Bookmarks (24) clear filters
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Programmatically interpretable reinforcement learning
Programmatically interpretable reinforcement learning, Verma et al., ICML 2018 Being able to trust (interpret, verify) a...
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Challenges of real-world reinforcement learning
Challenges of real-world reinforcement learning, Dulac-Arnold et al., ICML’19 Last week we looked at some of...
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Optimized risk scores
Optimized risk scores Ustun & Rudin, KDD’17 On Monday we looked at the case for interpretable...
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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...
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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...
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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...
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Applying deep learning to Airbnb search
Applying deep learning to Airbnb search Haldar et al., KDD’19 Last time out we looked at...
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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...
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Declarative recursive computation on an RDBMS
Declarative recursive computation on an RDBMS… or, why you should use a database for distributed machine...
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Snuba: automating weak supervision to label training data
Snuba: automating weak supervision to label training data Varma & Ré, VLDB 2019 This week we’re...
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Learning to prove theorems via interacting with proof assistants
Learning to prove theorems via interacting with proof assistants Yang & Deng, ICML’19 Something a little...
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Statistical foundations of virtual democracy
Statiscal foundations of virtual democracy Kahng et al., ICML’19 This is another paper on the theme...
