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Found 15 papers, 9 papers with code
Group Additive Structure Identification for Kernel Nonparametric Regression
The additive model is one of the most popularly used models for high dimensional nonparametric regression analysis.
Query K-means Clustering and the Double Dixie Cup Problem
We consider the problem of approximate $K$-means clustering with outliers and side information provided by same-cluster queries and possibly noisy answers.
Image processing in DNA
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.
Spatio-Temporal Graph Scattering Transform
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.
Linear Classifiers in Product Space Forms
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.
You are AllSet: A Multiset Function Framework for Hypergraph Neural Networks
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.
Highly Scalable and Provably Accurate Classification in Poincare Balls
For hierarchical data, the space of choice is a hyperbolic space since it guarantees low-distortion embeddings for tree-like structures.
Provably Accurate and Scalable Linear Classifiers in Hyperbolic Spaces
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.
Certified Graph Unlearning
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.
On the Study of Sample Complexity for Polynomial Neural Networks
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.
Machine Unlearning of Federated Clusters
We introduce, for the first time, the problem of machine unlearning for FC, and propose an efficient unlearning mechanism for a customized secure FC framework.
Unlearning Graph Classifiers with Limited Data Resources
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.
Semi-Quantitative Group Testing for Efficient and Accurate qPCR Screening of Pathogens with a Wide Range of Loads
Pathogenic infections pose a significant threat to global health, affecting millions of people every year and presenting substantial challenges to healthcare systems worldwide.
Federated Classification in Hyperbolic Spaces via Secure Aggregation of Convex Hulls
Third, we compute the complexity of the convex hulls in hyperbolic spaces to assess the extent of data leakage; at the same time, in order to limit communication cost for the hulls, we propose a new quantization method for the Poincar\'e disc coupled with Reed-Solomon-like encoding.
Interpretable Online Network Dictionary Learning for Inferring Long-Range Chromatin Interactions
First, we demonstrate that online cvxNDL retains the accuracy of classical DL methods while simultaneously ensuring unique interpretability and scalability.
