Probabilistic Methods, Applications sessions at NIPS 2017
Автор: Steven Van Vaerenbergh
Загружено: 2017-12-17
Просмотров: 813
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
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