Mathematical & Statistical Methods
A Stein variational Newton method
Gianluca Detommaso, Tiangang Cui, Youssef Marzouk, Alessio Spantini, Robert Scheichl
Neural Information Processing Systems http://nips.cc/
Proximal SCOPE for Distributed Sparse Learning
Shenyi Zhao, Gong-Duo Zhang, Ming-Wei Li, Wu-Jun Li
Neural Information Processing Systems http://nips.cc/
GIANT: Globally Improved Approximate Newton Method for Distributed Optimization
Shusen Wang, Fred Roosta, Peng Xu, Michael W. Mahoney
Neural Information Processing Systems http://nips.cc/
Cost Effective Active Search
Shali Jiang, Roman Garnett, Benjamin Moseley
Neural Information Processing Systems http://nips.cc/
Epidemic Learning: Boosting Decentralized Learning with Randomized Communication Martijn de V os
We discuss these works in more detail in Section 5. Authors are listed in alphabetical order.
Testing for Families of Distributions via the Fourier Transform
Alistair Stewart, Ilias Diakonikolas, Clement Canonne
Neural Information Processing Systems http://nips.cc/
Sampling from Structured Log-Concave Distributions via a Soft-Threshold Dikin Walk Oren Mangoubi Worcester Polytechnic Institute Nisheeth K. Vishnoi Yale University
Interest in this problem derives from its applications to Bayesian inference and differential privacy.