al.
Publicado: (2020)

Truthful mechanisms for linear bandit games with private contexts
por: Hu, Yiting, et al.
Publicado: (2025)

Personalized incentives as feedback design in generalized Nash equilibrium problems
por: Fabiani, Filippo, et al.
Publicado: (2022)

Convergence to Nash Equilibrium and No-regret Guarantee in (Markov) Potential Games
por: Dong, Jing, et al.
Publicado: (2024)

An explainable machine learning-based approach for analyzing customers' online data to identify the importance of product attributes
por: Karimzadeh, Aigin, et al.
Publicado: (2024)

Improved learning rates in multi-unit uniform price auctions
por: Potfer, Marius, et al.
Publicado: (2025)

An $α$-regret analysis of Adversarial Bilateral Trade
por: Azar, Yossi, et al.
Publicado: (2022)

Group-wise oracle-efficient algorithms for online multi-group learning
por: Deng, Samuel, et al.
Publicado: (2024)

Incentive-compatible Bandits: Importance Weighting No More
por: Zimmert, Julian, et al.
Publicado: (2024)

Adaptive Discretization against an Adversary: Lipschitz bandits, Dynamic Pricing, and Auction Tuning
por: Podimata, Chara, et al.
Publicado: (2020)

Learning to Bid Optimally and Efficiently in Adversarial First-price Auctions
por: Han, Yanjun, et al.
Publicado: (2020)

Conservative classifiers do consistently well with improving agents: characterizing statistical and online learning
por: Sharma, Dravyansh, et al.
Publicado: (2025)

Language Alignment via Nash-learning and Adaptive feedback
por: Azarafrooz, Ari, et al.
Publicado: (2024)

Responding to Promises: No-regret learning against followers with memory
por: Hebbar, Vijeth, et al.
Publicado: (2024)

The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions
por: Wen, Yuxiao, et al.
Publicado: (2026)

User Response in Ad Auctions: An MDP Formulation of Long-Term Revenue Optimization
por: Cai, Yang, et al.
Publicado: (2023)

Hierarchies of No-regret Algorithms
por: Xu, R., et al.
Publicado: (2026)

Sink equilibria and the attractors of learning in games
por: Biggar, Oliver, et al.
Publicado: (2025)

Offline congestion games: How feedback type affects data coverage requirement
por: Jiang, Haozhe, et al.
Publicado: (2022)

A Renegotiable contract-theoretic incentive mechanism for Federated learning
por: Tan, Xavier, et al.
Publicado: (2025)

High dimensional online calibration in polynomial time
por: Peng, Binghui
Publicado: (2025)

Benchmarking Inductive Biases for Multivariate Time-Series Anomaly Detection with a Robust Multi-View Channel-Graph Detector
por: Wei, Junhao, et al.
Publicado: (2026)

On the Convergence of Federated Learning Algorithms without Data Similarity
por: Beikmohammadi, Ali, et al.
Publicado: (2024)

C2:Cross learning module enhanced decision transformer with Constraint-aware loss for auto-bidding
por: Ding, Jinren, et al.
Publicado: (2026)

Optimal bounds for dissatisfaction in perpetual voting
por: Kozachinskiy, Alexander, et al.
Publicado: (2024)

Bayesian Strategic Classification
por: Cohen, Lee, et al.
Publicado: (2024)

An improved bound for the price of anarchy for related machine scheduling
por: Berger, Andre, et al.
Publicado: (2024)

Adaptive Bandit Algorithms for Contextual Matching Markets
por: Lin, Shiyun, et al.
Publicado: (2026)

Stable Matching with Ties: Approximation Ratios and Learning
por: Lin, Shiyun, et al.
Publicado: (2024)

Deep Reinforcement Learning for Sequential Combinatorial Auctions
por: Ravindranath, Sai Srivatsa, et al.
Publicado: (2024)

Computing Pure-Strategy Nash Equilibria in a Two-Party Policy Competition: Existence and Algorithmic Approaches
por: Lin, Chuang-Chieh, et al.
Publicado: (2025)

WOMAC: A Mechanism For Prediction Competitions
por: Srinivasan, Siddarth, et al.
Publicado: (2025)

Auto-bidding under Return-on-Spend Constraints with Uncertainty Quantification
por: Han, Jiale, et al.
Publicado: (2025)