73 citations · 317 across the 29 of their papers we have counts for
29 papers
A Three-regime Model of Network Pruning
Yefan Zhou, Yaoqing Yang, Arin Chang +1
Recent work has highlighted the complex influence training hyperparameters, e.g., the number of training epochs, can have on the prunability of machine learning models. Perhaps sur…
When are ensembles really effective?
Ryan Theisen, Hyunsuk Kim, Yaoqing Yang +2
Ensembling has a long history in statistical data analysis, with many impactful applications. However, in many modern machine learning settings, the benefits of ensembling are less…
Neurotoxin: Durable Backdoors in Federated Learning
Zhengming Zhang, Ashwinee Panda, Linyue Song +5
Due to their decentralized nature, federated learning (FL) systems have an inherent vulnerability during their training to adversarial backdoor attacks. In this type of attack, the…
Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data
Yaoqing Yang, Ryan Theisen, Liam Hodgkinson +4
Selecting suitable architecture parameters and training hyperparameters is essential for enhancing machine learning (ML) model performance. Several recent empirical studies conduct…
The Effect of Model Size on Worst-Group Generalization
Alan Pham, Eunice Chan, Vikranth Srivatsa +6
Overparameterization is shown to result in poor test accuracy on rare subgroups under a variety of settings where subgroup information is known. To gain a more complete picture, we…
A Dataset-Dispersion Perspective on Reconstruction Versus Recognition in Single-View 3D Reconstruction Networks
Yefan Zhou, Yiru Shen, Yujun Yan +2
Neural networks (NN) for single-view 3D reconstruction (SVR) have gained in popularity. Recent work points out that for SVR, most cutting-edge NNs have limited performance on recon…
Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices
Puja Trivedi, Ekdeep Singh Lubana, Yujun Yan +2
Unsupervised graph representation learning is critical to a wide range of applications where labels may be scarce or expensive to procure. Contrastive learning (CL) is an increasin…
Taxonomizing local versus global structure in neural network loss landscapes
Yaoqing Yang, Liam Hodgkinson, Ryan Theisen +4
Viewing neural network models in terms of their loss landscapes has a long history in the statistical mechanics approach to learning, and in recent years it has received attention…
Self-supervised Spatial Reasoning on Multi-View Line Drawings
Siyuan Xiang, Anbang Yang, Yanfei Xue +2
Spatial reasoning on multi-view line drawings by state-of-the-art supervised deep networks is recently shown with puzzling low performances on the SPARE3D dataset. Based on the fac…
Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks
Yujun Yan, Milad Hashemi, Kevin Swersky +2
In node classification tasks, graph convolutional neural networks (GCNs) have demonstrated competitive performance over traditional methods on diverse graph data. However, it is kn…
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Models
Zhengming Zhang, Yaoqing Yang, Zhewei Yao +3
Federated learning (FL) is a promising way to use the computing power of mobile devices while maintaining the privacy of users. Current work in FL, however, makes the unrealistic a…
Boundary thickness and robustness in learning models
Yaoqing Yang, Rajiv Khanna, Yaodong Yu +5
Robustness of machine learning models to various adversarial and non-adversarial corruptions continues to be of interest. In this paper, we introduce the notion of the boundary thi…
Serverless Straggler Mitigation using Local Error-Correcting Codes
Vipul Gupta, Dominic Carrano, Yaoqing Yang +3
Inexpensive cloud services, such as serverless computing, are often vulnerable to straggling nodes that increase end-to-end latency for distributed computation. We propose and impl…
Deep Unsupervised Learning of 3D Point Clouds via Graph Topology Inference and Filtering
Siheng Chen, Chaojing Duan, Yaoqing Yang +3
We propose a deep autoencoder with graph topology inference and filtering to achieve compact representations of unorganized 3D point clouds in an unsupervised manner. Many previous…
Coded Elastic Computing
Yaoqing Yang, Matteo Interlandi, Pulkit Grover +3
Cloud providers have recently introduced new offerings whereby spare computing resources are accessible at discounts compared to on-demand computing. Exploiting such opportunity is…
An Application of Storage-Optimal MatDot Codes for Coded Matrix Multiplication: Fast k-Nearest Neighbors Estimation
Utsav Sheth, Sanghamitra Dutta, Malhar Chaudhari +5
We propose a novel application of coded computing to the problem of the nearest neighbor estimation using MatDot Codes [Fahim. et.al. 2017], that are known to be optimal for matrix…
Straggler-Resilient and Communication-Efficient Distributed Iterative Linear Solver
Farzin Haddadpour, Yaoqing Yang, Malhar Chaudhari +2
We propose a novel distributed iterative linear inverse solver method. Our method, PolyLin, has significantly lower communication cost, both in terms of number of rounds as well as…
Coded Iterative Computing using Substitute Decoding
Yaoqing Yang, Malhar Chaudhari, Pulkit Grover +1
In this paper, we propose a new coded computing technique called "substitute decoding" for general iterative distributed computation tasks. In the first part of the paper, we use P…
FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation
Yaoqing Yang, Chen Feng, Yiru Shen +1
Recent deep networks that directly handle points in a point set, e.g., PointNet, have been state-of-the-art for supervised learning tasks on point clouds such as classification and…
Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling
Yiru Shen, Chen Feng, Yaoqing Yang +1
Unlike on images, semantic learning on 3D point clouds using a deep network is challenging due to the naturally unordered data structure. Among existing works, PointNet has achieve…
Fast Path Localization on Graphs via Multiscale Viterbi Decoding
Yaoqing Yang, Siheng Chen, Mohammad Ali Maddah-Ali +3
We consider a problem of localizing a path-signal that evolves over time on a graph. A path-signal can be viewed as the trajectory of a moving agent on a graph in several consecuti…
Coding Method for Parallel Iterative Linear Solver
Yaoqing Yang, Pulkit Grover, Soummya Kar
Computationally intensive distributed and parallel computing is often bottlenecked by a small set of slow workers known as stragglers. In this paper, we utilize the emerging idea o…
Localization, Decomposition, and Dictionary Learning of Piecewise-Constant Signals on Graphs
Siheng Chen, Yaoqing Yang, José. M. F. Moura +1
Motivated by the need to extract meaning from large amounts of complex structured data, we consider three critical problems on graphs: localization, decomposition, and dictionary l…
Detecting Localized Categorical Attributes on Graphs
Siheng Chen, Yaoqing Yang, Shi Zong +2
Do users from Carnegie Mellon University form social communities on Facebook? Do signal processing researchers from tightly collaborate with each other? Do Chinese restaurants in M…