74 citations · 170 across the 14 of their papers we have counts for
14 papers
Adaptive Bregman Proximal Stochastic Gradient with a Stabilized Barzilai--Borwein Step Size
Chenhan Jin, Shengze Xu, Binghui Xie +4
Bregman proximal stochastic gradient (BPSG) methods bring variance-reduced composite optimization to objectives whose geometry is poorly captured by Euclidean smoothness. Their per…
Understanding and Improving Feature Learning for Out-of-Distribution Generalization
Yongqiang Chen, Wei Huang, Kaiwen Zhou +3
A common explanation for the failure of out-of-distribution (OOD) generalization is that the model trained with empirical risk minimization (ERM) learns spurious features instead o…
Follower Agnostic Methods for Stackelberg Games
Chinmay Maheshwari, James Cheng, S. Shankar Sasty +2
In this paper, we present an efficient algorithm to solve online Stackelberg games, featuring multiple followers, in a follower-agnostic manner. Unlike previous works, our approach…
Measuring and Improving the Use of Graph Information in Graph Neural Networks
Yifan Hou, Jian Zhang, James Cheng +4
Graph neural networks (GNNs) have been widely used for representation learning on graph data. However, there is limited understanding on how much performance GNNs actually gain fro…
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack
Ruize Gao, Jiongxiao Wang, Kaiwen Zhou +5
The AutoAttack (AA) has been the most reliable method to evaluate adversarial robustness when considerable computational resources are available. However, the high computational co…
Efficient Private SCO for Heavy-Tailed Data via Averaged Clipping
Chenhan Jin, Kaiwen Zhou, Bo Han +2
We consider stochastic convex optimization for heavy-tailed data with the guarantee of being differentially private (DP). Most prior works on differentially private stochastic conv…
Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization
Yongqiang Chen, Kaiwen Zhou, Yatao Bian +8
Recently, there has been a growing surge of interest in enabling machine learning systems to generalize well to Out-of-Distribution (OOD) data. Most efforts are devoted to advancin…
An Adaptive Incremental Gradient Method With Support for Non-Euclidean Norms
Binghui Xie, Chenhan Jin, Kaiwen Zhou +2
Stochastic variance reduced methods have shown strong performance in solving finite-sum problems. However, these methods usually require the users to manually tune the step-size, w…
Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs
Yongqiang Chen, Yonggang Zhang, Yatao Bian +6
Despite recent success in using the invariance principle for out-of-distribution (OOD) generalization on Euclidean data (e.g., images), studies on graph data are still limited. Dif…
Accelerating Perturbed Stochastic Iterates in Asynchronous Lock-Free Optimization
Kaiwen Zhou, Anthony Man-Cho So, James Cheng
We show that stochastic acceleration can be achieved under the perturbed iterate framework (Mania et al., 2017) in asynchronous lock-free optimization, which leads to the optimal i…
Local Reweighting for Adversarial Training
Ruize Gao, Feng Liu, Kaiwen Zhou +3
Instances-reweighted adversarial training (IRAT) can significantly boost the robustness of trained models, where data being less/more vulnerable to the given attack are assigned sm…
Practical Schemes for Finding Near-Stationary Points of Convex Finite-Sums
Kaiwen Zhou, Lai Tian, Anthony Man-Cho So +1
In convex optimization, the problem of finding near-stationary points has not been adequately studied yet, unlike other optimality measures such as the function value. Even in the…
Fast Stochastic Variance Reduced Gradient Method with Momentum Acceleration for Machine Learning
Fanhua Shang, Yuanyuan Liu, James Cheng +1
Recently, research on accelerated stochastic gradient descent methods (e.g., SVRG) has made exciting progress (e.g., linear convergence for strongly convex problems). However, the…
Guaranteed Sufficient Decrease for Variance Reduced Stochastic Gradient Descent
Fanhua Shang, Yuanyuan Liu, James Cheng +2
In this paper, we propose a novel sufficient decrease technique for variance reduced stochastic gradient descent methods such as SAG, SVRG and SAGA. In order to make sufficient dec…