Papers
Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature
Tom Gunter, Michael A Osborne, Roman Garnett et al.
Altitude Training: Strong Bounds for Single-Layer Dropout
Stefan Wager, William Fithian, Sida Wang et al.
Bayesian Nonlinear Support Vector Machines and Discriminative Factor Modeling
Ricardo Henao, Xin Yuan, Lawrence Carin
Incremental Local Gaussian Regression
Franziska Meier, Philipp Hennig, Stefan Schaal
Clustering from Labels and Time-Varying Graphs
Shiau Hong Lim, Yudong Chen, Huan Xu
Near-Optimal-Sample Estimators for Spherical Gaussian Mixtures
Ananda Theertha Suresh, Alon Orlitsky, Jayadev Acharya et al.
Median Selection Subset Aggregation for Parallel Inference
Xiangyu Wang, Peichao Peng, David B Dunson
Object Localization based on Structural SVM using Privileged Information
Jan Feyereisl, Suha Kwak, Jeany Son et al.
Weakly-supervised Discovery of Visual Pattern Configurations
Hyun Oh Song, Yong Jae Lee, Stefanie Jegelka et al.
On the Computational Efficiency of Training Neural Networks
Roi Livni, Shai Shalev-Shwartz, Ohad Shamir
Graph Clustering With Missing Data: Convex Algorithms and Analysis
Ramya Korlakai Vinayak, Samet Oymak, Babak Hassibi
A Differential Equation for Modeling Nesterov’s Accelerated Gradient Method: Theory and Insights
Weijie Su, Stephen Boyd, Emmanuel Candes
Inferring sparse representations of continuous signals with continuous orthogonal matching pursuit
Karin C Knudson, Jacob Yates, Alexander Huk et al.
Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators
Kai Zhong, Ian En-Hsu Yen, Inderjit S Dhillon et al.
Consistent Binary Classification with Generalized Performance Metrics
Oluwasanmi O Koyejo, Nagarajan Natarajan, Pradeep K Ravikumar et al.
Recursive Context Propagation Network for Semantic Scene Labeling
Abhishek Sharma, Oncel Tuzel, Ming-Yu Liu
Optimal decision-making with time-varying evidence reliability
Jan Drugowitsch, Ruben Moreno-Bote, Alexandre Pouget
Online and Stochastic Gradient Methods for Non-decomposable Loss Functions
Purushottam Kar, Harikrishna Narasimhan, Prateek Jain
Multi-scale Graphical Models for Spatio-Temporal Processes
firdaus janoos, Huseyin Denli, Niranjan Subrahmanya
Learning Distributed Representations for Structured Output Prediction
Vivek Srikumar, Christopher D. Manning
A Boosting Framework on Grounds of Online Learning
Tofigh Naghibi Mohamadpoor, Beat Pfister
A* Sampling
Chris J Maddison, Daniel Tarlow, Tom Minka
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives
Aaron Defazio, Francis Bach, Simon Lacoste-Julien
Communication Efficient Distributed Machine Learning with the Parameter Server
Mu Li, David G Andersen, Alexander J Smola et al.
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
David Eigen, Christian Puhrsch, Rob Fergus