1 citations · 1 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
Certain and Approximately Certain Models for Statistical Learning
Cheng Zhen, Nischal Aryal, Arash Termehchy +2
Real-world data is often incomplete and contains missing values. To train accurate models over real-world datasets, users need to spend a substantial amount of time and resources i…
How Does User Behavior Evolve During Exploratory Visual Analysis?
Sanad Saha, Nischal Aryal, Leilani Battle +1
Exploratory visual analysis (EVA) is an essential stage of the data science pipeline, where users often lack clear analysis goals at the start and iteratively refine them as they l…