Papers
Hierarchical Compound Poisson Factorization
Mehmet Basbug, Barbara Engelhardt
Hierarchical Decision Making In Electricity Grid Management
Gal Dalal, Elad Gilboa, Shie Mannor
Hierarchical Span-Based Conditional Random Fields for Labeling and Segmenting Events in Wearable Sensor Data Streams
Roy Adams, Nazir Saleheen, Edison Thomaz et al.
Hierarchical Variational Models
Rajesh Ranganath, Dustin Tran, David Blei
Horizontally Scalable Submodular Maximization
Mario Lucic, Olivier Bachem, Morteza Zadimoghaddam et al.
How to Fake Multiply by a Gaussian Matrix
Michael Kapralov, Vamsi Potluru, David Woodruff
Hyperparameter optimization with approximate gradient
Fabian Pedregosa
Importance Sampling Tree for Large-scale Empirical Expectation
Olivier Canevet, Cijo Jose, Francois Fleuret
Improved SVRG for Non-Strongly-Convex or Sum-of-Non-Convex Objectives
Zeyuan Allen-Zhu, Yang Yuan
Inference Networks for Sequential Monte Carlo in Graphical Models
Brooks Paige, Frank Wood
Interacting Particle Markov Chain Monte Carlo
Tom Rainforth, Christian Naesseth, Fredrik Lindsten et al.
Interactive Bayesian Hierarchical Clustering
Sharad Vikram, Sanjoy Dasgupta
Isotonic Hawkes Processes
Yichen Wang, Bo Xie, Nan Du et al.
K-Means Clustering with Distributed Dimensions
Hu Ding, Yu Liu, Lingxiao Huang et al.
k-variates++: more pluses in the k-means++
Richard Nock, Raphael Canyasse, Roksana Boreli et al.
L1-regularized Neural Networks are Improperly Learnable in Polynomial Time
Yuchen Zhang, Jason D. Lee, Michael I. Jordan
Large-Margin Softmax Loss for Convolutional Neural Networks
Weiyang Liu, Yandong Wen, Zhiding Yu et al.
Learning and Inference via Maximum Inner Product Search
Stephen Mussmann, Stefano Ermon
Learning Convolutional Neural Networks for Graphs
Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov
Learning End-to-end Video Classification with Rank-Pooling
Basura Fernando, Stephen Gould
Learning from Multiway Data: Simple and Efficient Tensor Regression
Rose Yu, Yan Liu
Learning Granger Causality for Hawkes Processes
Hongteng Xu, Mehrdad Farajtabar, Hongyuan Zha
Learning Mixtures of Plackett-Luce Models
Zhibing Zhao, Peter Piech, Lirong Xia
Learning Physical Intuition of Block Towers by Example
Adam Lerer, Sam Gross, Rob Fergus
Learning Population-Level Diffusions with Generative RNNs
Tatsunori Hashimoto, David Gifford, Tommi Jaakkola