Alexey Kurakin

According to our database, Alexey Kurakin authored at least 34 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

Diffusion Denoising as a Certified Defense against Clean-label Poisoning.

Sanghyun Hong, Nicholas Carlini, Alexey Kurakin

CoRR, 2024

DART: A Principled Approach to Adversarially Robust Unsupervised Domain Adaptation.

Yunjuan Wang, Hussein Hazimeh, Natalia Ponomareva, Alexey Kurakin, Ibrahim Hammoud, Raman Arora

CoRR, 2024

2023

Towards Large Scale Transfer Learning for Differentially Private Image Classification.

Harsh Mehta, Abhradeep Guha Thakurta, Alexey Kurakin, Ashok Cutkosky

Trans. Mach. Learn. Res., 2023

Differentially Private Image Classification from Features.

Harsh Mehta, Walid Krichene, Abhradeep Guha Thakurta, Alexey Kurakin, Ashok Cutkosky

Trans. Mach. Learn. Res., 2023

How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy.

Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H. Brendan McMahan, Sergei Vassilvitskii, Steve Chien, Abhradeep Guha Thakurta

J. Artif. Intell. Res., 2023

Harnessing large-language models to generate private synthetic text.

Alexey Kurakin, Natalia Ponomareva, Umar Syed, Liam MacDermed, Andreas Terzis

CoRR, 2023

Privacy-Preserving Recommender Systems with Synthetic Query Generation using Differentially Private Large Language Models.

Aldo Gael Carranza, Rezsa Farahani, Natalia Ponomareva, Alex Kurakin, Matthew Jagielski, Milad Nasr

CoRR, 2023

RETVec: Resilient and Efficient Text Vectorizer.

Elie Bursztein, Marina Zhang, Owen Vallis, Xinyu Jia, Alexey Kurakin

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

How to DP-fy ML: A Practical Tutorial to Machine Learning with Differential Privacy.

Natalia Ponomareva, Sergei Vassilvitskii, Zheng Xu, Brendan McMahan, Alexey Kurakin, Chiyaun Zhang

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

2022

Publishing Efficient On-device Models Increases Adversarial Vulnerability.

Sanghyun Hong, Nicholas Carlini, Alexey Kurakin

CoRR, 2022

Large Scale Transfer Learning for Differentially Private Image Classification.

Harsh Mehta, Abhradeep Thakurta, Alexey Kurakin, Ashok Cutkosky

CoRR, 2022

Toward Training at ImageNet Scale with Differential Privacy.

Alexey Kurakin, Steve Chien, Shuang Song, Roxana Geambasu, Andreas Terzis, Abhradeep Thakurta

CoRR, 2022

Handcrafted Backdoors in Deep Neural Networks.

Sanghyun Hong, Nicholas Carlini, Alexey Kurakin

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation.

David Berthelot, Rebecca Roelofs, Kihyuk Sohn, Nicholas Carlini, Alexey Kurakin

Proceedings of the Tenth International Conference on Learning Representations, 2022

2020

High Accuracy and High Fidelity Extraction of Neural Networks.

Matthew Jagielski, Nicholas Carlini, David Berthelot, Alex Kurakin, Nicolas Papernot

Proceedings of the 29th USENIX Security Symposium, 2020

FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence.

Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin Raffel, Ekin Dogus Cubuk, Alexey Kurakin, Chun-Liang Li

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming.

Sumanth Dathathri, Krishnamurthy Dvijotham, Alexey Kurakin, Aditi Raghunathan, Jonathan Uesato, Rudy Bunel, Shreya Shankar, Jacob Steinhardt, Ian J. Goodfellow, Percy Liang, Pushmeet Kohli

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring.

David Berthelot, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, Colin Raffel

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring.

David Berthelot, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, Colin Raffel

CoRR, 2019

High-Fidelity Extraction of Neural Network Models.

Matthew Jagielski, Nicholas Carlini, David Berthelot, Alex Kurakin, Nicolas Papernot

CoRR, 2019

On Evaluating Adversarial Robustness.

Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian J. Goodfellow, Aleksander Madry, Alexey Kurakin

CoRR, 2019

2018

Adversarial Vision Challenge.

Wieland Brendel, Jonas Rauber, Alexey Kurakin, Nicolas Papernot, Behar Veliqi, Marcel Salathé, Sharada P. Mohanty, Matthias Bethge

CoRR, 2018

Adversarial Attacks and Defences Competition.

Alexey Kurakin, Ian J. Goodfellow, Samy Bengio, Yinpeng Dong, Fangzhou Liao, Ming Liang, Tianyu Pang, Jun Zhu, Xiaolin Hu, Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, Alan L. Yuille, Sangxia Huang, Yao Zhao, Yuzhe Zhao, Zhonglin Han, Junjiajia Long, Yerkebulan Berdibekov, Takuya Akiba, Seiya Tokui, Motoki Abe

CoRR, 2018

Adversarial Logit Pairing.

Harini Kannan, Alexey Kurakin, Ian J. Goodfellow

CoRR, 2018

Adversarial Examples that Fool both Human and Computer Vision.

Gamaleldin F. Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alex Kurakin, Ian J. Goodfellow, Jascha Sohl-Dickstein

CoRR, 2018

Adversarial Examples that Fool both Computer Vision and Time-Limited Humans.

Gamaleldin F. Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alexey Kurakin, Ian J. Goodfellow, Jascha Sohl-Dickstein

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Ensemble Adversarial Training: Attacks and Defenses.

Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian J. Goodfellow, Dan Boneh, Patrick D. McDaniel

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Ensemble Adversarial Training: Attacks and Defenses.

Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Dan Boneh, Patrick D. McDaniel

CoRR, 2017

Large-Scale Evolution of Image Classifiers.

Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka I. Leon-Suematsu, Quoc V. Le, Alex Kurakin

CoRR, 2017

Large-Scale Evolution of Image Classifiers.

Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka I. Leon-Suematsu, Jie Tan, Quoc V. Le, Alexey Kurakin

Proceedings of the 34th International Conference on Machine Learning, 2017

Adversarial examples in the physical world.

Alexey Kurakin, Ian J. Goodfellow, Samy Bengio

Proceedings of the 5th International Conference on Learning Representations, 2017

Adversarial Machine Learning at Scale.

Alexey Kurakin, Ian J. Goodfellow, Samy Bengio

Proceedings of the 5th International Conference on Learning Representations, 2017

2011

Hand Gesture Recognition through On-line Skeletonization - Application of Continuous Skeleton to Real-time Shape Analysis.

Alexey Kurakin, Leonid M. Mestetskiy

Proceedings of the VISAPP 2011, 2011

Hand Geometry Analysis by Continuous Skeletons.

Leonid M. Mestetskiy, Irina Bakina, Alexey Kurakin

Proceedings of the Image Analysis and Recognition - 8th International Conference, 2011