With the increasing adoption of machine learning (ML) models and systems in high-stakes
settings across different industries, guaranteeing a model's performance after deployment …

Artificial Intelligence (AI) is increasingly playing an integral role in determining our day-to-
day experiences. Increasingly, the applications of AI are no longer limited to search and …

Estimating the quantiles of a large dataset is a fundamental problem in both the streaming
algorithms literature and the differential privacy literature. However, all existing private …

Predictive models are increasingly used to make various consequential decisions in high-
stakes domains such as healthcare, finance, and policy. It becomes critical to ensure that …

Data pipelines (ie, converting raw data to features) are critical for machine learning (ML)
models, yet their development and management is time-consuming. Feature stores have …

Median absolute deviation (MAD), the median of the absolute deviations from the median,
has been found useful in various applications such as outlier detection. Together with …

Stream monitoring is fundamental in many data stream applications, such as financial data
trackers, security, anomaly detection, and load balancing. In that respect, quantiles are of …

Data summaries (aka, sketches) are compact data structures that can be updated flexibly
and efficiently to capture certain properties of a data set. Well-known examples include set …

The research area of data summarization seeks to find small data structures that can be
updated flexibly, and answer certain queries on the input accurately. Summaries are widely …

Predictive models are increasingly used to make various consequential decisions in high-
stakes domains such as healthcare, finance, and policy. It becomes critical to ensure that …