Uncertainty
Boosting Black Box Variational Inference
Francesco Locatello, Gideon Dresdner, Rajiv Khanna, Isabel Valera, Gunnar Raetsch
Posterior distributions depend on the modeling assumptions and can rarely be computed exactly. V ariational Inference (VI) is a technique to approximate posterior distributions through optimization. It involves choosing a set of tractable densities, a.k.a.
Causal Inference via Kernel Deviance Measures
Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh
In many areas of science, we strive to answer questions that are fundamentally causal in nature.
Overlapping Clustering Models, and One (class) SVM to Bind Them All
Xueyu Mao, Purnamrita Sarkar, Deepayan Chakrabarti
Neural Information Processing Systems http://nips.cc/
Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces
Motoya Ohnishi, Masahiro Yukawa, Mikael Johansson, Masashi Sugiyama
Neural Information Processing Systems http://nips.cc/
Contrastive Learning from Pairwise Measurements
Yi Chen, Zhuoran Yang, Yuchen Xie, Zhaoran Wang
We provide numerical experiments to corroborate our theory.
Coupled Variational Bayes via Optimization Embedding
Theoretically, we establish an interesting connection to gradient flow and demonstrate the extreme flexibility of this implicit distribution family in the limit sense.
Gaussian Process Conditional Density Estimation
Vincent Dutordoir, Hugh Salimbeni, James Hensman, Marc Deisenroth
Neural Information Processing Systems http://nips.cc/
A Bayes-Sard Cubature Method
Toni Karvonen, Chris J. Oates, Simo Sarkka
Neural Information Processing Systems http://nips.cc/
Online Structure Learning for Feed-Forward and Recurrent Sum-Product Networks
Agastya Kalra, Abdullah Rashwan, Wei-Shou Hsu, Pascal Poupart, Prashant Doshi, Georgios Trimponias
Neural Information Processing Systems http://nips.cc/
