Search Results for author: Uthaipon Tantipongpipat

Found 8 papers, 4 papers with code

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

2 code implementations18 May 2021 Kyra Yee, Uthaipon Tantipongpipat, Shubhanshu Mishra

However, we demonstrate that formalized fairness metrics and quantitative analysis on their own are insufficient for capturing the risk of representational harm in automatic cropping.

Fairness Image Cropping

Fast and Memory Efficient Differentially Private-SGD via JL Projections

no code implementations5 Feb 2021 Zhiqi Bu, Sivakanth Gopi, Janardhan Kulkarni, Yin Tat Lee, Judy Hanwen Shen, Uthaipon Tantipongpipat

Unlike previous attempts to make DP-SGD faster which work only on a subset of network architectures or use compiler techniques, we propose an algorithmic solution which works for any network in a black-box manner which is the main contribution of this paper.

FAST DIFFERENTIALLY PRIVATE-SGD VIA JL PROJECTIONS

no code implementations1 Jan 2021 Zhiqi Bu, Sivakanth Gopi, Janardhan Kulkarni, Yin Tat Lee, Uthaipon Tantipongpipat

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.

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

no code implementations19 Jun 2020 Uthaipon Tantipongpipat

In this work, we study the $\lambda$-regularized $A$-optimal design problem and introduce the $\lambda$-regularized proportional volume sampling algorithm, generalized from [Nikolov, Singh, and Tantipongpipat, 2019], for this problem with the approximation guarantee that extends upon the previous work.

Maximizing Determinants under Matroid Constraints

no code implementations16 Apr 2020 Vivek Madan, Aleksandar Nikolov, Mohit Singh, Uthaipon Tantipongpipat

Our main result is a new approximation algorithm with an approximation guarantee that depends only on the dimension $d$ of the vectors and not on the size $k$ of the output set.

Experimental Design

Differentially Private Synthetic Mixed-Type Data Generation For Unsupervised Learning

1 code implementation6 Dec 2019 Uthaipon Tantipongpipat, Chris Waites, Digvijay Boob, Amaresh Ankit Siva, Rachel Cummings

We introduce the DP-auto-GAN framework for synthetic data generation, which combines the low dimensional representation of autoencoders with the flexibility of Generative Adversarial Networks (GANs).

Synthetic Data Generation

Multi-Criteria Dimensionality Reduction with Applications to Fairness

2 code implementations NeurIPS 2019 Uthaipon Tantipongpipat, Samira Samadi, Mohit Singh, Jamie Morgenstern, Santosh Vempala

Our main result is an exact polynomial-time algorithm for the two-criterion dimensionality reduction problem when the two criteria are increasing concave functions.

Dimensionality Reduction Fairness

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