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Poster
FasterRisk: Fast and Accurate Interpretable Risk Scores
Jiachang Liu · Chudi Zhong · Boxuan Li · Margo Seltzer · Cynthia Rudin

Wed Nov 30 02:00 PM -- 04:00 PM (PST) @ Hall J #615
in Poster Session 4 »

Over the last century, risk scores have been the most popular form of predictive model used in healthcare and criminal justice. Risk scores are sparse linear models with integer coefficients; often these models can be memorized or placed on an index card. Typically, risk scores have been created either without data or by rounding logistic regression coefficients, but these methods do not reliably produce high-quality risk scores. Recent work used mathematical programming, which is computationally slow. We introduce an approach for efficiently producing a collection of high-quality risk scores learned from data. Specifically, our approach produces a pool of almost-optimal sparse continuous solutions, each with a different support set, using a beam-search algorithm. Each of these continuous solutions is transformed into a separate risk score through a "star ray" search, where a range of multipliers are considered before rounding the coefficients sequentially to maintain low logistic loss. Our algorithm returns all of these high-quality risk scores for the user to consider. This method completes within minutes and can be valuable in a broad variety of applications.

Keywords: interpretability · Rashomon set · risk scores · scoring system
[ Paper]  [ Poster]  [ OpenReview] 

Author Information

Jiachang Liu (Duke University)
Chudi Zhong (Duke University)
Boxuan Li (Duke University)
Margo Seltzer (University of British Columbia)

**MARGO I. SELTZER** is Canada 150 Research Chair in Computer Systems and the Cheriton Family chair in Computer Science at the University of British Columbia. Her research interests are in systems, construed quite broadly: systems for capturing and accessing data provenance, file systems, databases, transaction processing systems, storage and analysis of graph-structured data, new architectures for parallelizing execution, and systems that apply technology to problems in healthcare. She is the author of several widely-used software packages including database and transaction libraries and the 4.4BSD log-structured file system. Dr. Seltzer was a co-founder and CTO of Sleepycat Software, the makers of Berkeley DB, recipient of the 2020 ACM SIGMOD Systems Award. She serves on Advisory Council for the Canadian COVID alert app and the Computer Science and Telecommunications Board (CSTB) of the (US) National Academies. She is a past President of the USENIX Assocation and served as the USENIX representative to the Computing Research Association Board of Directors and on the Computing Community Consortium. She is a member of the National Academy of Engineering, the American Academy of Arts and Sciences, a Sloan Foundation Fellow in Computer Science, an ACM Fellow, a Bunting Fellow, and was the recipient of the 1996 Radcliffe Junior Faculty Fellowship. She is recognized as an outstanding teacher and mentor, having received the Phi Beta Kappa teaching award in 1996, the Abrahmson Teaching Award in 1999, the Capers and Marion McDonald Award for Excellence in Mentoring and Advising in 2010, and the CRA-E Undergraduate Research Mentoring Award in 2017. Professor Seltzer received an A.B. degree in Applied Mathematics from Harvard/Radcliffe College and a Ph. D. in Computer Science from the University of California, Berkeley.

Cynthia Rudin (Duke)

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