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Found 10 papers, 2 papers with code
Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale
In industry system, such as ranking system in Meta, it is unclear whether NAS algorithms from the literature can outperform production baselines because of: (1) scale - Meta ranking systems serve billions of users, (2) strong baselines - the baselines are production models optimized by hundreds to thousands of world-class engineers for years since the rise of deep learning, (3) dynamic baselines - engineers may have established new and stronger baselines during NAS search, and (4) efficiency - the search pipeline must yield results quickly in alignment with the productionization life cycle.
NASRec: Weight Sharing Neural Architecture Search for Recommender Systems
To overcome the data multi-modality and architecture heterogeneity challenges in the recommendation domain, NASRec establishes a large supernet (i. e., search space) to search the full architectures.
Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction
Click-Through Rate (CTR) prediction is one of the most important machine learning tasks in recommender systems, driving personalized experience for billions of consumers.
Feature Interaction Interpretability: A Case for Explaining Ad-Recommendation Systems via Neural Interaction Detection
Recommendation is a prevalent application of machine learning that affects many users; therefore, it is important for recommender models to be accurate and interpretable.
Detecting Statistical Interactions from Neural Network Weights
Interpreting neural networks is a crucial and challenging task in machine learning.
SPALS: Fast Alternating Least Squares via Implicit Leverage Scores Sampling
In this paper, we show ways of sampling intermediate steps of alternating minimization algorithms for computing low rank tensor CP decompositions, leading to the sparse alternating least squares (SPALS) method.
On Bochner's and Polya's Characterizations of Positive-Definite Kernels and the Respective Random Feature Maps
A well-known construction of such functions comes from Bochner's characterization, which connects a positive-definite function with a probability distribution.
Spectral Sparsification of Random-Walk Matrix Polynomials
Our work is particularly motivated by the algorithmic problems for speeding up the classic Newton's method in applications such as computing the inverse square-root of the precision matrix of a Gaussian random field, as well as computing the $q$th-root transition (for $q\geq1$) in a time-reversible Markov model.
Model Selection for Topic Models via Spectral Decomposition
Topic models have achieved significant successes in analyzing large-scale text corpus.
Scalable Parallel Factorizations of SDD Matrices and Efficient Sampling for Gaussian Graphical Models
random samples for $n$-dimensional Gaussian random fields with SDDM precision matrices.
