This paper addresses the challenge of optimizing meta-parameters (i.e.,
hyperparameters) in machine learning algorithms, a critical factor influencing
training efficiency and model performance. Moving away from the computationally
expensive traditional meta-parameter search methods, we introduce MetaOptimize
framework that dynamically adjusts meta-parameters, particularly step sizes
(also known as learning rates), during training. More specifically,
MetaOptimize can wrap around any first-order optimization algorithm, tuning
step sizes on the fly to minimize a specific form of regret that accounts for
long-term effect of step sizes on training, through a discounted sum of future
losses. We also introduce low complexity variants of MetaOptimize that, in
conjunction with its adaptability to multiple optimization algorithms,
demonstrate performance competitive to those of best hand-crafted learning rate
schedules across various machine learning applications.

优化元参数（即超参数）对于训练效率和模型性能至关重要，本文介绍了 MetaOptimize 框架，它通过动态调整元参数（特别是学习速率）来优化训练过程，通过折现未来损失的累积和来最小化后悔，通过适应多种优化算法和引入低复杂度变体，实现与手工设计的学习率计划相媲美的性能，在各种机器学习应用中展现竞争力。

元参数优化的框架：MetaOptimize

MetaOptimize: A Framework for Optimizing Step Sizes and Other  Meta-parameters

Network Morphism based Neural Architecture Search (NAS) is one of the most
efficient methods, however, knowing where and when to add new neurons or remove
dis-functional ones is generally left to black-box Reinforcement Learning
models. In this paper, we present a new Network Morphism based NAS called Noisy
Heuristics NAS which uses heuristics learned from manually developing neural
network models and inspired by biological neuronal dynamics. Firstly, we add
new neurons randomly and prune away some to select only the best fitting
neurons. Secondly, we control the number of layers in the network using the
relationship of hidden units to the number of input-output connections. Our
method can increase or decrease the capacity or non-linearity of models online
which is specified with a few meta-parameters by the user. Our method
generalizes both on toy datasets and on real-world data sets such as MNIST,
CIFAR-10, and CIFAR-100. The performance is comparable to the hand-engineered
architecture ResNet-18 with the similar parameters.

本文介绍了一种基于网络态势的神经结构搜索方法 - Noisy Heuristics NAS，该方法利用人工智能动态学中所学得的启发式算法和生物神经元的动态特性，通过添加和修剪神经元以及控制网络中隐含层数量的关系等元参数的方式，可以在线增加或减少神经网络模型的容量或非线性，进而实现对神经结构的扩展和优化。实验结果在 MNIST、CIFAR-10 和 CIFAR-100 等数据集上表现良好，并与手工构建的 ResNet-18 结构相当。

基于网络同构和启发式的噪声模型神经架构搜索

Noisy Heuristics NAS: A Network Morphism based Neural Architecture Search using Heuristics

The efficiency of reinforcement learning algorithms depends critically on a
few meta-parameters that modulates the learning updates and the trade-off
between exploration and exploitation. The adaptation of the meta-parameters is
an open question in reinforcement learning, which arguably has become more of
an issue recently with the success of deep reinforcement learning in
high-dimensional state spaces. The long learning times in domains such as Atari
2600 video games makes it not feasible to perform comprehensive searches of
appropriate meta-parameter values. We propose the Online Meta-learning by
Parallel Algorithm Competition (OMPAC) method. In the OMPAC method, several
instances of a reinforcement learning algorithm are run in parallel with small
differences in the initial values of the meta-parameters. After a fixed number
of episodes, the instances are selected based on their performance in the task
at hand. Before continuing the learning, Gaussian noise is added to the
meta-parameters with a predefined probability. We validate the OMPAC method by
improving the state-of-the-art results in stochastic SZ-Tetris and in standard
Tetris with a smaller, 10$\times$10, board, by 31% and 84%, respectively, and
by improving the results for deep Sarsa($\lambda$) agents in three Atari 2600
games by 62% or more. The experiments also show the ability of the OMPAC method
to adapt the meta-parameters according to the learning progress in different
tasks.

该研究提出了一种名为 OMPAC 的在线元学习算法用于调整强化学习算法中关键的元参数以提高效率，实验证明，该方法可以在 Atari 2600 视频游戏和 Tetris 的问题上实现优越的性能。