Collaborative machine learning involves training models on data from multiple
parties but must incentivize their participation. Existing data valuation
methods fairly value and reward each party based on shared data or model
parameters but neglect the privacy risks involved. To address this, we
introduce differential privacy (DP) as an incentive. Each party can select its
required DP guarantee and perturb its sufficient statistic (SS) accordingly.
The mediator values the perturbed SS by the Bayesian surprise it elicits about
the model parameters. As our valuation function enforces a privacy-valuation
trade-off, parties are deterred from selecting excessive DP guarantees that
reduce the utility of the grand coalition's model. Finally, the mediator
rewards each party with different posterior samples of the model parameters.
Such rewards still satisfy existing incentives like fairness but additionally
preserve DP and a high similarity to the grand coalition's posterior. We
empirically demonstrate the effectiveness and practicality of our approach on
synthetic and real-world datasets.

通过差分隐私作为奖励，我们通过引入协作机器学习训练模型，对多个参与方的数据进行价值评估和奖励，并保护隐私风险。实证结果表明，我们的方法在合成和真实数据集上具有有效性和实用性。

私有协同机器学习中的激励机制

Incentives in Private Collaborative Machine Learning

The recently developed Prior-Data Fitted Networks (PFNs) have shown very
promising results for applications in low-data regimes. The TabPFN model, a
special case of PFNs for tabular data, is able to achieve state-of-the-art
performance on a variety of classification tasks while producing posterior
predictive distributions in mere seconds by in-context learning without the
need for learning parameters or hyperparameter tuning. This makes TabPFN a very
attractive option for a wide range of domain applications. However, a major
drawback of the method is its lack of interpretability. Therefore, we propose
several adaptations of popular interpretability methods that we specifically
design for TabPFN. By taking advantage of the unique properties of the model,
our adaptations allow for more efficient computations than existing
implementations. In particular, we show how in-context learning facilitates the
estimation of Shapley values by avoiding approximate retraining and enables the
use of Leave-One-Covariate-Out (LOCO) even when working with large-scale
Transformers. In addition, we demonstrate how data valuation methods can be
used to address scalability challenges of TabPFN. Our proposed methods are
implemented in a package tabpfn_iml and made available at
this https URL

由于先验拟合网络（PFN）在低数据环境中展示出的极具潜力的结果，我们提出了一种适用于 TabPFN 的解释性方法，通过利用该模型的独特性质，我们的适应方法能更高效地计算 Shapley 值的估计以及在处理大规模 Transformer 时使用 Leave-One-Covariate-Out。此外，我们还展示了如何利用数据估值方法来解决 TabPFN 的可扩展性挑战。

可解释的 TabPFN 机器学习

Interpretable Machine Learning for TabPFN

Data Shapley has recently been proposed as a principled framework to quantify
the contribution of individual datum in machine learning. It can effectively
identify helpful or harmful data points for a learning algorithm. In this
paper, we propose Beta Shapley, which is a substantial generalization of Data
Shapley. Beta Shapley arises naturally by relaxing the efficiency axiom of the
Shapley value, which is not critical for machine learning settings. Beta
Shapley unifies several popular data valuation methods and includes data
Shapley as a special case. Moreover, we prove that Beta Shapley has several
desirable statistical properties and propose efficient algorithms to estimate
it. We demonstrate that Beta Shapley outperforms state-of-the-art data
valuation methods on several downstream ML tasks such as: 1) detecting
mislabeled training data; 2) learning with subsamples; and 3) identifying
points whose addition or removal have the largest positive or negative impact
on the model.

本文提出 Beta Shapley，它是 Data Shapley 的实质性泛化，其可作为数种流行的数据估值方法的统一形式，具有理想的统计性质，可用于估计与机器学习相关的模型性能。