:: Library Catalog

Imagen de Portada

Guardado en:

Detalles Bibliográficos
Autores principales:	Han, Minbiao, Zhang, Fengxue, Chen, Yuxin
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning
Acceso en línea:	https://arxiv.org/abs/2405.08318
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Direct Regret Optimization in Bayesian Optimization
por: Zhang, Fengxue, et al.
Publicado: (2025)

Constrained Multi-objective Bayesian Optimization through Optimistic Constraints Estimation
por: Li, Diantong, et al.
Publicado: (2024)

Learning in Online Principal-Agent Interactions: The Power of Menus
por: Han, Minbiao, et al.
Publicado: (2023)

Horizon-Free Regret for Linear Markov Decision Processes
por: Zhang, Zihan, et al.
Publicado: (2024)

Online Learning for Uninformed Markov Games: Empirical Nash-Value Regret and Non-Stationarity Adaptation
por: Liu, Junyan, et al.
Publicado: (2026)

Asymptotically Optimal Regret for Black-Box Predict-then-Optimize
por: Tan, Samuel, et al.
Publicado: (2024)

Near-Optimal Regret for Distributed Adversarial Bandits: A Black-Box Approach
por: Qiu, Hao, et al.
Publicado: (2026)

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

From Debate to Equilibrium: Belief-Driven Multi-Agent LLM Reasoning via Bayesian Nash Equilibrium
por: Yi, Xie, et al.
Publicado: (2025)

An Exponentially Converging Particle Method for the Mixed Nash Equilibrium of Continuous Games
por: Wang, Guillaume, et al.
Publicado: (2022)

On the Limitations and Possibilities of Nash Regret Minimization in Zero-Sum Matrix Games under Noisy Feedback
por: Maiti, Arnab, et al.
Publicado: (2023)

Data Poisoning to Fake a Nash Equilibrium in Markov Games
por: Wu, Young, et al.
Publicado: (2023)

Iterative Nash Policy Optimization: Aligning LLMs with General Preferences via No-Regret Learning
por: Zhang, Yuheng, et al.
Publicado: (2024)

Regret Analysis for Randomized Gaussian Process Upper Confidence Bound
por: Takeno, Shion, et al.
Publicado: (2024)

No-Regret Thompson Sampling for Finite-Horizon Markov Decision Processes with Gaussian Processes
por: Bayrooti, Jasmine, et al.
Publicado: (2025)

No-Regret Gaussian Process Optimization of Time-Varying Functions
por: Mauduit, Eliabelle, et al.
Publicado: (2025)

Policy Optimization finds Nash Equilibrium in Regularized General-Sum LQ Games
por: Zaman, Muhammad Aneeq uz, et al.
Publicado: (2024)

Safety Game: Inference-Time Alignment of Black-Box LLMs via Constrained Optimization
por: Nguyen, Tuan, et al.
Publicado: (2025)

Safe Multi-Agent Reinforcement Learning with Convergence to Generalized Nash Equilibrium
por: Li, Zeyang, et al.
Publicado: (2024)

Gaussian Process Upper Confidence Bound Achieves Nearly-Optimal Regret in Noise-Free Gaussian Process Bandits
por: Iwazaki, Shogo
Publicado: (2025)

AlphaExploitem: Going Beyond the Nash Equilibrium in Poker by Learning to Exploit Suboptimal Play
por: Murgoci, Vlad, et al.
Publicado: (2026)

Frequentist Regret Analysis of Gaussian Process Thompson Sampling via Fractional Posteriors
por: Roy, Somjit, et al.
Publicado: (2026)

Regret Analysis of Guided Diffusion for Black-Box Optimization over Structured Inputs
por: Adachi, Masaki, et al.
Publicado: (2026)

Approximating Nash Equilibria in General-Sum Games via Meta-Learning
por: Sychrovský, David, et al.
Publicado: (2025)

An Efficient Black-Box Reduction from Online Learning to Multicalibration, and a New Route to $Φ$-Regret Minimization
por: Farina, Gabriele, et al.
Publicado: (2026)

Adaptive Exponential Integration for Stable Gaussian Mixture Black-Box Variational Inference
por: Che, Baojun, et al.
Publicado: (2026)

Improved Regret Bounds for Gaussian Process Upper Confidence Bound in Bayesian Optimization
por: Iwazaki, Shogo
Publicado: (2025)

Mixed Strategy Nash Equilibrium for Crowd Navigation
por: Sun, Max Muchen, et al.
Publicado: (2024)

An Online Feasible Point Method for Benign Generalized Nash Equilibrium Problems
por: Sachs, Sarah, et al.
Publicado: (2024)

Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit Optimization
por: Tran-The, Hung, et al.
Publicado: (2022)

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing
por: Xian, Ruicheng, et al.
Publicado: (2025)

Nash CoT: Multi-Path Inference with Preference Equilibrium
por: Zhang, Ziqi, et al.
Publicado: (2024)

Adapting to Online Distribution Shifts in Deep Learning: A Black-Box Approach
por: Baby, Dheeraj, et al.
Publicado: (2025)

Posterior Sampling Reinforcement Learning with Gaussian Processes for Continuous Control: Sublinear Regret Bounds for Unbounded State Spaces
por: Flynn, Hamish, et al.
Publicado: (2026)

Tighter Regret Lower Bound for Gaussian Process Bandits with Squared Exponential Kernel in Hypersphere
por: Iwazaki, Shogo
Publicado: (2026)

Data-Free Black-Box Federated Learning via Zeroth-Order Gradient Estimation
por: Ma, Xinge, et al.
Publicado: (2025)

Faster Rates for No-Regret Learning in General Games via Cautious Optimism
por: Soleymani, Ashkan, et al.
Publicado: (2025)

Constraint Learning in Multi-Agent Dynamic Games from Demonstrations of Local Nash Interactions
por: Zhang, Zhouyu, et al.
Publicado: (2025)

Fairness-Aware Meta-Learning via Nash Bargaining
por: Zeng, Yi, et al.
Publicado: (2024)

A Black-Box Debiasing Framework for Conditional Sampling
por: Cui, Han, et al.
Publicado: (2025)