Statistical Learning
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration
Jacob Gardner, Geoff Pleiss, Kilian Q. Weinberger, David Bindel, Andrew G. Wilson
Neural Information Processing Systems http://nips.cc/
Importance Weighting and Variational Inference
Recent work used importance sampling ideas for better variational bounds on likelihoods.
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.
Implicit Probabilistic Integrators for ODEs
Onur Teymur, Han Cheng Lie, Tim Sullivan, Ben Calderhead
We then introduce our method, which builds on and adapts the work of Conrad et al. (2016) and Teymur et al. (2016), and provide a
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.
First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time
To the best of our knowledge, this is the first theoretical result of first-order stochastic algorithms with an almost linear time in terms of problem's
Out-of-Distribution Detection using Multiple Semantic Label Representations
Gabi Shalev, Yossi Adi, Joseph Keshet
Neural Information Processing Systems http://nips.cc/
Diffusion Maps for Textual Network Embedding
Xinyuan Zhang, Yitong Li, Dinghan Shen, Lawrence Carin
Textual network embedding leverages rich text information associated with the network to learn low-dimensional vectorial representations of vertices.