Papers
Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo
Yu-Xiang Wang, Stephen Fienberg, Alex Smola
Boosted Categorical Restricted Boltzmann Machine for Computational Prediction of Splice Junctions
Taehoon Lee, Sungroh Yoon
Moderated and Drifting Linear Dynamical Systems
Jinyan Guan, Kyle Simek, Ernesto Brau et al.
Sparse Subspace Clustering with Missing Entries
Congyuan Yang, Daniel Robinson, Rene Vidal
An Asynchronous Distributed Proximal Gradient Method for Composite Convex Optimization
Necdet Aybat, Zi Wang, Garud Iyengar
PU Learning for Matrix Completion
Cho-Jui Hsieh, Nagarajan Natarajan, Inderjit Dhillon
Entropy-Based Concentration Inequalities for Dependent Variables
Liva Ralaivola, Massih-Reza Amini
Multi-instance multi-label learning in the presence of novel class instances
Anh Pham, Raviv Raich, Xiaoli Fern et al.
A Convex Exemplar-based Approach to MAD-Bayes Dirichlet Process Mixture Models
En-Hsu Yen, Xin Lin, Kai Zhong et al.
Optimizing Neural Networks with Kronecker-factored Approximate Curvature
James Martens, Roger Grosse
Consistent Multiclass Algorithms for Complex Performance Measures
Harikrishna Narasimhan, Harish Ramaswamy, Aadirupa Saha et al.
Fixed-point algorithms for learning determinantal point processes
Zelda Mariet, Suvrit Sra
High Confidence Policy Improvement
Philip Thomas, Georgios Theocharous, Mohammad Ghavamzadeh
PASSCoDe: Parallel ASynchronous Stochastic dual Co-ordinate Descent
Cho-Jui Hsieh, Hsiang-Fu Yu, Inderjit Dhillon
Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret
Haitham Bou Ammar, Rasul Tutunov, Eric Eaton
Complete Dictionary Recovery Using Nonconvex Optimization
Ju Sun, Qing Qu, John Wright
An Empirical Exploration of Recurrent Network Architectures
Rafal Jozefowicz, Wojciech Zaremba, Ilya Sutskever
Global Convergence of Stochastic Gradient Descent for Some Non-convex Matrix Problems
Christopher De Sa, Christopher Re, Kunle Olukotun
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