Papers
8,340 papers found
Scalable Variational Inference in Log-supermodular Models
Josip Djolonga, Andreas Krause
Scaling up Natural Gradient by Sparsely Factorizing the Inverse Fisher Matrix
Roger Grosse, Ruslan Salakhudinov
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Kelvin Xu, Jimmy Ba, Ryan Kiros et al.
Simple regret for infinitely many armed bandits
Alexandra Carpentier, Michal Valko
Sparse Subspace Clustering with Missing Entries
Congyuan Yang, Daniel Robinson, Rene Vidal
Sparse Variational Inference for Generalized GP Models
Rishit Sheth, Yuyang Wang, Roni Khardon
Spectral Clustering via the Power Method - Provably
Christos Boutsidis, Prabhanjan Kambadur, Alex Gittens
Spectral MLE: Top-K Rank Aggregation from Pairwise Comparisons
Yuxin Chen, Changho Suh
Statistical and Algorithmic Perspectives on Randomized Sketching for Ordinary Least-Squares
Garvesh Raskutti, Michael Mahoney
Stay on path: PCA along graph paths
Megasthenis Asteris, Anastasios Kyrillidis, Alex Dimakis et al.
Stochastic Dual Coordinate Ascent with Adaptive Probabilities
Dominik Csiba, Zheng Qu, Peter Richtarik
Stochastic Optimization with Importance Sampling for Regularized Loss Minimization
Peilin Zhao, Tong Zhang
Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization
Yuchen Zhang, Xiao Lin
Streaming Sparse Principal Component Analysis
Wenzhuo Yang, Huan Xu
Strongly Adaptive Online Learning
Amit Daniely, Alon Gonen, Shai Shalev-Shwartz
Structural Maxent Models
Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri et al.
Submodularity in Data Subset Selection and Active Learning
Kai Wei, Rishabh Iyer, Jeff Bilmes
Subsampling Methods for Persistent Homology
Frederic Chazal, Brittany Fasy, Fabrizio Lecci et al.
Support Matrix Machines
Luo Luo, Yubo Xie, Zhihua Zhang et al.
Surrogate Functions for Maximizing Precision at the Top
Purushottam Kar, Harikrishna Narasimhan, Prateek Jain
Swept Approximate Message Passing for Sparse Estimation
Andre Manoel, Florent Krzakala, Eric Tramel et al.
Telling cause from effect in deterministic linear dynamical systems
Naji Shajarisales, Dominik Janzing, Bernhard Schoelkopf et al.
The Benefits of Learning with Strongly Convex Approximate Inference
Ben London, Bert Huang, Lise Getoor
The Composition Theorem for Differential Privacy
Peter Kairouz, Sewoong Oh, Pramod Viswanath