In contrast to offline working fashions, two research paradigms are devised for online learning: (1) Online Meta Learning (OML) learns good priors over model parameters (or learning to learn) in a sequential setting where tasks are revealed one after another. Although it provides a sub-linear...
Meta-learningDeep learningArtificial intelligenceFairness has become a critical value online, and the latest studies consider it in many problems. In recommender systems, fairness is important since the visibility of items is controlled by systems. Previous fairness-aware recommender systems assume that ...
To address issues of group-level fairness in machine learning, it is natural to adjust model parameters based on specific fairness objectives over a sensitive-attributed validation set. Such an adjustment procedure can be cast within a meta-learning framework. However, naive integration of fairness ...
We propose a meta-learning-based cold-start recommendation framework called FaRM to alleviate the unfairness of recommendations. The proposed framework consists of three steps. We first propose a fairness-aware meta-path generation method to eliminate bias in sensitive attributes. In addition, we ...
Fairness-aware classifier with prejudice remover regularizer. In Joint Eur. Conf. Machine Learning and Knowledge Discovery in Databases 35–50 (Springer, 2012). Zafar, M. B., Valera, I., Rogriguez, M. G. & Gummadi, K. P. Fairness constraints: mechanisms for fair classification. In ...
Scalable Meta-Evaluation of LLMs as Evaluators via Agent Debate, Preprint 2024. [Paper] EvalLM: Interactive Evaluation of Large Language Model Prompts on User-Defined Criteria, CHI 2024. [Paper] LLMs as Narcissistic Evaluators: When Ego Inflates Evaluation Scores, Preprint 2023. [Paper] ...
2.以感知差异( impression variance-aware)对广告进行重排,个性化计算不适用PC属性,即VRS; 3.对公平性衡量指标等进行实验和线上AB,最终用于全美住房广告; VRS是现有在线广告系统公开的第一个用于提升隐私数据公平性的大规模架构方案,用来减轻PC属性所带来的算法偏见。
Recent years have seen the rapid development of fairness-aware machine learning in mitigating unfairness or discrimination in decision-making in a wide range of applications. However, much less attention has been paid to the fairness-aware multi-objective optimization, which is indeed commonly seen in...
Mary, J., Calauzenes, C., El Karoui, N.: Fairness-aware learning for continuous attributes and treatments. In: Proceedings of Machine Learning Research. 36th International Conference on Machine Learning (ICML), vol. 97, pp. 4382–4391 (2019) McCullagh, P., Nelder, J.A.: Generalized Lin...
it is problematic if large and well-known companies implement algorithms without being aware of the possible pitfalls and negative consequences. Thus, to move the field forward, it is paramount to systematically review and synthesize existing knowledge about biases and discrimination in algorithmic decisi...