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Found 9 papers, 0 papers with code
Proceedings of NeurIPS 2018 Workshop on Machine Learning for the Developing World: Achieving Sustainable Impact
This is the Proceedings of NeurIPS 2018 Workshop on Machine Learning for the Developing World: Achieving Sustainable Impact, held in Montreal, Canada on December 8, 2018
Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction
We provide a model-agnostic formalization of change surfaces, illustrating how they can provide variable, heterogeneous, and non-monotonic rates of change across multiple dimensions.
Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data
We introduce methods for identifying anomalous patterns in non-iid data by combining Gaussian processes with novel log-likelihood ratio statistic and subset scanning techniques.
Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning
This is the Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning, held in Long Beach, California, USA on December 7, 2017
Proceedings of NIPS 2017 Workshop on Machine Learning for the Developing World
This is the Proceedings of NIPS 2017 Workshop on Machine Learning for the Developing World, held in Long Beach, California, USA on December 8, 2017
Machine Learning for Drug Overdose Surveillance
We describe two recently proposed machine learning approaches for discovering emerging trends in fatal accidental drug overdoses.
Proceedings of NIPS 2016 Workshop on Interpretable Machine Learning for Complex Systems
This is the Proceedings of NIPS 2016 Workshop on Interpretable Machine Learning for Complex Systems, held in Barcelona, Spain on December 9, 2016
Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces
We present a scalable Gaussian process model for identifying and characterizing smooth multidimensional changepoints, and automatically learning changes in expressive covariance structure.
Lass-0: sparse non-convex regression by local search
We compute approximate solutions to L0 regularized linear regression using L1 regularization, also known as the Lasso, as an initialization step.
