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.

Low-Rank and Sparse Tools for Background Modeling and Subtraction in Videos

Fast and accurate machine learning on sparse matrices - matrix factorizations, regression, classification, top-N recommendations.

Predict scores of NBA games using regularized matrix completion

Matrix and Tensor Completion for Background Model Initialization

Lightweight Python library for in-memory matrix completion.

Imputation method for scRNA-seq based on low-rank approximation

Data Science and Matrix Optimization course

Matlab library for gradient descent algorithms: Version 1.0.1

Factor-Based Imputation for Missing Data

MATLAB library of gradient descent algorithms for sparse modeling: Version 1.0.3

Code for Graph Convolutional Matrix Factorization for Bipartite Edge Prediction

[ICML 2019] ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation

[Tool] Low rank matrix recovery by minimizing matrix norm

A visual complexity dataset across seven different categories, including Scenes, Advertisements, Visualization and infographics, Objects, Interior design, Art, and Suprematism for computer vision application.

OnLine Low-rank Subspace tracking by TEnsor CP Decomposition in Matlab: Version 1.0.1

[ICLR 2020, Oral] Harnessing Structures for Value-Based Planning and Reinforcement Learning

Solve many kinds of least-squares and matrix-recovery problems

A fast and accurate deconvolution algorithm based on regularized matrix completion algorithm (ENIGMA)

Low-rank Matrix Completion using Alternating Minimization