Meta-learning neural Bloom filters

Meta-learning neural bloom filters Rae et al., ICML'19 Bloom filters are wonderful things, enabling us to quickly ask whether a given set could possibly contain a certain value. They produce this answer while using minimal space and offering O(1) inserts and lookups. It’s no wonder Bloom filters and their derivatives (the family of approximate set … Continue reading Meta-learning neural Bloom filters

Challenging common assumptions in the unsupervised learning of disentangled representations

Challenging common assumptions in the unsupervised learning of disentangled representations Locatello et al., ICML'19 Today’s paper choice won a best paper award at ICML’19. The ‘common assumptions’ that the paper challenges seem to be: "unsupervised learning of disentangled representations is possible, and useful!" The key idea behind the unsupervised learning of disentangled representations is that … Continue reading Challenging common assumptions in the unsupervised learning of disentangled representations

Data Shapley: equitable valuation of data for machine learning

Data Shapley: equitable valuation of data for machine learning Ghorbani & Zou et al., ICML'19 It’s incredibly difficult from afar to make sense of the almost 800 papers published at ICML this year! In practical terms I was reduced to looking at papers highlighted by others (e.g. via best paper awards), and scanning the list … Continue reading Data Shapley: equitable valuation of data for machine learning

View-centric performance optimization for database-backed web applications

View-centric performance optimization for database-backed web applications Yang et al., ICSE 2019 The problem set-up in this paper discusses the importance of keeping web page load times low as a fundamental contributor to user satisfaction (See e.g. ‘Why performance matters’). Between client-side tools such as Google’s Lighthouse, back-end tools that can analyse ORM usage and … Continue reading View-centric performance optimization for database-backed web applications

Three key checklists and remedies for trustworthy analysis of online controlled experiments at scale

Three key checklists and remedies for trustworthy analysis of online controlled experiments at scale Fabijan et al., ICSE 2019 Last time out we looked at machine learning at Microsoft, where we learned among other things that using an online controlled experiment (OCE) approach to rolling out changes to ML-centric software is important. Prior to that … Continue reading Three key checklists and remedies for trustworthy analysis of online controlled experiments at scale

Software engineering for machine learning: a case study

Software engineering for machine learning: a case study Amershi et al., ICSE'19 Previously on The Morning Paper we’ve looked at the spread of machine learning through Facebook and Google and some of the lessons learned together with processes and tools to address the challenges arising. Today it’s the turn of Microsoft. More specifically, we’ll be … Continue reading Software engineering for machine learning: a case study

Automating chaos experiments in production

Automating chaos experiments in production Basiri et al., ICSE 2019 Are you ready to take your system assurance programme to the next level? This is a fascinating paper from members of Netflix’s Resilience Engineering team describing their chaos engineering initiatives: automated controlled experiments designed to verify hypotheses about how the system should behave under gray … Continue reading Automating chaos experiments in production