134 citations · 920 across the 72 of their papers we have counts for
10 papers · 1 filter
SLAP: Self-supervised Anomaly Detection with Adversarial Perturbation
Yizhou Wang, Can Qin, Rongzhe Wei +3
Anomaly detection is a fundamental yet challenging problem in machine learning due to the lack of label information. In this work, we propose a novel and powerful framework, dubbed…
Rethinking Adam: A Twofold Exponential Moving Average Approach
Yizhou Wang, Yue Kang, Can Qin +4
Adaptive gradient methods, e.g. \textsc{Adam}, have achieved tremendous success in machine learning. Scaling the learning rate element-wisely by a certain form of second moment est…
On Computation and Generalization of Generative Adversarial Imitation Learning
Minshuo Chen, Yizhou Wang, Tianyi Liu +4
Generative Adversarial Imitation Learning (GAIL) is a powerful and practical approach for learning sequential decision-making policies. Different from Reinforcement Learning (RL),…
L_DMI: An Information-theoretic Noise-robust Loss Function
Yilun Xu, Peng Cao, Yuqing Kong +1
Accurately annotating large scale dataset is notoriously expensive both in time and in money. Although acquiring low-quality-annotated dataset can be much cheaper, it often badly d…
Max-MIG: an Information Theoretic Approach for Joint Learning from Crowds
Peng Cao, Yilun Xu, Yuqing Kong +1
Eliciting labels from crowds is a potential way to obtain large labeled data. Despite a variety of methods developed for learning from crowds, a key challenge remains unsolved: \em…
Efficient Model-Free Reinforcement Learning Using Gaussian Process
Ying Fan, Letian Chen, Yizhou Wang
Efficient Reinforcement Learning usually takes advantage of demonstration or good exploration strategy. By applying posterior sampling in model-free RL under the hypothesis of GP,…
Maximal Sparsity with Deep Networks?
Bo Xin, Yizhou Wang, Wen Gao +1
The iterations of many sparse estimation algorithms are comprised of a fixed linear filter cascaded with a thresholding nonlinearity, which collectively resemble a typical neural n…
Stable Feature Selection from Brain sMRI
Bo Xin, Lingjing Hu, Yizhou Wang +1
Neuroimage analysis usually involves learning thousands or even millions of variables using only a limited number of samples. In this regard, sparse models, e.g. the lasso, are app…
Predictive Encoding of Contextual Relationships for Perceptual Inference, Interpolation and Prediction
Mingmin Zhao, Chengxu Zhuang, Yizhou Wang +1
We propose a new neurally-inspired model that can learn to encode the global relationship context of visual events across time and space and to use the contextual information to mo…
Exploring Algorithmic Limits of Matrix Rank Minimization under Affine Constraints
Bo Xin, David Wipf
Many applications require recovering a matrix of minimal rank within an affine constraint set, with matrix completion a notable special case. Because the problem is NP-hard in gene…