NIPS 2017 - presentations from the Probabilistic Methods, Applications sessions

• Reliable Decision Support using Counterfactual Models • Convolutional Gaussian Processes • Counterfactual Fairness • An Empirical Bayes Approach to Optimizing Machine Learning Algorithms • PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference • Multiresolution Kernel Approximation for Gaussian Process Regression • Multi-Information Source Optimization • Doubly Stochastic Variational Inference for Deep Gaussian Processes • Permutation-based Causal Inference Algorithms with Interventions • Gradients of Generative Models for Improved Discriminative Analysis of Tandem Mass Spectra • Style Transfer from Non-parallel Text by Cross-Alignment • Premise Selection for Theorem Proving by Deep Graph Embedding • Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks • Unsupervised Learning of Disentangled Representations from Video