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Bayesian Alignments of Warped Multi-Output Gaussian Processes
Markus Kaiser, Clemens Otte, Thomas Runkler, Carl Henrik Ek
The proposed model allows for both arbitrary alignments of the inputs and non-parametric output warpings to transform the observations. This gives rise to multiple deep Gaussian process models connected via latent generating processes.
Generalized Inverse Optimization through Online Learning
Chaosheng Dong, Yiran Chen, Bo Zeng
Neural Information Processing Systems http://nips.cc/
Forward Modeling for Partial Observation Strategy Games - A StarCraft Defogger
Gabriel Synnaeve, Zeming Lin, Jonas Gehring, Dan Gant, Vegard Mella, Vasil Khalidov, Nicolas Carion, Nicolas Usunier
We formulate the problem of defogging as state estimation and future state prediction from previous, partial observations in the context of real-time strategy games.
Uncertainty-Aware Attention for Reliable Interpretation and Prediction
Jay Heo, Hae Beom Lee, Saehoon Kim, Juho Lee, Kwang Joon Kim, Eunho Yang, Sung Ju Hwang
Further evaluation of both the accuracy of the uncertainty calibration and the prediction performance with "I don't know"
Meta-Gradient Reinforcement Learning
Zhongwen Xu, Hado P. van Hasselt, David Silver
Neural Information Processing Systems http://nips.cc/
Variational Learning on Aggregate Outputs with Gaussian Processes
Ho Chung Law, Dino Sejdinovic, Ewan Cameron, Tim Lucas, Seth Flaxman, Katherine Battle, Kenji Fukumizu
Neural Information Processing Systems http://nips.cc/
Distilled Wasserstein Learning for Word Embedding and Topic Modeling
Hongteng Xu, Wenlin Wang, Wei Liu, Lawrence Carin
Accordingly, the word embeddings are learned to inherit those relationships.
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks
Ali Shafahi, W. Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, Tom Goldstein
Neural Information Processing Systems http://nips.cc/
cpSGD: Communication-efficient and differentially-private distributed SGD
Naman Agarwal, Ananda Theertha Suresh, Felix Xinnan X. Yu, Sanjiv Kumar, Brendan McMahan
Distributed stochastic gradient descent is an important subroutine in distributed learning.