28 citations · 155 across the 20 of their papers we have counts for
20 papers
How to Sell High-Dimensional Data Optimally
Andrew Li, R. Ravi, Karan Singh +2
Motivated by the problem of selling large, proprietary data, we consider an information pricing problem proposed by Bergemann et al. that involves a decision-making buyer and a mon…
Probabilistic Bilevel Coreset Selection
Xiao Zhou, Renjie Pi, Weizhong Zhang +2
The goal of coreset selection in supervised learning is to produce a weighted subset of data, so that training only on the subset achieves similar performance as training on the en…
Model Agnostic Sample Reweighting for Out-of-Distribution Learning
Xiao Zhou, Yong Lin, Renjie Pi +4
Distributionally robust optimization (DRO) and invariant risk minimization (IRM) are two popular methods proposed to improve out-of-distribution (OOD) generalization performance of…
Normalizing Flow with Variational Latent Representation
Hanze Dong, Shizhe Diao, Weizhong Zhang +1
Normalizing flow (NF) has gained popularity over traditional maximum likelihood based methods due to its strong capability to model complex data distributions. However, the standar…
DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics
Renjie Pi, Weizhong Zhang, Yueqi Xie +4
The Federated Learning (FL) paradigm is known to face challenges under heterogeneous client data. Local training on non-iid distributed data results in deflected local optimum, whi…
Robust Federated Learning against both Data Heterogeneity and Poisoning Attack via Aggregation Optimization
Yueqi Xie, Weizhong Zhang, Renjie Pi +4
Non-IID data distribution across clients and poisoning attacks are two main challenges in real-world federated learning (FL) systems. While both of them have attracted great resear…
Approximation algorithms for Steiner Tree Augmentation Problems
R. Ravi, Weizhong Zhang, Michael Zlatin
In the Steiner Tree Augmentation Problem (STAP), we are given a graph , a set of terminals , and a Steiner tree spanning . The edges $L := E \setmi…
Fast Adversarial Training with Adaptive Step Size
Zhichao Huang, Yanbo Fan, Chen Liu +5
While adversarial training and its variants have shown to be the most effective algorithms to defend against adversarial attacks, their extremely slow training process makes it har…
Self-Guided Noise-Free Data Generation for Efficient Zero-Shot Learning
Jiahui Gao, Renjie Pi, Yong Lin +7
There is a rising interest in further exploring the zero-shot learning potential of large pre-trained language models (PLMs). A new paradigm called data-generation-based zero-shot…
Finding Dynamics Preserving Adversarial Winning Tickets
Xupeng Shi, Pengfei Zheng, A. Adam Ding +2
Modern deep neural networks (DNNs) are vulnerable to adversarial attacks and adversarial training has been shown to be a promising method for improving the adversarial robustness o…
Safe Screening for Sparse Conditional Random Fields
Weizhong Zhang, Shuang Qiu
Sparse Conditional Random Field (CRF) is a powerful technique in computer vision and natural language processing for structured prediction. However, solving sparse CRFs in large-sc…
Efficient Neural Network Training via Forward and Backward Propagation Sparsification
Xiao Zhou, Weizhong Zhang, Zonghao Chen +2
Sparse training is a natural idea to accelerate the training speed of deep neural networks and save the memory usage, especially since large modern neural networks are significantl…
Effective Sparsification of Neural Networks with Global Sparsity Constraint
Xiao Zhou, Weizhong Zhang, Hang Xu +1
Weight pruning is an effective technique to reduce the model size and inference time for deep neural networks in real-world deployments. However, since magnitudes and relative impo…
Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning
Xing Yan, Weizhong Zhang, Lin Ma +2
We propose a parsimonious quantile regression framework to learn the dynamic tail behaviors of financial asset returns. Our model captures well both the time-varying characteristic…
Gradient Method for Continuous Influence Maximization with Budget-Saving Considerations
Wei Chen, Weizhong Zhang, Haoyu Zhao
Continuous influence maximization (CIM) generalizes the original influence maximization by incorporating general marketing strategies: a marketing strategy mix is a vector $\boldsy…
Convex Formulation of Overparameterized Deep Neural Networks
Cong Fang, Yihong Gu, Weizhong Zhang +1
Analysis of over-parameterized neural networks has drawn significant attention in recentyears. It was shown that such systems behave like convex systems under various restrictedset…
A Sufficient Condition for Convergences of Adam and RMSProp
Fangyu Zou, Li Shen, Zequn Jie +2
Adam and RMSProp are two of the most influential adaptive stochastic algorithms for training deep neural networks, which have been pointed out to be divergent even in the convex se…
Safe Element Screening for Submodular Function Minimization
Weizhong Zhang, Bin Hong, Lin Ma +2
Submodular functions are discrete analogs of convex functions, which have applications in various fields, including machine learning and computer vision. However, in large-scale ap…
Hierarchical Attention-Based Recurrent Highway Networks for Time Series Prediction
Yunzhe Tao, Lin Ma, Weizhong Zhang +3
Time series prediction has been studied in a variety of domains. However, it is still challenging to predict future series given historical observations and past exogenous data. Ex…
Scaling Up Sparse Support Vector Machines by Simultaneous Feature and Sample Reduction
Weizhong Zhang, Bin Hong, Wei Liu +4
Sparse support vector machine (SVM) is a popular classification technique that can simultaneously learn a small set of the most interpretable features and identify the support vect…