PAPIs 2017 Proceedings of Machine Learning Research (PMLR) Vol. 82

  • K. Gretchen Greene DragonPaint: Rule Based Bootstrapping for Small Data with an Application to Cartoon Coloring
  • Mark Hamilton, Sudarshan Raghunathan, Akshaya Annavajhala, Danil Kirsanov, Eduardo Leon, Eli Barzilay, Ilya Matiach, Joe Davison, Maureen Busch, Miruna Oprescu, Ratan Sur, Roope Astala, Tong Wen, ChangYoung Park Flexible and Scalable Deep Learning with MMLSpark
  • Harlan D. Harris An Architecture and Domain Specific Language Framework for Repeated Domain-Specific Predictive Modeling
  • Lucas B. Miguel, Daniel Takabayashi, Jose R. Pizani, Tiago Andrade, Brody West Marvin - Open source artificial intelligence platform
  • Thomas Vandal, Max Livingston, Camen Piho, Sam Zimmerman Prediction and Uncertainty Quantification of Daily Airport Flight Delays

PAPIs 2016 Proceedings of Machine Learning Research (PMLR) Vol. 67

  • Pierre Gutierrez, Jean-Yves Gérardy Causal Inference and Uplift Modelling: A Review of the Literature
  • Li Erran Li, Eric Chen, Jeremy Hermann, Pusheng Zhang, Luming Wang Scaling Machine Learning as a Service

PAPIs 2015 Proceedings of Machine Learning Research (PMLR) Vol. 50

  • AzureML Team AzureML: Anatomy of a machine learning service
  • Brian Gawalt Deploying high throughput predictive models with the actor framework
  • James Montgomery, Mark D. Reid, Barry Drake Protocols and Structures for Inference: A RESTful API for Machine Learning
  • Atakan Cetinsoy, Francisco J. Martin, José Antonio Ortega, Poul Petersen The Past, Present, and Future of Machine Learning APIs