b'David Evans'

DeepAI AI Chat

Featured Co-authors

Yang Liu
344 publications
Mehdi Bennis
169 publications
Quanquan Gu
116 publications
Dawn Song
115 publications
Martin Jaggi
95 publications
Qiang Yang
92 publications
Ramesh Raskar
78 publications
Han Yu
76 publications
Taylor Berg-Kirkpatrick
65 publications
Yuan Tian
62 publications
Xiao Zhang
60 publications

research

∙ 03/02/2023

GlucoSynth: Generating Differentially-Private Synthetic Glucose Traces

In this paper we focus on the problem of generating high-quality, privat...

0 Josephine Lamp, et al. ∙

research

∙ 01/06/2023

TrojanPuzzle: Covertly Poisoning Code-Suggestion Models

With tools like GitHub Copilot, automatic code suggestion is no longer a...

0 Hojjat Aghakhani, et al. ∙

research

∙ 10/20/2022

Balanced Adversarial Training: Balancing Tradeoffs between Fickleness and Obstinacy in NLP Models

Traditional (fickle) adversarial examples involve finding a small pertur...

7 Hannah Chen, et al. ∙

research

∙ 09/02/2022

Are Attribute Inference Attacks Just Imputation?

Models can expose sensitive information about their training data. In an...

9 Bargav Jayaraman, et al. ∙

research

∙ 05/25/2022

Memorization in NLP Fine-tuning Methods

Large language models are shown to present privacy risks through memoriz...

0 Fatemehsadat Mireshghallah, et al. ∙

research

∙ 06/07/2021

Formalizing Distribution Inference Risks

Property inference attacks reveal statistical properties about a trainin...

12 Anshuman Suri, et al. ∙

research

∙ 04/30/2021

Stealthy Backdoors as Compression Artifacts

In a backdoor attack on a machine learning model, an adversary produces ...

27 Yulong Tian, et al. ∙

research

∙ 03/24/2021

Improved Estimation of Concentration Under ℓ_p-Norm Distance Metrics Using Half Spaces

Concentration of measure has been argued to be the fundamental cause of ...

2 Jack Prescott, et al. ∙

research

∙ 11/03/2020

Finding Friends and Flipping Frenemies: Automatic Paraphrase Dataset Augmentation Using Graph Theory

Most NLP datasets are manually labeled, so suffer from inconsistent labe...

8 Hannah Chen, et al. ∙

research

∙ 06/30/2020

Model-Targeted Poisoning Attacks: Provable Convergence and Certified Bounds

Machine learning systems that rely on training data collected from untru...

0 Fnu Suya, et al. ∙

research

∙ 05/25/2020

Pointwise Paraphrase Appraisal is Potentially Problematic

The prevailing approach for training and evaluating paraphrase identific...

0 Hannah Chen, et al. ∙

research

∙ 05/21/2020

Revisiting Membership Inference Under Realistic Assumptions

Membership inference attacks on models trained using machine learning ha...

5 Bargav Jayaraman, et al. ∙

research

∙ 04/21/2020

Certifying Joint Adversarial Robustness for Model Ensembles

Deep Neural Networks (DNNs) are often vulnerable to adversarial examples...

4 Mainuddin Ahmad Jonas, et al. ∙

research

∙ 03/20/2020

One Neuron to Fool Them All

Despite vast research in adversarial examples, the root causes of model ...

22 Anshuman Suri, et al. ∙

research

∙ 03/01/2020

Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models

Starting with Gilmer et al. (2018), several works have demonstrated the ...

4 Xiao Zhang, et al. ∙

research

∙ 02/26/2020

Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization

Training machine learning models to be robust against adversarial inputs...

6 Sicheng Zhu, et al. ∙

research

∙ 12/10/2019

Advances and Open Problems in Federated Learning

Federated learning (FL) is a machine learning setting where many clients...

33 Peter Kairouz, et al. ∙

research

∙ 10/30/2019

Efficient Privacy-Preserving Nonconvex Optimization

While many solutions for privacy-preserving convex empirical risk minimi...

17 Lingxiao Wang, et al. ∙

research

∙ 08/19/2019

Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries

In a black-box setting, the adversary only has API access to the target ...

0 Fnu Suya, et al. ∙

research

∙ 05/29/2019

Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness

Many recent works have shown that adversarial examples that fool classif...

2 Saeed Mahloujifar, et al. ∙

research

∙ 02/24/2019

When Relaxations Go Bad: "Differentially-Private" Machine Learning

Differential privacy is becoming a standard notion for performing privac...

4 Bargav Jayaraman, et al. ∙

research

∙ 01/28/2019

Context-aware Monitoring in Robotic Surgery

Robotic-assisted minimally invasive surgery (MIS) has enabled procedures...

0 Mohammad Samin Yasar, et al. ∙

research

∙ 12/28/2018

EPPA for two-graphs and antipodal metric spaces

We prove that the class of two-graphs has the extension property for par...

0 David Evans, et al. ∙

research

∙ 10/22/2018

Cost-Sensitive Robustness against Adversarial Examples

Several recent works have developed methods for training classifiers tha...

10 Xiao Zhang, et al. ∙

research

∙ 05/06/2018

Smoothing and Mapping using Multiple Robots

Mapping expansive regions is an arduous and often times incomplete when ...

0 Karthik Paga, et al. ∙

research

∙ 05/05/2018

AutoTiering: Automatic Data Placement Manager in Multi-Tier All-Flash Datacenter

In the year of 2017, the capital expenditure of Flash-based Solid State ...

0 Zhengyu Yang, et al. ∙

research

∙ 01/26/2018

Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning

Machine learning is a popular approach to signatureless malware detectio...

0 Hyrum S. Anderson, et al. ∙

research

∙ 12/23/2017

Query-limited Black-box Attacks to Classifiers

We study black-box attacks on machine learning classifiers where each qu...

0 Fnu Suya, et al. ∙

research

∙ 04/04/2017

Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks

Although deep neural networks (DNNs) have achieved great success in many...

0 Weilin Xu, et al. ∙