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Found 12 papers, 6 papers with code
Unlearning Nonlinear Graph Classifiers in the Limited Training Data Regime
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.
Machine Unlearning of Federated Clusters
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
