This paper investigates the relationships between hyperparameters of machine
learning and fairness. Data-driven solutions are increasingly used in critical
socio-technical applications where ensuring fairness is important. Rather than
explicitly encoding decision logic via control and data structures, the ML
developers provide input data, perform some pre-processing, choose ML
algorithms, and tune hyperparameters (HPs) to infer a program that encodes the
decision logic. Prior works report that the selection of HPs can significantly
influence fairness. However, tuning HPs to find an ideal trade-off between
accuracy, precision, and fairness has remained an expensive and tedious task.
Can we predict fairness of HP configuration for a given dataset? Are the
predictions robust to distribution shifts?
We focus on group fairness notions and investigate the HP space of 5 training
algorithms. We first find that tree regressors and XGBoots significantly
outperformed deep neural networks and support vector machines in accurately
predicting the fairness of HPs. When predicting the fairness of ML
hyperparameters under temporal distribution shift, the tree regressors
outperforms the other algorithms with reasonable accuracy. However, the
precision depends on the ML training algorithm, dataset, and protected
attributes. For example, the tree regressor model was robust for training data
shift from 2014 to 2018 on logistic regression and discriminant analysis HPs
with sex as the protected attribute; but not for race and other training
algorithms. Our method provides a sound framework to efficiently perform
fine-tuning of ML training algorithms and understand the relationships between
HPs and fairness.

通过研究机器学习的超参数与公正之间的关系，本文提供了一种有效的方法来理解超参数和公正之间的关系，以及在训练算法中进行细致调整的框架。

预测机器学习软件配置的公平性

Predicting Fairness of ML Software Configuration

Current federated learning (FL) approaches view decentralized training data
as a single table, divided among participants either horizontally (by rows) or
vertically (by columns). However, these approaches are inadequate for handling
distributed relational tables across databases. This scenario requires
intricate SQL operations like joins and unions to obtain the training data,
which is either costly or restricted by privacy concerns. This raises the
question: can we directly run FL on distributed relational tables?
In this paper, we formalize this problem as relational federated learning
(RFL). We propose TablePuppet, a generic framework for RFL that decomposes the
learning process into two steps: (1) learning over join (LoJ) followed by (2)
learning over union (LoU). In a nutshell, LoJ pushes learning down onto the
vertical tables being joined, and LoU further pushes learning down onto the
horizontal partitions of each vertical table. TablePuppet incorporates
computation/communication optimizations to deal with the duplicate tuples
introduced by joins, as well as differential privacy (DP) to protect against
both feature and label leakages. We demonstrate the efficiency of TablePuppet
in combination with two widely-used ML training algorithms, stochastic gradient
descent (SGD) and alternating direction method of multipliers (ADMM), and
compare their computation/communication complexity. We evaluate the SGD/ADMM
algorithms developed atop TablePuppet by training diverse ML models. Our
experimental results show that TablePuppet achieves model accuracy comparable
to the centralized baselines running directly atop the SQL results. Moreover,
ADMM takes less communication time than SGD to converge to similar model
accuracy.

将分布式关系型表上的联合学习问题形式化为关系型联邦学习（RFL），并提出 TablePuppet 作为一个通用框架，将学习过程分解为联接学习和并集学习两个步骤，通过优化数据库操作和隐私保护，实现了与 SQL 结果直接运行的中心化基线相媲美的模型准确性。同时，TablePuppet 中的 ADMM 算法在收敛到相似模型准确性时比 SGD 算法花费更少的通信时间。