Statistical Learning
Learning Disentangled Representation for Robust Person Re-identification
Recent reID methods focus on learning discriminative features but robust to only a particular factor of variations ( e.g., human
Fast Sparse Group Lasso
Yasutoshi Ida, Yasuhiro Fujiwara, Hisashi Kashima
Sparse Group Lasso is a method of linear regression analysis that finds sparse parameters in terms of both feature groups and individual features.
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local Computations
Debraj Basu, Deepesh Data, Can Karakus, Suhas Diggavi
Neural Information Processing Systems http://nips.cc/
Comparing Unsupervised Word Translation Methods Step by Step
Mareike Hartmann, Yova Kementchedjhieva, Anders Sรธgaard
Neural Information Processing Systems http://nips.cc/
Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration
Kwang-Sung Jun, Ashok Cutkosky, Francesco Orabona
In this paper, we consider the nonparametric least square regression in a Reproducing Kernel Hilbert Space (RKHS).
Efficient Graph Generation with Graph Recurrent Attention Networks
Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Will Hamilton, David K. Duvenaud, Raquel Urtasun, Richard Zemel
We propose a new family of efficient and expressive deep generative models of graphs, called Graph Recurrent Attention Networks (GRANs).
Locally Private Learning without Interaction Requires Separation
Neural Information Processing Systems http://nips.cc/
Bayesian Optimization with Unknown Search Space
Huong Ha, Santu Rana, Sunil Gupta, Thanh Nguyen, Hung Tran-The, Svetha Venkatesh
Neural Information Processing Systems http://nips.cc/
Game Design for Eliciting Distinguishable Behavior
Fan Yang, Liu Leqi, Yifan Wu, Zachary Lipton, Pradeep K. Ravikumar, Tom M. Mitchell, William W. Cohen
However, these traditional games are limited because they are typically designed based on heuristics.