387 citations · 2k across the 64 of their papers we have counts for
21 papers · 1 filter
Bipartite Ranking Fairness through a Model Agnostic Ordering Adjustment
Sen Cui, Weishen Pan, Changshui Zhang +1
Algorithmic fairness has been a serious concern and received lots of interest in machine learning community. In this paper, we focus on the bipartite ranking scenario, where the in…
A Theoretical View of Linear Backpropagation and Its Convergence
Ziang Li, Yiwen Guo, Haodi Liu +1
Backpropagation (BP) is widely used for calculating gradients in deep neural networks (DNNs). Applied often along with stochastic gradient descent (SGD) or its variants, BP is cons…
Learned ISTA with Error-based Thresholding for Adaptive Sparse Coding
Ziang Li, Kailun Wu, Yiwen Guo +1
Drawing on theoretical insights, we advocate an error-based thresholding (EBT) mechanism for learned ISTA (LISTA), which utilizes a function of the layer-wise reconstruction error…
CATRO: Channel Pruning via Class-Aware Trace Ratio Optimization
Wenzheng Hu, Zhengping Che, Ning Liu +4
Deep convolutional neural networks are shown to be overkill with high parametric and computational redundancy in many application scenarios, and an increasing number of works have…
Explaining Algorithmic Fairness Through Fairness-Aware Causal Path Decomposition
Weishen Pan, Sen Cui, Jiang Bian +2
Algorithmic fairness has aroused considerable interests in data mining and machine learning communities recently. So far the existing research has been mostly focusing on the devel…
Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning
Sen Cui, Weishen Pan, Jian Liang +2
Federated learning (FL) has gain growing interests for its capability of learning from distributed data sources collectively without the need of accessing the raw data samples acro…
Collaboration Equilibrium in Federated Learning
Sen Cui, Jian Liang, Weishen Pan +3
Federated learning (FL) refers to the paradigm of learning models over a collaborative research network involving multiple clients without sacrificing privacy. Recently, there have…
The Definitions of Interpretability and Learning of Interpretable Models
Weishen Pan, Changshui Zhang
As machine learning algorithms getting adopted in an ever-increasing number of applications, interpretation has emerged as a crucial desideratum. In this paper, we propose a mathem…
Recent Advances in Large Margin Learning
Yiwen Guo, Changshui Zhang
This paper serves as a survey of recent advances in large margin training and its theoretical foundations, mostly for (nonlinear) deep neural networks (DNNs) that are probably the…
Learning Fast Approximations of Sparse Nonlinear Regression
Yuhai Song, Zhong Cao, Kailun Wu +2
The idea of unfolding iterative algorithms as deep neural networks has been widely applied in solving sparse coding problems, providing both solid theoretical analysis in convergen…
On Connections between Regularizations for Improving DNN Robustness
Yiwen Guo, Long Chen, Yurong Chen +1
This paper analyzes regularization terms proposed recently for improving the adversarial robustness of deep neural networks (DNNs), from a theoretical point of view. Specifically,…
Road Network Metric Learning for Estimated Time of Arrival
Yiwen Sun, Kun Fu, Zheng Wang +2
Recently, deep learning have achieved promising results in Estimated Time of Arrival (ETA), which is considered as predicting the travel time from the origin to the destination alo…
FMA-ETA: Estimating Travel Time Entirely Based on FFN With Attention
Yiwen Sun, Yulu Wang, Kun Fu +4
Estimated time of arrival (ETA) is one of the most important services in intelligent transportation systems and becomes a challenging spatial-temporal (ST) data mining task in rece…
Fusion Recurrent Neural Network
Yiwen Sun, Yulu Wang, Kun Fu +3
Considering deep sequence learning for practical application, two representative RNNs - LSTM and GRU may come to mind first. Nevertheless, is there no chance for other RNNs? Will t…
Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utility
Sen Cui, Weishen Pan, Changshui Zhang +1
Bipartite ranking, which aims to learn a scoring function that ranks positive individuals higher than negative ones from labeled data, is widely adopted in various applications whe…
Constructing Geographic and Long-term Temporal Graph for Traffic Forecasting
Yiwen Sun, Yulu Wang, Kun Fu +3
Traffic forecasting influences various intelligent transportation system (ITS) services and is of great significance for user experience as well as urban traffic control. It is cha…
RL-Duet: Online Music Accompaniment Generation Using Deep Reinforcement Learning
Nan Jiang, Sheng Jin, Zhiyao Duan +1
This paper presents a deep reinforcement learning algorithm for online accompaniment generation, with potential for real-time interactive human-machine duet improvisation. Differen…
Adversarial Margin Maximization Networks
Ziang Yan, Yiwen Guo, Changshui Zhang
The tremendous recent success of deep neural networks (DNNs) has sparked a surge of interest in understanding their predictive ability. Unlike the human visual system which is able…
Sparse DNNs with Improved Adversarial Robustness
Yiwen Guo, Chao Zhang, Changshui Zhang +1
Deep neural networks (DNNs) are computationally/memory-intensive and vulnerable to adversarial attacks, making them prohibitive in some real-world applications. By converting dense…
Relaxed Sparse Eigenvalue Conditions for Sparse Estimation via Non-convex Regularized Regression
Zheng Pan, Changshui Zhang
Non-convex regularizers usually improve the performance of sparse estimation in practice. To prove this fact, we study the conditions of sparse estimations for the sharp concave re…
A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems
Pinghua Gong, Changshui Zhang, Zhaosong Lu +2
Non-convex sparsity-inducing penalties have recently received considerable attentions in sparse learning. Recent theoretical investigations have demonstrated their superiority over…