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Found 10 papers, 2 papers with code
Incentivizing News Consumption on Social Media Platforms Using Large Language Models and Realistic Bot Accounts
We examine whether our over-time intervention enhances the following of news media organization, the sharing and the liking of news content and the tweeting about politics and the liking of political content.
Revisiting Zero-Shot Abstractive Summarization in the Era of Large Language Models from the Perspective of Position Bias
We characterize and study zero-shot abstractive summarization in Large Language Models (LLMs) by measuring position bias, which we propose as a general formulation of the more restrictive lead bias phenomenon studied previously in the literature.
Robust Fair Clustering: A Novel Fairness Attack and Defense Framework
Experimentally, we observe that CFC is highly robust to the proposed attack and is thus a truly robust fair clustering alternative.
On the Robustness of Deep Clustering Models: Adversarial Attacks and Defenses
Clustering models constitute a class of unsupervised machine learning methods which are used in a number of application pipelines, and play a vital role in modern data science.
Fairness Degrading Adversarial Attacks Against Clustering Algorithms
As a first step, we propose a fairness degrading attack algorithm for k-median clustering that operates under a whitebox threat model -- where the clustering algorithm, fairness notion, and the input dataset are known to the adversary.
Fair Clustering Using Antidote Data
Extensive experiments on different clustering algorithms and fairness notions show that our algorithms can achieve desired levels of fairness on many real-world datasets with a very small percentage of antidote data added.
Fair Algorithms for Hierarchical Agglomerative Clustering
Hierarchical Agglomerative Clustering (HAC) algorithms are extensively utilized in modern data science, and seek to partition the dataset into clusters while generating a hierarchical relationship between the data samples.
Suspicion-Free Adversarial Attacks on Clustering Algorithms
To the best of our knowledge, this is the first work that generates spill-over adversarial samples without the knowledge of the true metric ensuring that the perturbed sample is not an outlier, and theoretically proves the above.
Strong Black-box Adversarial Attacks on Unsupervised Machine Learning Models
We first provide a strong (iterative) black-box adversarial attack that can craft adversarial samples which will be incorrectly clustered irrespective of the choice of clustering algorithm.
An approach to predictively securing critical cloud infrastructures through probabilistic modeling
Cloud infrastructures are being increasingly utilized in critical infrastructures such as banking/finance, transportation and utility management.
