Learning certifiably optimal rule lists for categorical data

Learning certifiably optimal rule lists for categorical data Angelino et al., JMLR 2018 Today we’re taking a closer look at CORELS, the Certifiably Optimal RulE ListS algorithm that we encountered in Rudin’s arguments for interpretable models earlier this week. We’ve been able to create rule lists (decision trees) for a long time, e.g. using CART, … Continue reading Learning certifiably optimal rule lists for categorical data

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead Rudin et al., arXiv 2019 With thanks to Glyn Normington for pointing out this paper to me. It’s pretty clear from the title alone what Cynthia Rudin would like us to do! The paper is a mix of technical … Continue reading Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Learning a unified embedding for visual search at Pinterest

Learning a unified embedding for visual search at Pinterest Zhai et al., KDD'19 Last time out we looked at some great lessons from Airbnb as they introduced deep learning into their search system. Today’s paper choice highlights an organisation that has been deploying multiple deep learning models in search (visual search) for a while: Pinterest. … Continue reading Learning a unified embedding for visual search at Pinterest

Applying deep learning to Airbnb search

Applying deep learning to Airbnb search Haldar et al., KDD'19 Last time out we looked at Booking.com’s lessons learned from introducing machine learning to their product stack. Today’s paper takes a look at what happened in Airbnb when they moved from standard machine learning approaches to deep learning. It’s written in a very approachable style … Continue reading Applying deep learning to Airbnb search

150 successful machine learning models: 6 lessons learned at Booking.com

150 successful machine learning models: 6 lessons learned at Booking.com Bernadi et al., KDD'19 Here’s a paper that will reward careful study for many organisations. We’ve previously looked at the deep penetration of machine learning models in the product stacks of leading companies, and also some of the pre-requisites for being successful with it. Today’s … Continue reading 150 successful machine learning models: 6 lessons learned at Booking.com

Declarative recursive computation on an RDBMS

Declarative recursive computation on an RDBMS... or, why you should use a database for distributed machine learing Jankov et al., VLDB'19 If you think about a system like Procella that’s combining transactional and analytic workloads on top of a cloud-native architecture, extensions to SQL for streaming, dataflow based materialized views (see e.g. Naiad, Noria, Multiverses, … Continue reading Declarative recursive computation on an RDBMS