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pro vyhledávání: '"Xu, Yunbei"'
In this paper, we develop a unified framework for lower bound methods in statistical estimation and interactive decision making. Classical lower bound techniques -- such as Fano's inequality, Le Cam's method, and Assouad's lemma -- have been central
Externí odkaz:
http://arxiv.org/abs/2410.05117
The Robust Satisficing (RS) model is an emerging approach to robust optimization, offering streamlined procedures and robust generalization across various applications. However, the statistical theory of RS remains unexplored in the literature. This
Externí odkaz:
http://arxiv.org/abs/2405.20451
Autor:
Xu, Yunbei, Zeevi, Assaf
We develop a general theory to optimize the frequentist regret for sequential learning problems, where efficient bandit and reinforcement learning algorithms can be derived from unified Bayesian principles. We propose a novel optimization approach to
Externí odkaz:
http://arxiv.org/abs/2310.00806
Autor:
Xu, Yunbei
Learning theory is a dynamic and rapidly evolving field that aims to provide mathematical foundations for designing and understanding the behavior of algorithms and procedures that can learn from data automatically. At the heart of this field lies th
Autor:
Xu, Yunbei, Zeevi, Assaf
We study problem-dependent rates, i.e., generalization errors that scale near-optimally with the variance, the effective loss, or the gradient norms evaluated at the "best hypothesis." We introduce a principled framework dubbed "uniform localized con
Externí odkaz:
http://arxiv.org/abs/2011.06186
Autor:
Xu, Yunbei, Zeevi, Assaf
The principle of optimism in the face of uncertainty is one of the most widely used and successful ideas in multi-armed bandits and reinforcement learning. However, existing optimistic algorithms (primarily UCB and its variants) often struggle to dea
Externí odkaz:
http://arxiv.org/abs/2007.07876
Primal-Dual Hybrid Gradient (PDHG) and Alternating Direction Method of Multipliers (ADMM) are two widely-used first-order optimization methods. They reduce a difficult problem to simple subproblems, so they are easy to implement and have many applica
Externí odkaz:
http://arxiv.org/abs/1811.08937
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