279 citations · 304 across the 9 of their papers we have counts for
10 papers
Investors Embrace Gender Diversity, Not Female CEOs: The Role of Gender in Startup Fundraising
Christopher Cassion, Yuhang Qian, Constant Bossou +1
The allocation of venture capital is one of the primary factors determining who takes products to market, which startups succeed or fail, and as such who gets to participate in the…
To Cluster, or Not to Cluster: An Analysis of Clusterability Methods
A. Adolfsson, M. Ackerman, N. C. Brownstein
Clustering is an essential data mining tool that aims to discover inherent cluster structure in data. For most applications, applying clustering is only appropriate when cluster st…
Uncovering Group Level Insights with Accordant Clustering
Amit Dhurandhar, Margareta Ackerman, Xiang Wang
Clustering is a widely-used data mining tool, which aims to discover partitions of similar items in data. We introduce a new clustering paradigm, \emph{accordant clustering}, which…
Algorithmic Songwriting with ALYSIA
Margareta Ackerman, David Loker
This paper introduces ALYSIA: Automated LYrical SongwrIting Application. ALYSIA is based on a machine learning model using Random Forests, and we discuss its success at pitch and r…
An Effective and Efficient Approach for Clusterability Evaluation
Margareta Ackerman, Andreas Adolfsson, Naomi Brownstein
Clustering is an essential data mining tool that aims to discover inherent cluster structure in data. As such, the study of clusterability, which evaluates whether data possesses s…
When is Clustering Perturbation Robust?
Margareta Ackerman, Jarrod Moore
Clustering is a fundamental data mining tool that aims to divide data into groups of similar items. Generally, intuition about clustering reflects the ideal case -- exact data sets…