46 citations · 84 across the 6 of their papers we have counts for
6 papers
Learning from Aggregated Data: Curated Bags versus Random Bags
Lin Chen, Gang Fu, Amin Karbasi +1
Protecting user privacy is a major concern for many machine learning systems that are deployed at scale and collect from a diverse set of population. One way to address this concer…
Approximately Optimal Core Shapes for Tensor Decompositions
Mehrdad Ghadiri, Matthew Fahrbach, Gang Fu +1
This work studies the combinatorial optimization problem of finding an optimal core tensor shape, also called multilinear rank, for a size-constrained Tucker decomposition. We give…
Sequential Attention for Feature Selection
Taisuke Yasuda, MohammadHossein Bateni, Lin Chen +3
Feature selection is the problem of selecting a subset of features for a machine learning model that maximizes model quality subject to a budget constraint. For neural networks, pr…
Feature Cross Search via Submodular Optimization
Lin Chen, Hossein Esfandiari, Gang Fu +2
In this paper, we study feature cross search as a fundamental primitive in feature engineering. The importance of feature cross search especially for the linear model has been know…
Deep & Cross Network for Ad Click Predictions
Ruoxi Wang, Bin Fu, Gang Fu +1
Feature engineering has been the key to the success of many prediction models. However, the process is non-trivial and often requires manual feature engineering or exhaustive searc…
Greedy Column Subset Selection: New Bounds and Distributed Algorithms
Jason Altschuler, Aditya Bhaskara, Gang Fu +3
The problem of column subset selection has recently attracted a large body of research, with feature selection serving as one obvious and important application. Among the technique…