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20182021
most citedFast and Memory Efficient Differentially Private-SGD via JL Projections

8 citations · 8 across the 2 of their papers we have counts for

collaborators

9 papers

cs.CY2021

Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency

Kyra Yee, Uthaipon Tantipongpipat, Shubhanshu Mishra

Twitter uses machine learning to crop images, where crops are centered around the part predicted to be the most salient. In fall 2020, Twitter users raised concerns that the automa…

cs.LG20218 cited

Fast and Memory Efficient Differentially Private-SGD via JL Projections

Zhiqi Bu, Sivakanth Gopi, Janardhan Kulkarni +3

Differentially Private-SGD (DP-SGD) of Abadi et al. (2016) and its variations are the only known algorithms for private training of large scale neural networks. This algorithm requ…

cs.DS2020

-Regularized A-Optimal Design and its Approximation by -Regularized Proportional Volume Sampling

Uthaipon Tantipongpipat

In this work, we study the -regularized -optimal design problem and introduce the -regularized proportional volume sampling algorithm, generalized from [Nikolov, Singh, an…

cs.DS2020

Maximizing Determinants under Matroid Constraints

Vivek Madan, Aleksandar Nikolov, Mohit Singh +1

Given vectors and a matroid , we study the problem of finding a basis of such that is maximized…

cs.LG2019

Differentially Private Synthetic Mixed-Type Data Generation For Unsupervised Learning

Uthaipon Tantipongpipat, Chris Waites, Digvijay Boob +2

We introduce the DP-auto-GAN framework for synthetic data generation, which combines the low dimensional representation of autoencoders with the flexibility of Generative Adversari…

cs.DM2019

Multi-Criteria Dimensionality Reduction with Applications to Fairness

Uthaipon Tantipongpipat, Samira Samadi, Mohit Singh +2

Dimensionality reduction is a classical technique widely used for data analysis. One foundational instantiation is Principal Component Analysis (PCA), which minimizes the average r…