Zobrazeno 1 - 10
of 96
pro vyhledávání: '"MORGENSTERN, JAMIE"'
Clustering is a fundamental problem in machine learning and operations research. Therefore, given the fact that fairness considerations have become of paramount importance in algorithm design, fairness in clustering has received significant attention
Externí odkaz:
http://arxiv.org/abs/2406.15960
Autor:
Bertran, Martin, Tang, Shuai, Kearns, Michael, Morgenstern, Jamie, Roth, Aaron, Wu, Zhiwei Steven
Machine unlearning is motivated by desire for data autonomy: a person can request to have their data's influence removed from deployed models, and those models should be updated as if they were retrained without the person's data. We show that, count
Externí odkaz:
http://arxiv.org/abs/2405.20272
Publikováno v:
Proceedings of the 4th ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO 2024)
As training datasets become increasingly drawn from unstructured, uncontrolled environments such as the web, researchers and industry practitioners have increasingly relied upon data filtering techniques to "filter out the noise" of web-scraped data.
Externí odkaz:
http://arxiv.org/abs/2405.08209
Large language models (LLMs) are increasing in capability and popularity, propelling their application in new domains -- including as replacements for human participants in computational social science, user testing, annotation tasks, and more. In ma
Externí odkaz:
http://arxiv.org/abs/2402.01908
Autor:
Bose, Avinandan, Curmei, Mihaela, Jiang, Daniel L., Morgenstern, Jamie, Dean, Sarah, Ratliff, Lillian J., Fazel, Maryam
This paper studies ML systems that interactively learn from users across multiple subpopulations with heterogeneous data distributions. The primary objective is to provide specialized services for different user groups while also predicting user pref
Externí odkaz:
http://arxiv.org/abs/2312.11846
In critical machine learning applications, ensuring fairness is essential to avoid perpetuating social inequities. In this work, we address the challenges of reducing bias and improving accuracy in data-scarce environments, where the cost of collecti
Externí odkaz:
http://arxiv.org/abs/2312.08559
Autor:
Bertran, Martin, Tang, Shuai, Kearns, Michael, Morgenstern, Jamie, Roth, Aaron, Wu, Zhiwei Steven
Membership inference attacks are designed to determine, using black box access to trained models, whether a particular example was used in training or not. Membership inference can be formalized as a hypothesis testing problem. The most effective exi
Externí odkaz:
http://arxiv.org/abs/2307.03694
The remarkable attention which fair clustering has received in the last few years has resulted in a significant number of different notions of fairness. Despite the fact that these notions are well-justified, they are often motivated and studied in a
Externí odkaz:
http://arxiv.org/abs/2305.19475
Autor:
Globus-Harris, Ira, Gupta, Varun, Jung, Christopher, Kearns, Michael, Morgenstern, Jamie, Roth, Aaron
We show how to take a regression function $\hat{f}$ that is appropriately ``multicalibrated'' and efficiently post-process it into an approximately error minimizing classifier satisfying a large variety of fairness constraints. The post-processing re
Externí odkaz:
http://arxiv.org/abs/2209.07312
Autor:
Ahmadi, Saba, Awasthi, Pranjal, Khuller, Samir, Kleindessner, Matthäus, Morgenstern, Jamie, Sukprasert, Pattara, Vakilian, Ali
In this paper, we propose a natural notion of individual preference (IP) stability for clustering, which asks that every data point, on average, is closer to the points in its own cluster than to the points in any other cluster. Our notion can be mot
Externí odkaz:
http://arxiv.org/abs/2207.03600