Papers
RAAM: The Benefits of Robustness in Approximating Aggregated MDPs in Reinforcement Learning
Marek Petrik, Dharmashankar Subramanian
Randomized Experimental Design for Causal Graph Discovery
Huining Hu, Zhentao Li, Adrian R Vetta
Ranking via Robust Binary Classification
Hyokun Yun, Parameswaran Raman, S. Vishwanathan
Rates of Convergence for Nearest Neighbor Classification
Kamalika Chaudhuri, Sanjoy Dasgupta
Real-Time Decoding of an Integrate and Fire Encoder
Shreya Saxena, Munther Dahleh
Recovery of Coherent Data via Low-Rank Dictionary Pursuit
Guangcan Liu, Ping Li
Recurrent Models of Visual Attention
Volodymyr Mnih, Nicolas Heess, Alex Graves et al.
Recursive Context Propagation Network for Semantic Scene Labeling
Abhishek Sharma, Oncel Tuzel, Ming-Yu Liu
Recursive Inversion Models for Permutations
Christopher Meek, Marina Meila
Reducing the Rank in Relational Factorization Models by Including Observable Patterns
Maximilian Nickel, Xueyan Jiang, Volker Tresp
Repeated Contextual Auctions with Strategic Buyers
Kareem Amin, Afshin Rostamizadeh, Umar Syed
Reputation-based Worker Filtering in Crowdsourcing
Srikanth Jagabathula, Lakshminarayanan Subramanian, Ashwin Venkataraman
Restricted Boltzmann machines modeling human choice
Takayuki Osogami, Makoto Otsuka
Robust Bayesian Max-Margin Clustering
Changyou Chen, Jun Zhu, Xinhua Zhang
Robust Classification Under Sample Selection Bias
Anqi Liu, Brian Ziebart
Robust Kernel Density Estimation by Scaling and Projection in Hilbert Space
Robert A Vandermeulen, Clayton Scott
Robust Logistic Regression and Classification
Jiashi Feng, Huan Xu, Shie Mannor et al.
Robust Tensor Decomposition with Gross Corruption
Quanquan Gu, Huan Gui, Jiawei Han
Rounding-based Moves for Metric Labeling
M. Pawan Kumar
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives
Aaron Defazio, Francis Bach, Simon Lacoste-Julien
Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature
Tom Gunter, Michael A Osborne, Roman Garnett et al.
Scalable Inference for Neuronal Connectivity from Calcium Imaging
Alyson K. Fletcher, Sundeep Rangan
Scalable Kernel Methods via Doubly Stochastic Gradients
Bo Dai, Bo Xie, Niao He et al.
Scalable Methods for Nonnegative Matrix Factorizations of Near-separable Tall-and-skinny Matrices
Austin R Benson, Jason Lee, Bartek Rajwa et al.
Scalable Non-linear Learning with Adaptive Polynomial Expansions
Alekh Agarwal, Alina Beygelzimer, Daniel J. Hsu et al.