3 papers
cs.LG2026
Learning with Conflicts of Interest
Nischal Aryal, Arash Termehchy, Ali Vakilian +1
Financial, social, and political factors often prevent the interests of the owners of ML systems and services and their users from being perfectly aligned. ML systems often produce…
cs.LG2026
Learning Over Dirty Data with Minimal Repairs
Cheng Zhen, Prayoga, Nischal Aryal +3
Missing data often exists in real-world datasets, requiring significant time and effort for data repair to learn accurate models. In this paper, we show that imputing all missing v…
cs.DB2026
Querying with Conflicts of Interest
Nischal Aryal, Arash Termehchy, Marianne Winslett
Conflicts of interest often arise between data sources and their users regarding how the users' information needs should be interpreted by the data source. For example, an online p…