Probabilistic Methods, Applications sessions at NIPS 2017

Presentations from the Probabilistic Methods, Applications sessions: 02:10 Reliable Decision Support using Counterfactual Models 17:06 Convolutional Gaussian Processes 28:46 Counterfactual Fairness 48:30 An Empirical Bayes Approach to Optimizing Machine Learning Algorithms 53:30 PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference 59:01 Multiresolution Kernel Approximation for Gaussian Process Regression 1:03:44 Multi-Information Source Optimization 1:08:07 Doubly Stochastic Variational Inference for Deep Gaussian Processes 1:13:03 Permutation-based Causal Inference Algorithms with Interventions 1:17:28 Gradients of Generative Models for Improved Discriminative Analysis of Tandem Mass Spectra 1:20:47 Style Transfer from Non-parallel Text by Cross-Alignment 1:25:40 Premise Selection for Theorem Proving by Deep Graph Embedding 1:29:40 Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks 1:34:07 Unsupervised Learning of Disentangled Representations from Video