A model among many may only be best under certain states of the world.
Switching from a model to another can also be costly. Finding a procedure to
dynamically choose a model in these circumstances requires to solve a complex
estimation procedure and a dynamic programming problem. A Reinforcement
learning algorithm is used to approximate and estimate from the data the
optimal solution to this dynamic programming problem. The algorithm is shown to
consistently estimate the optimal policy that may choose different models based
on a set of covariates. A typical example is the one of switching between
different portfolio models under rebalancing costs, using macroeconomic
information. Using a set of macroeconomic variables and price data, an
empirical application to the aforementioned portfolio problem shows superior
performance to choosing the best portfolio model with hindsight.

The paper introduces a reinforcement learning-based method for model selection in the presence of changing circumstances, particularly in the context of portfolio management with rebalancing costs, demonstrating superior performance compared to hindsight-based model selection.

动作 - 状态依赖的动态模型选择

Action-State Dependent Dynamic Model Selection

Becoming a (super) hero is almost every kid's dream. During their sheltered
childhood, they do whatever it takes to grow up to be one. Work hard, play hard
-- all day long. But as they're getting older, distractions are more and more
likely to occur. They're getting off track. They start discovering what is
feared as simple math. Finally, they end up as a researcher, writing boring,
non-impressive papers all day long because they only rely on simple
mathematics. No top-tier conferences, no respect, no groupies. Life's over.
To finally put an end to this tragedy, we propose a fundamentally new
algorithm, dubbed zero2hero, that turns every research paper into a scientific
masterpiece. Given a LaTeX document containing ridiculously simple math, based
on next-generation large language models, our system automatically
over-complicates every single equation so that no one, including yourself, is
able to understand what the hell is going on. Future reviewers will be blown
away by the complexity of your equations, immediately leading to acceptance.
zero2hero gets you back on track, because you deserve to be a
hero$^{\text{TM}}$. Code leaked at
https://github.com/mweiherer/zero2hero.

提出一种名为 zero2hero 的算法，基于大型语言模型，该算法使得包含了简单数学的 LaTeX 文档的每个公式都变得极为复杂，从而提高论文的复杂度，提高风评和被接受的概率。

从零到英雄：用极其复杂的数学说服

From Zero to Hero: Convincing with Extremely Complicated Math

Comment on ``Understanding OR, PS and DR'' [arXiv:0804.2958]

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