Hand Geometry Analysis by Continuous Skeletons. - Leonid M. Mestetskiy; Irina Bakina; Alexey Kurakin (ICIAR (2), 2011)

Hand Gesture Recognition through On-line Skeletonization - Application of Continuous Skeleton to Real-time Shape Analysis. - Alexey Kurakin; Leonid M. Mestetskiy (VISAPP, 2011)

Adversarial Machine Learning at Scale. - Alexey Kurakin; Ian J. Goodfellow; Samy Bengio (ICLR (Poster), 2017)

Adversarial examples in the physical world. - Alexey Kurakin; Ian J. Goodfellow; Samy Bengio (ICLR (Workshop), 2017)

Large-Scale Evolution of Image Classifiers. - Esteban Real; Sherry Moore; Andrew Selle; Saurabh Saxena; Yutaka I. Leon-Suematsu; Jie Tan 001; Quoc V. Le; Alexey Kurakin (ICML, 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)

Ensemble Adversarial Training: Attacks and Defenses. - Florian Tramèr; Alexey Kurakin; Nicolas Papernot; Dan Boneh; Patrick D. McDaniel (CoRR, 2017)

Ensemble Adversarial Training: Attacks and Defenses. - Florian Tramèr; Alexey Kurakin; Nicolas Papernot; Ian J. Goodfellow; Dan Boneh; Patrick D. McDaniel (ICLR (Poster), 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 (NeurIPS, 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 Logit Pairing. - Harini Kannan; Alexey Kurakin; Ian J. Goodfellow (CoRR, 2018)

Adversarial Attacks and Defences Competition. - Alexey Kurakin; Ian J. Goodfellow; Samy Bengio; Yinpeng Dong; Fangzhou Liao; Ming Liang; Tianyu Pang; Jun Zhu 001; Xiaolin Hu 001; Cihang Xie; Jianyu Wang 001; Zhishuai Zhang; et al. (CoRR, 2018)

Adversarial Vision Challenge. - Wieland Brendel; Jonas Rauber; Alexey Kurakin; Nicolas Papernot; Behar Veliqi; Marcel Salathé; Sharada P. Mohanty; Matthias Bethge (CoRR, 2018)

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)

High-Fidelity Extraction of Neural Network Models. - Matthew Jagielski; Nicholas Carlini; David Berthelot; Alex Kurakin; Nicolas Papernot (CoRR, 2019)

ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring. - David Berthelot; Nicholas Carlini; Ekin D. Cubuk; Alex Kurakin; Kihyuk Sohn; Han Zhang 010; Colin Raffel (CoRR, 2019)

ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring. - David Berthelot; Nicholas Carlini; Ekin D. Cubuk; Alex Kurakin; Kihyuk Sohn; Han Zhang 010; Colin Raffel (ICLR, 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 (NeurIPS, 2020)

FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence. - Kihyuk Sohn; David Berthelot; Nicholas Carlini; Zizhao Zhang; Han Zhang 010; Colin Raffel; Ekin Dogus Cubuk; Alexey Kurakin; Chun-Liang Li (NeurIPS, 2020)

High Accuracy and High Fidelity Extraction of Neural Networks. - Matthew Jagielski; Nicholas Carlini; David Berthelot; Alex Kurakin; Nicolas Papernot (USENIX Security Symposium, 2020)

AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation. - David Berthelot; Rebecca Roelofs; Kihyuk Sohn; Nicholas Carlini; Alexey Kurakin (ICLR, 2022)

Handcrafted Backdoors in Deep Neural Networks. - Sanghyun Hong 001; Nicholas Carlini; Alexey Kurakin (NeurIPS, 2022)

Toward Training at ImageNet Scale with Differential Privacy. - Alexey Kurakin; Steve Chien; Shuang Song 001; Roxana Geambasu; Andreas Terzis; Abhradeep Thakurta (CoRR, 2022)

Large Scale Transfer Learning for Differentially Private Image Classification. - Harsh Mehta; Abhradeep Thakurta; Alexey Kurakin; Ashok Cutkosky (CoRR, 2022)

How to DP-fy ML: A Practical Tutorial to Machine Learning with Differential Privacy. - Natalia Ponomareva 001; Sergei Vassilvitskii; Zheng Xu 002; Brendan McMahan; Alexey Kurakin; Chiyaun Zhang (KDD, 2023)

RETVec: Resilient and Efficient Text Vectorizer. - Elie Bursztein; Marina Zhang; Owen Vallis; Xinyu Jia; Alexey Kurakin (NeurIPS, 2023)

Publishing Efficient On-device Models Increases Adversarial Vulnerability. - Sanghyun Hong 001; Nicholas Carlini; Alexey Kurakin (SaTML, 2023)

Privacy-Preserving Recommender Systems with Synthetic Query Generation using Differentially Private Large Language Models. - Aldo Gael Carranza; Rezsa Farahani; Natalia Ponomareva 001; Alex Kurakin; Matthew Jagielski; Milad Nasr (CoRR, 2023)

Harnessing large-language models to generate private synthetic text. - Alexey Kurakin; Natalia Ponomareva 001; Umar Syed; Liam MacDermed; Andreas Terzis (CoRR, 2023)

How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy. - Natalia Ponomareva 001; Hussein Hazimeh 001; Alex Kurakin; Zheng Xu 002; Carson Denison; H. Brendan McMahan; Sergei Vassilvitskii; Steve Chien; Abhradeep Guha Thakurta (J. Artif. Intell. Res., 2023)

Differentially Private Image Classification from Features. - Harsh Mehta; Walid Krichene; Abhradeep Guha Thakurta; Alexey Kurakin; Ashok Cutkosky (Trans. Mach. Learn. Res., 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)

Private prediction for large-scale synthetic text generation. - Kareem Amin 002; Alex Bie; Weiwei Kong; Alexey Kurakin; Natalia Ponomareva 001; Umar Syed; Andreas Terzis; Sergei Vassilvitskii (EMNLP (Findings), 2024)

Synthetic Query Generation for Privacy-Preserving Deep Retrieval Systems using Differentially Private Language Models. - Aldo G. Carranza; Rezsa Farahani; Natalia Ponomareva 001; Alexey Kurakin; Matthew Jagielski; Milad Nasr (NAACL-HLT, 2024)

Diffusion Denoising as a Certified Defense against Clean-label Poisoning. - Sanghyun Hong 001; Nicholas Carlini; Alexey Kurakin (CoRR, 2024)

DART: A Principled Approach to Adversarially Robust Unsupervised Domain Adaptation. - Yunjuan Wang; Hussein Hazimeh 001; Natalia Ponomareva 001; Alexey Kurakin; Ibrahim Hammoud; Raman Arora (SaTML, 2025)