1 code implementation • 6 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.
no code implementations • 28 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.
no code implementations • 18 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.
1 code implementation • 18 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.
1 code implementation • 7 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.
1 code implementation • 8 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.
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.
1 code implementation • 19 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.
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.
no code implementations • 22 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.
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.
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.