Statistical Learning
Counterfactual Predictions under Runtime Confounding
The type of information that these tools need to convey is often counterfactual in nature. Decision-makers need to know what is likely to happen if they choose to take a particular action.
Function-Space Distributions over Kernels
Gregory Benton, Wesley J. Maddox, Jayson Salkey, Julio Albinati, Andrew Gordon Wilson
Neural Information Processing Systems http://nips.cc/
Robust PCA with compressed data
Wooseok Ha, Rina Foygel Barber
Neural Information Processing Systems http://nips.cc/
Predictive Inference Is Free with the Jackknife+-after-Bootstrap
Ensemble learning is a popular technique for enhancing the performance of machine learning algorithms.
Distributed Submodular Cover: Succinctly Summarizing Massive Data
Baharan Mirzasoleiman, Amin Karbasi, Ashwinkumar Badanidiyuru, Andreas Krause
Neural Information Processing Systems http://nips.cc/
A Universal Catalyst for First-Order Optimization
Hongzhou Lin, Julien Mairal, Zaid Harchaoui
Neural Information Processing Systems http://nips.cc/
From Predictions to Decisions: Using L kahead Regularization
But when deployed transparently, learned models also affect how users act in order to improve outcomes. The standard approach to learning predictive models is agnostic to induced user actions and provides no guarantees as to the effect of actions.
