Om Thakkar | DeepAI

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Nathan Srebro
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Gautam Kamath
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Françoise Beaufays
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∙ 02/19/2023

Why Is Public Pretraining Necessary for Private Model Training?

In the privacy-utility tradeoff of a model trained on benchmark language...

0 Arun Ganesh, et al. ∙

∙ 10/04/2022

Recycling Scraps: Improving Private Learning by Leveraging Intermediate Checkpoints

All state-of-the-art (SOTA) differentially private machine learning (DP ...

0 Virat Shejwalkar, et al. ∙

∙ 04/20/2022

Detecting Unintended Memorization in Language-Model-Fused ASR

End-to-end (E2E) models are often being accompanied by language models (...

0 W. Ronny Huang, et al. ∙

∙ 04/18/2022

Extracting Targeted Training Data from ASR Models, and How to Mitigate It

Recent work has designed methods to demonstrate that model updates in AS...

0 Ehsan Amid, et al. ∙

∙ 11/09/2021

The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection

Hyperparameter optimization is a ubiquitous challenge in machine learnin...

0 Shubhankar Mohapatra, et al. ∙

∙ 10/31/2021

Revealing and Protecting Labels in Distributed Training

Distributed learning paradigms such as federated learning often involve ...

0 Trung Dang, et al. ∙

∙ 04/15/2021

A Method to Reveal Speaker Identity in Distributed ASR Training, and How to Counter It

End-to-end Automatic Speech Recognition (ASR) models are commonly traine...

0 Trung Dang, et al. ∙

∙ 09/21/2020

Training Production Language Models without Memorizing User Data

This paper presents the first consumer-scale next-word prediction (NWP) ...

0 Swaroop Ramaswamy, et al. ∙

∙ 07/13/2020

Privacy Amplification via Random Check-Ins

Differentially Private Stochastic Gradient Descent (DP-SGD) forms a fund...

12 Borja Balle, et al. ∙

∙ 06/12/2020

Understanding Unintended Memorization in Federated Learning

Recent works have shown that generative sequence models (e.g., language ...

0 Om Thakkar, et al. ∙

∙ 06/21/2019

Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis

We design a general framework for answering adaptive statistical queries...

0 Ryan Rogers, et al. ∙

∙ 05/09/2019

Differentially Private Learning with Adaptive Clipping

We introduce a new adaptive clipping technique for training learning mod...

0 Om Thakkar, et al. ∙

∙ 03/14/2018

Model-Agnostic Private Learning via Stability

We design differentially private learning algorithms that are agnostic t...

0 Raef Bassily, et al. ∙

∙ 12/28/2017

Differentially Private Matrix Completion, Revisited

We study the problem of privacy-preserving collaborative filtering where...

0 Prateek Jain, et al. ∙

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