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Differentially Private Multiway and k-Cut

Differentially Private Multiway and k-Cut

A Google TechTalk, 2024-10-30, presented by Zongrui Zou Privacy in ML Seminar. ABSTRACT: In this talk, we show how to ...

Zongrui Zou: Differentially Private Multiway and k-Cut

Zongrui Zou: Differentially Private Multiway and k-Cut

CMU Theory Lunch Talk Speaker: Zongrui Zou Date: October 9, 2024 Title:

Approximation Algorithm for Multiway Cut

Approximation Algorithm for Multiway Cut

Read more details and related context about Approximation Algorithm for Multiway Cut.

Differentially Private Multi-party Data Release for Linear Regression

Differentially Private Multi-party Data Release for Linear Regression

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Differentially Private Fine-tuning of Language Models

Differentially Private Fine-tuning of Language Models

A Google TechTalk, presented by Gautam Kamath, University of Waterloo, at the 2021 Google Federated Learning and Analytics ...

2020 Methods Lecture, Daniel Kifer, "Basic Differential Privacy Algorithms and Statistics"

2020 Methods Lecture, Daniel Kifer, "Basic Differential Privacy Algorithms and Statistics"

Read more details and related context about 2020 Methods Lecture, Daniel Kifer, "Basic Differential Privacy Algorithms and Statistics".

2020 Methods Lecture,  Daniel Kife, "Introduction to Differential Privacy"

2020 Methods Lecture, Daniel Kife, "Introduction to Differential Privacy"

Read more details and related context about 2020 Methods Lecture, Daniel Kife, "Introduction to Differential Privacy".

Differential Privacy - Simply Explained

Differential Privacy - Simply Explained

Companies are collecting more and more data about us and that can cause harm. With

Shrinkwrap: Differentially-Private Query Processing in Private Data Federations

Shrinkwrap: Differentially-Private Query Processing in Private Data Federations

Johes Bater (Northwestern University) Privacy and the Science of Data Analysis ...

Marginal-based Methods for Differentially Private Synthetic Data

Marginal-based Methods for Differentially Private Synthetic Data

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