Search Results for author: Chao Pan

Found 12 papers, 6 papers with code

Unlearning Nonlinear Graph Classifiers in the Limited Training Data Regime

1 code implementation6 Nov 2022 Chao Pan, Eli Chien, Olgica Milenkovic

As the demand for user privacy grows, controlled data removal (machine unlearning) is becoming an important feature of machine learning models for data-sensitive Web applications such as social networks and recommender systems.

Graph Classification Recommendation Systems

Machine Unlearning of Federated Clusters

no code implementations28 Oct 2022 Chao Pan, Jin Sima, Saurav Prakash, Vishal Rana, Olgica Milenkovic

Federated clustering is an unsupervised learning problem that arises in a number of practical applications, including personalized recommender and healthcare systems.


On the Study of Sample Complexity for Polynomial Neural Networks

no code implementations18 Jul 2022 Chao Pan, Chuanyi Zhang

As a general type of machine learning approach, artificial neural networks have established state-of-art benchmarks in many pattern recognition and data analysis tasks.

Face Recognition Image Generation

Certified Graph Unlearning

1 code implementation18 Jun 2022 Eli Chien, Chao Pan, Olgica Milenkovic

For example, when unlearning $20\%$ of the nodes on the Cora dataset, our approach suffers only a $0. 1\%$ loss in test accuracy while offering a $4$-fold speed-up compared to complete retraining.


Provably Accurate and Scalable Linear Classifiers in Hyperbolic Spaces

1 code implementation7 Mar 2022 Chao Pan, Eli Chien, Puoya Tabaghi, Jianhao Peng, Olgica Milenkovic

The excellent performance of the Poincar\'e second-order and strategic perceptrons shows that the proposed framework can be extended to general machine learning problems in hyperbolic spaces.

Time Series Analysis

Highly Scalable and Provably Accurate Classification in Poincare Balls

1 code implementation8 Sep 2021 Eli Chien, Chao Pan, Puoya Tabaghi, Olgica Milenkovic

For hierarchical data, the space of choice is a hyperbolic space since it guarantees low-distortion embeddings for tree-like structures.

Classification Time Series Analysis

You are AllSet: A Multiset Function Framework for Hypergraph Neural Networks

1 code implementation ICLR 2022 Eli Chien, Chao Pan, Jianhao Peng, Olgica Milenkovic

We propose AllSet, a new hypergraph neural network paradigm that represents a highly general framework for (hyper)graph neural networks and for the first time implements hypergraph neural network layers as compositions of two multiset functions that can be efficiently learned for each task and each dataset.

Benchmarking Node Classification

Linear Classifiers in Product Space Forms

1 code implementation19 Feb 2021 Puoya Tabaghi, Chao Pan, Eli Chien, Jianhao Peng, Olgica Milenkovic

The results show that classification in low-dimensional product space forms for scRNA-seq data offers, on average, a performance improvement of $\sim15\%$ when compared to that in Euclidean spaces of the same dimension.

Spatio-Temporal Graph Scattering Transform

no code implementations ICLR 2021 Chao Pan, Siheng Chen, Antonio Ortega

Although spatio-temporal graph neural networks have achieved great empirical success in handling multiple correlated time series, they may be impractical in some real-world scenarios due to a lack of sufficient high-quality training data.

Time Series Analysis

Image processing in DNA

no code implementations22 Oct 2019 Chao Pan, S. M. Hossein Tabatabaei Yazdi, S Kasra Tabatabaei, Alvaro G. Hernandez, Charles Schroeder, Olgica Milenkovic

The main obstacles for the practical deployment of DNA-based data storage platforms are the prohibitively high cost of synthetic DNA and the large number of errors introduced during synthesis.

Image Inpainting Quantization

Query K-means Clustering and the Double Dixie Cup Problem

no code implementations NeurIPS 2018 I Chien, Chao Pan, Olgica Milenkovic

We consider the problem of approximate $K$-means clustering with outliers and side information provided by same-cluster queries and possibly noisy answers.

Group Additive Structure Identification for Kernel Nonparametric Regression

no code implementations NeurIPS 2017 Chao Pan, Michael Zhu

The additive model is one of the most popularly used models for high dimensional nonparametric regression analysis.


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