Search Results for author: Justin Hsu

Found 12 papers, 5 papers with code

Learning Differentially Private Mechanisms

no code implementations4 Jan 2021 Subhajit Roy, Justin Hsu, Aws Albarghouthi

We demonstrate that our approach is able to learn foundational algorithms from the differential privacy literature and significantly outperforms natural program synthesis baselines.

Program Synthesis

Analyzing Accuracy Loss in Randomized Smoothing Defenses

no code implementations3 Mar 2020 Yue Gao, Harrison Rosenberg, Kassem Fawaz, Somesh Jha, Justin Hsu

In test-time attacks an adversary crafts adversarial examples, which are specially crafted perturbations imperceptible to humans which, when added to an input example, force a machine learning model to misclassify the given input example.

Autonomous Driving Speech Recognition

Hypothesis Testing Interpretations and Renyi Differential Privacy

no code implementations24 May 2019 Borja Balle, Gilles Barthe, Marco Gaboardi, Justin Hsu, Tetsuya Sato

These conditions are useful to analyze the distinguishability power of divergences and we use them to study the hypothesis testing interpretation of some relaxations of differential privacy based on Renyi divergence.

Two-sample testing

Data Poisoning against Differentially-Private Learners: Attacks and Defenses

no code implementations23 Mar 2019 Yuzhe Ma, Xiaojin Zhu, Justin Hsu

Data poisoning attacks aim to manipulate the model produced by a learning algorithm by adversarially modifying the training set.

Data Poisoning

Fake News Detection via NLP is Vulnerable to Adversarial Attacks

1 code implementation5 Jan 2019 Zhixuan Zhou, Huankang Guan, Meghana Moorthy Bhat, Justin Hsu

In this paper, we argue that these models have the potential to misclassify fact-tampering fake news as well as under-written real news.

Fact Checking Fake News Detection

Do Prices Coordinate Markets?

no code implementations3 Nov 2015 Justin Hsu, Jamie Morgenstern, Ryan Rogers, Aaron Roth, Rakesh Vohra

Second, we provide learning-theoretic results that show that such prices are robust to changing the buyers in the market, so long as all buyers are sampled from the same (unknown) distribution.

Really Natural Linear Indexed Type Checking

1 code implementation16 Mar 2015 Arthur Azevedo de Amorim, Emilio Jesús Gallego Arias, Marco Gaboardi, Justin Hsu

A natural way to enhance the expressiveness of this approach is by allowing the indices to depend on runtime information, in the spirit of dependent types.

Logic in Computer Science

Computer-aided verification in mechanism design

1 code implementation13 Feb 2015 Gilles Barthe, Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Pierre-Yves Strub

To address both concerns, we explore techniques from computer-aided verification to construct formal proofs of incentive properties.

Computer Science and Game Theory Logic in Computer Science

Higher-Order Approximate Relational Refinement Types for Mechanism Design and Differential Privacy

1 code implementation25 Jul 2014 Gilles Barthe, Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Pierre-Yves Strub

Unlike typical programmatic properties, it is not sufficient for algorithms to merely satisfy the property---incentive properties are only useful if the strategic agents also believe this fact.

Programming Languages Computer Science and Game Theory

Privately Solving Linear Programs

no code implementations15 Feb 2014 Justin Hsu, Aaron Roth, Tim Roughgarden, Jonathan Ullman

In this paper, we initiate the systematic study of solving linear programs under differential privacy.

Dual Query: Practical Private Query Release for High Dimensional Data

no code implementations6 Feb 2014 Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Zhiwei Steven Wu

We present a practical, differentially private algorithm for answering a large number of queries on high dimensional datasets.

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