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

A recommender system that optimizes its recommendations solely to fit a
user's history of ratings for consumed items can create a filter bubble,
wherein the user does not get to experience items from novel, unseen
categories. One approach to mitigate this undesired behavior is to recommend
items with high potential for serendipity, namely surprising items that are
likely to be highly rated. In this paper, we propose a content-based
formulation of serendipity that is rooted in Bayesian surprise and use it to
measure the serendipity of items after they are consumed and rated by the user.
When coupled with a collaborative-filtering component that identifies similar
users, this enables recommending items with high potential for serendipity. To
facilitate the evaluation of topic-level models for surprise and serendipity,
we introduce a dataset of book reading histories extracted from Goodreads,
containing over 26 thousand users and close to 1.3 million books, where we
manually annotate 449 books read by 4 users in terms of their time-dependent,
topic-level surprise. Experimental evaluations show that models that use
Bayesian surprise correlate much better with the manual annotations of
topic-level surprise than distance-based heuristics, and also obtain better
serendipitous item recommendation performance.

我们提出了一种基于内容的机制，利用贝叶斯惊喜度量消费者收看并给出评分后物品的惊喜性，并结合协同过滤组件来推荐具有高惊喜潜力的物品，从而避免推荐系统中的筛泡现象。通过实验证明，使用贝叶斯惊喜能够更好地与主题级惊喜手动注释的相关性，并获得更好的推荐性能。

基于主题的贝叶斯惊喜和巧合的推荐系统

Topic-Level Bayesian Surprise and Serendipity for Recommender Systems

The human intrinsic desire to pursue knowledge, also known as curiosity, is
considered essential in the process of skill acquisition. With the aid of
artificial curiosity, we could equip current techniques for control, such as
Reinforcement Learning, with more natural exploration capabilities. A promising
approach in this respect has consisted of using Bayesian surprise on model
parameters, i.e. a metric for the difference between prior and posterior
beliefs, to favour exploration. In this contribution, we propose to apply
Bayesian surprise in a latent space representing the agent's current
understanding of the dynamics of the system, drastically reducing the
computational costs. We extensively evaluate our method by measuring the
agent's performance in terms of environment exploration, for continuous tasks,
and looking at the game scores achieved, for video games. Our model is
computationally cheap and compares positively with current state-of-the-art
methods on several problems. We also investigate the effects caused by
stochasticity in the environment, which is often a failure case for
curiosity-driven agents. In this regime, the results suggest that our approach
is resilient to stochastic transitions.

利用人工好奇心提高强化学习系统的探索能力，本文提出了一种利用贝叶斯惊奇度作为衡量模型参数先验和后验之间差异的方法，将其应用于代理模型的潜在空间中，大大降低计算成本，研究表明其对连续任务的环境探索和视频游戏分数的影响要好于当前最先进技术，同时具有对抗随机性环境的鲁棒性。