Replication code for "State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual Prediction"
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Updated
May 28, 2024 - R
Replication code for "State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual Prediction"
Implementation of tensor network algorithms for completion of sparsely sampled quantum states
Tensor Extraction of Latent Features (T-ELF). Within T-ELF's arsenal are non-negative matrix and tensor factorization solutions, equipped with automatic model determination (also known as the estimation of latent factors - rank) for accurate data modeling. Our software suite encompasses cutting-edge data pre-processing and post-processing modules.
My graduate research on low-rank matrix and tensor completion, and maximum volume algorithms for finding dominant submatrices.
mfair: Matrix Factorization with Auxiliary Information in R
Bachelor Thesis in Econometrics, Maastricht University
Imputation method for scRNA-seq based on low-rank approximation
Solve many kinds of least-squares and matrix-recovery problems
An official implementation of "Joint Inference of Diffusion and Structure in Partially Observed Social Networks Using Coupled Matrix Factorization"
Scaled matrix completion and cell deconvolution with NanoString data, Yichen Zhang, 2019
Exact Matrix Completion via Convex Optimization
Lightweight Python library for in-memory matrix completion.
[ICML 2019] ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation
A fast and accurate deconvolution algorithm based on regularized matrix completion algorithm (ENIGMA)
A project performing gradient descent and stochastic average gradient descent for matrix completion. The algorithms are tested on some synthetic data before being used on downscaled real X-ray absorption data from a spectromicroscopy experiment. The algorithms' behaviours and outputs are examined in the report.
This is the repository of our article published in RecSys 2019 "Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches" and of several follow-up studies.
Conformalized matrix completion
Communication-Efficient Stratified Stochastic Gradient Descent for Distributed Matrix Completion
Code for ICLR2023 paper "Graph Signal Sampling for Inductive 1-bit Matrix Completion: a Closed-Form Solution"
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