23 citations · 65 across the 12 of their papers we have counts for
13 papers
iALS++: Speeding up Matrix Factorization with Subspace Optimization
Steffen Rendle, Walid Krichene, Li Zhang +1
iALS is a popular algorithm for learning matrix factorization models from implicit feedback with alternating least squares. This algorithm was invented over a decade ago but still…
Revisiting the Performance of iALS on Item Recommendation Benchmarks
Steffen Rendle, Walid Krichene, Li Zhang +1
Matrix factorization learned by implicit alternating least squares (iALS) is a popular baseline in recommender system research publications. iALS is known to be one of the most com…
Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates
Steve Chien, Prateek Jain, Walid Krichene +4
We study the problem of differentially private (DP) matrix completion under user-level privacy. We design a joint differentially private variant of the popular Alternating-Least-Sq…
Global Convergence of Second-order Dynamics in Two-layer Neural Networks
Walid Krichene, Kenneth F. Caluya, Abhishek Halder
Recent results have shown that for two-layer fully connected neural networks, gradient flow converges to a global optimum in the infinite width limit, by making a connection betwee…
Neural Collaborative Filtering vs. Matrix Factorization Revisited
Steffen Rendle, Walid Krichene, Li Zhang +1
Embedding based models have been the state of the art in collaborative filtering for over a decade. Traditionally, the dot product or higher order equivalents have been used to com…
Superbloom: Bloom filter meets Transformer
John Anderson, Qingqing Huang, Walid Krichene +2
We extend the idea of word pieces in natural language models to machine learning tasks on opaque ids. This is achieved by applying hash functions to map each id to multiple hash to…