Papers
Optimal and Adaptive Algorithms for Online Boosting
Alina Beygelzimer, Satyen Kale, Haipeng Luo
A Deeper Look at Planning as Learning from Replay
Harm Vanseijen, Rich Sutton
Scaling up Natural Gradient by Sparsely Factorizing the Inverse Fisher Matrix
Roger Grosse, Ruslan Salakhudinov
Intersecting Faces: Non-negative Matrix Factorization With New Guarantees
Rong Ge, James Zou
Compressing Neural Networks with the Hashing Trick
Wenlin Chen, James Wilson, Stephen Tyree et al.
Distributed Inference for Dirichlet Process Mixture Models
Hong Ge, Yutian Chen, Moquan Wan et al.
Modeling Order in Neural Word Embeddings at Scale
Andrew Trask, David Gilmore, Matthew Russell
Deep Unsupervised Learning using Nonequilibrium Thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan et al.
Learning Scale-Free Networks by Dynamic Node Specific Degree Prior
Qingming Tang, Siqi Sun, Jinbo Xu
Correlation Clustering in Data Streams
KookJin Ahn, Graham Cormode, Sudipto Guha et al.
Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes
Yves-Laurent Kom Samo, Stephen Roberts
Removing systematic errors for exoplanet search via latent causes
Bernhard Schölkopf, David Hogg, Dun Wang et al.
A Fast Variational Approach for Learning Markov Random Field Language Models
Yacine Jernite, Alexander Rush, David Sontag
Proteins, Particles, and Pseudo-Max-Marginals: A Submodular Approach
Jason Pacheco, Erik Sudderth
Online Time Series Prediction with Missing Data
Oren Anava, Elad Hazan, Assaf Zeevi
How Hard is Inference for Structured Prediction?
Amir Globerson, Tim Roughgarden, David Sontag et al.
Scalable Bayesian Optimization Using Deep Neural Networks
Jasper Snoek, Oren Rippel, Kevin Swersky et al.
Binary Embedding: Fundamental Limits and Fast Algorithm
Xinyang Yi, Constantine Caramanis, Eric Price
An embarrassingly simple approach to zero-shot learning
Bernardino Romera-Paredes, Philip Torr
Subsampling Methods for Persistent Homology
Frederic Chazal, Brittany Fasy, Fabrizio Lecci et al.
Cheap Bandits
Manjesh Hanawal, Venkatesh Saligrama, Michal Valko et al.
Bimodal Modelling of Source Code and Natural Language
Miltos Allamanis, Daniel Tarlow, Andrew Gordon et al.
Gradient-based Hyperparameter Optimization through Reversible Learning
Dougal Maclaurin, David Duvenaud, Ryan Adams
Distributional Rank Aggregation, and an Axiomatic Analysis
Adarsh Prasad, Harsh Pareek, Pradeep Ravikumar
Celeste: Variational inference for a generative model of astronomical images
Jeffrey Regier, Andrew Miller, Jon McAuliffe et al.