The field of algorithmic optimization has significantly advanced with the
development of methods for the automatic configuration of algorithmic
parameters. This article delves into the Algorithm Configuration Problem,
focused on optimizing parametrized algorithms for solving specific instances of
decision/optimization problems. We present a comprehensive framework that not
only formalizes the Algorithm Configuration Problem, but also outlines
different approaches for its resolution, leveraging machine learning models and
heuristic strategies. The article categorizes existing methodologies into
per-instance and per-problem approaches, distinguishing between offline and
online strategies for model construction and deployment. By synthesizing these
approaches, we aim to provide a clear pathway for both understanding and
addressing the complexities inherent in algorithm configuration.

算法优化领域已经通过自动配置算法参数的方法显著进展。本文深入研究了算法配置问题，重点优化针对特定决策 / 优化问题实例的参数化算法。我们提出了一个全面的框架，不仅形式化了算法配置问题，还概述了利用机器学习模型和启发式策略解决该问题的不同方法。本文将已有的方法论分为按实例和按问题的方法，并区分了模型构建和部署的离线和在线策略。通过综合这些方法，我们旨在为理解和应对算法配置中固有的复杂性提供清晰的路径。

算法配置问题

The Algorithm Configuration Problem

The research on neural network (NN) based image compression has shown
superior performance compared to classical compression frameworks. Unlike the
hand-engineered transforms in the classical frameworks, NN-based models learn
the non-linear transforms providing more compact bit representations, and
achieve faster coding speed on parallel devices over their classical
counterparts. Those properties evoked the attention of both scientific and
industrial communities, resulting in the standardization activity JPEG-AI. The
verification model for the standardization process of JPEG-AI is already in
development and has surpassed the advanced VVC intra codec. To generate
reconstructed images with the desired bits per pixel and assess the BD-rate
performance of both the JPEG-AI verification model and VVC intra, bit rate
matching is employed. However, the current state of the JPEG-AI verification
model experiences significant slowdowns during bit rate matching, resulting in
suboptimal performance due to an unsuitable model. The proposed methodology
offers a gradual algorithmic optimization for matching bit rates, resulting in
a fourfold acceleration and over 1% improvement in BD-rate at the base
operation point. At the high operation point, the acceleration increases up to
sixfold.

通过渐进式的算法优化，目前的 JPEG-AI 验证模型在匹配比特率时有了四倍的加速，基本操作点的 BD 率性能提高了 1%，在高操作点，加速增加至六倍。

JPEG-AI 验证模型中的比特率匹配算法优化

Bit Rate Matching Algorithm Optimization in JPEG-AI Verification Model

Ad platforms such as Facebook, Google and LinkedIn promise value for
advertisers through their targeted advertising. However, multiple studies have
shown that ad delivery on such platforms can be skewed by gender or race due to
hidden algorithmic optimization by the platforms, even when not requested by
the advertisers. Building on prior work measuring skew in ad delivery, we
develop a new methodology for black-box auditing of algorithms for
discrimination in the delivery of job advertisements. Our first contribution is
to identify the distinction between skew in ad delivery due to protected
categories such as gender or race, from skew due to differences in
qualification among people in the targeted audience. This distinction is
important in U.S. law, where ads may be targeted based on qualifications, but
not on protected categories. Second, we develop an auditing methodology that
distinguishes between skew explainable by differences in qualifications from
other factors, such as the ad platform's optimization for engagement or
training its algorithms on biased data. Our method controls for job
qualification by comparing ad delivery of two concurrent ads for similar jobs,
but for a pair of companies with different de facto gender distributions of
employees. We describe the careful statistical tests that establish evidence of
non-qualification skew in the results. Third, we apply our proposed methodology
to two prominent targeted advertising platforms for job ads: Facebook and
LinkedIn. We confirm skew by gender in ad delivery on Facebook, and show that
it cannot be justified by differences in qualifications. We fail to find skew
in ad delivery on LinkedIn. Finally, we suggest improvements to ad platform
practices that could make external auditing of their algorithms in the public
interest more feasible and accurate.

本文提出了一种黑盒审核算法检测招聘广告投放中的歧视现象的新方法，通过对两个公司的类似工作广告的同时投放来控制工作资质的差异，验证了 Facebook 招聘广告投放的性别偏差，证明了该现象不能通过资质差异来解释，而 LinkedIn 广告投放则未出现此类现象。