4 citations · 4 across the 3 of their papers we have counts for
6 papers
One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks
Atish Agarwala, Abhimanyu Das, Brendan Juba +4
Can deep learning solve multiple tasks simultaneously, even when they are unrelated and very different? We investigate how the representations of the underlying tasks affect the ab…
Multicalibrated Partitions for Importance Weights
Parikshit Gopalan, Omer Reingold, Vatsal Sharan +1
The ratio between the probability that two distributions and give to points are known as importance weights or propensity scores and play a fundamental role in many dif…
PIDForest: Anomaly Detection via Partial Identification
Parikshit Gopalan, Vatsal Sharan, Udi Wieder
We consider the problem of detecting anomalies in a large dataset. We propose a framework called Partial Identification which captures the intuition that anomalies are easy to dist…
Memory-Sample Tradeoffs for Linear Regression with Small Error
Vatsal Sharan, Aaron Sidford, Gregory Valiant
We consider the problem of performing linear regression over a stream of -dimensional examples, and show that any algorithm that uses a subquadratic amount of memory exhibits a…
Efficient Anomaly Detection via Matrix Sketching
Vatsal Sharan, Parikshit Gopalan, Udi Wieder
We consider the problem of finding anomalies in high-dimensional data using popular PCA based anomaly scores. The naive algorithms for computing these scores explicitly compute the…
Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries
Edward Gan, Jialin Ding, Kai Sheng Tai +2
Interactive analytics increasingly involves querying for quantiles over sub-populations of high cardinality datasets. Data processing engines such as Druid and Spark use mergeable…