Skip to content
conftrace_

AISTATS 2018

216 papers

On the challenges of learning with inference networks on sparse, high-dimensional data

Rahul Krishnan, Dawen Liang, Matthew Hoffman

Parallelised Bayesian Optimisation via Thompson Sampling

Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos

Can clustering scale sublinearly with its clusters? A variational EM acceleration of GMMs and k-means

Dennis Forster, Jörg Lücke

Iterative Spectral Method for Alternative Clustering

Chieh Wu, Stratis Ioannidis, Mario Sznaier, Xiangyu Li, David Kaeli

Iterative Supervised Principal Components

Juho Piironen, Aki Vehtari

Fast Threshold Tests for Detecting Discrimination

Emma Pierson, Sam Corbett-Davies, Sharad Goel

Mixed Membership Word Embeddings for Computational Social Science

James Foulds

Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach

Satoshi Hara, Kohei Hayashi

Multi-scale Nystrom Method

Woosang Lim, Rundong Du, Bo Dai, Kyomin Jung, Le Song

Accelerated Stochastic Power Iteration

Peng Xu, Bryan He, Christopher De Sa, Ioannis Mitliagkas, Chris Re

Growth-Optimal Portfolio Selection under CVaR Constraints

Guy Uziel, Ran El-Yaniv

Combinatorial Preconditioners for Proximal Algorithms on Graphs

Thomas Möllenhoff, Zhenzhang Ye, Tao Wu, Daniel Cremers

An Analysis of Categorical Distributional Reinforcement Learning

Mark Rowland, Marc Bellemare, Will Dabney, Rémi Munos, Yee Whye Teh

A Fast Algorithm for Separated Sparsity via Perturbed Lagrangians

Aleksander Madry, Slobodan Mitrovic, Ludwig Schmidt

Gauged Mini-Bucket Elimination for Approximate Inference

Sungsoo Ahn, Michael Chertkov, Jinwoo Shin, Adrian Weller

The Geometry of Random Features

Krzysztof Choromanski, Mark Rowland, Tamas Sarlos, Vikas Sindhwani, Richard Turner