Statistical Learning
Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds
Raghav Somani, Chirag Gupta, Prateek Jain, Praneeth Netrapalli
We study the problem of sparse regression where the goal is to learn a sparse vector that best optimizes a given objective function.
Horizon-Independent Minimax Linear Regression
We consider online linear regression: at each round, an adversary reveals a covariate vector, the learner predicts a real value, the adversary reveals a label, and the learner suffers the squared prediction error. The aim is to minimize the difference between the cumulative loss and that of the linear predictor that is best in hindsight.
Empirical Risk Minimization Under Fairness Constraints
Michele Donini, Luca Oneto, Shai Ben-David, John S. Shawe-Taylor, Massimiliano Pontil
We address the problem of algorithmic fairness: ensuring that sensitive information does not unfairly influence the outcome of a classifier. We present an approach based on empirical risk minimization, which incorporates a fairness constraint into the learning problem. It encourages the conditional risk of the learned classifier to be approximately constant with respect to the sensitive variable. We derive both risk and fairness bounds that support the statistical consistency of our methodology. We specify our approach to kernel methods and observe that the fairness requirement implies an orthogonality constraint which can be easily added to these methods. We further observe that for linear models the constraint translates into a simple data preprocessing step. Experiments indicate that the method is empirically effective and performs favorably against state-of-the-art approaches.
Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimation
Siyuan Huang, Siyuan Qi, Yinxue Xiao, Yixin Zhu, Ying Nian Wu, Song-Chun Zhu
Neural Information Processing Systems http://nips.cc/
Bilevel Distance Metric Learning for Robust Image Recognition
Jie Xu, Lei Luo, Cheng Deng, Heng Huang
Neural Information Processing Systems http://nips.cc/
Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks
Hang Gao, Zheng Shou, Alireza Zareian, Hanwang Zhang, Shih-Fu Chang
In this work, we propose Covariance-Preserving Adversarial Augmentation Networks to overcome existing limits of low-shot learning. Specifically, a novel Generative Adversarial Network is designed to model the latent distribution of each novel class given its related base counterparts.
Learning to Teach with Dynamic Loss Functions
Lijun Wu, Fei Tian, Yingce Xia, Yang Fan, Tao Qin, Lai Jian-Huang, Tie-Yan Liu
Neural Information Processing Systems http://nips.cc/
Gradient Descent Meets Shift-and-Invert Preconditioning for Eigenvector Computation
Neural Information Processing Systems http://nips.cc/
RetGK: Graph Kernels based on Return Probabilities of Random Walks
Zhen Zhang, Mianzhi Wang, Yijian Xiang, Yan Huang, Arye Nehorai
Graph-structured data arise in wide applications, such as computer vision, bioinfor-matics, and social networks. Quantifying similarities among graphs is a fundamental problem.
Exact natural gradient in deep linear networks and its application to the nonlinear case
Alberto Bernacchia, Mate Lengyel, Guillaume Hennequin
Neural Information Processing Systems http://nips.cc/