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Found 12 papers, 3 papers with code
An Algorithm for Streaming Differentially Private Data
Much of the research in differential privacy has focused on offline applications with the assumption that all data is available at once.
On the (In)Compatibility between Group Fairness and Individual Fairness
Furthermore, when there exists a conflict between the two, we first relax the former to the Pareto frontier (or equivalently the optimal trade-off) between $L^2$ error and statistical disparity, and then analyze the compatibility between the frontier and the individual fairness requirements.
Differentially Private Low-dimensional Synthetic Data from High-dimensional Datasets
Differentially private synthetic data provide a powerful mechanism to enable data analysis while protecting sensitive information about individuals.
Semi-Supervised Clustering of Sparse Graphs: Crossing the Information-Theoretic Threshold
The stochastic block model is a canonical random graph model for clustering and community detection on network-structured data.
Fair Data Representation for Machine Learning at the Pareto Frontier
Numerical simulations underscore the advantages: (1) the pre-processing step is compositive with arbitrary conditional expectation estimation supervised learning methods and unseen data; (2) the fair representation protects the sensitive information by limiting the inference capability of the remaining data with respect to the sensitive data; (3) the optimal affine maps are computationally efficient even for high-dimensional data.
GRAND++: Graph Neural Diffusion with A Source Term
We propose GRAph Neural Diffusion with a source term (GRAND++) for graph deep learning with a limited number of labeled nodes, i. e., low-labeling rate.
A Performance Guarantee for Spectral Clustering
The two-step spectral clustering method, which consists of the Laplacian eigenmap and a rounding step, is a widely used method for graph partitioning.
Strong Consistency, Graph Laplacians, and the Stochastic Block Model
We study the performance of classical two-step spectral clustering via the graph Laplacian to learn the stochastic block model.
A Hybrid Architecture for On-Device Compressive Machine Learning
By processing the data on the device that has collected the data, we can dramatically increase the level of privacy.
What Happens on the Edge, Stays on the Edge: Toward Compressive Deep Learning
We propose a hybrid hardware-software framework that has the potential to significantly reduce the computational complexity and memory requirements of on-device machine learning.
Certifying Global Optimality of Graph Cuts via Semidefinite Relaxation: A Performance Guarantee for Spectral Clustering
This paper is devoted to the theoretical foundations of spectral clustering and graph cuts.
Rapid, Robust, and Reliable Blind Deconvolution via Nonconvex Optimization
To the best of our knowledge, our algorithm is the first blind deconvolution algorithm that is numerically efficient, robust against noise, and comes with rigorous recovery guarantees under certain subspace conditions.
Information Theory Information Theory
