58 citations · 89 across the 14 of their papers we have counts for
14 papers
Attribute-Efficient PAC Learning of Low-Degree Polynomial Threshold Functions with Nasty Noise
Shiwei Zeng, Jie Shen
The concept class of low-degree polynomial threshold functions (PTFs) plays a fundamental role in machine learning. In this paper, we study PAC learning of -sparse degree- PT…
List-Decodable Sparse Mean Estimation
Shiwei Zeng, Jie Shen
Robust mean estimation is one of the most important problems in statistics: given a set of samples in where an fraction are drawn from some distribution and…
Semi-verified PAC Learning from the Crowd
Shiwei Zeng, Jie Shen
We study the problem of crowdsourced PAC learning of threshold functions. This is a challenging problem and only recently have query-efficient algorithms been established under the…
Sample-Optimal PAC Learning of Halfspaces with Malicious Noise
Jie Shen
We study efficient PAC learning of homogeneous halfspaces in in the presence of malicious noise of Valiant (1985). This is a challenging noise model and only until r…
On the Power of Localized Perceptron for Label-Optimal Learning of Halfspaces with Adversarial Noise
Jie Shen
We study {\em online} active learning of homogeneous halfspaces in with adversarial noise where the overall probability of a noisy label is constrained to be at most…
Efficient PAC Learning from the Crowd with Pairwise Comparisons
Shiwei Zeng, Jie Shen
We study crowdsourced PAC learning of threshold functions, where the labels are gathered from a pool of annotators some of whom may behave adversarially. This is yet a challenging…
One-Bit Compressed Sensing via One-Shot Hard Thresholding
Jie Shen
This paper concerns the problem of 1-bit compressed sensing, where the goal is to estimate a sparse signal from a few of its binary measurements. We study a non-convex sparsity-con…
Attribute-Efficient Learning of Halfspaces with Malicious Noise: Near-Optimal Label Complexity and Noise Tolerance
Jie Shen, Chicheng Zhang
This paper is concerned with computationally efficient learning of homogeneous sparse halfspaces in under noise. Though recent works have established attribute-effic…
Efficient active learning of sparse halfspaces with arbitrary bounded noise
Chicheng Zhang, Jie Shen, Pranjal Awasthi
We study active learning of homogeneous -sparse halfspaces in under the setting where the unlabeled data distribution is isotropic log-concave and each label is f…
A Tight Bound of Hard Thresholding
Jie Shen, Ping Li
This paper is concerned with the hard thresholding operator which sets all but the largest absolute elements of a vector to zero. We establish a {\em tight} bound to quantitati…
Social Trust Prediction via Max-norm Constrained 1-bit Matrix Completion
Jing Wang, Jie Shen, Huan Xu
Social trust prediction addresses the significant problem of exploring interactions among users in social networks. Naturally, this problem can be formulated in the matrix completi…
Efficient Online Minimization for Low-Rank Subspace Clustering
Jie Shen, Ping Li, Huan Xu
Low-rank representation~(LRR) has been a significant method for segmenting data that are generated from a union of subspaces. It is, however, known that solving the LRR program is…
Object Proposal with Kernelized Partial Ranking
Jing Wang, Jie Shen, Ping Li
Object proposals are an ensemble of bounding boxes with high potential to contain objects. In order to determine a small set of proposals with a high recall, a common scheme is ext…
Online Optimization for Large-Scale Max-Norm Regularization
Jie Shen, Huan Xu, Ping Li
Max-norm regularizer has been extensively studied in the last decade as it promotes an effective low-rank estimation for the underlying data. However, such max-norm regularized pro…