no code implementations • 10 May 2023 • Aldo Gael Carranza, Rezsa Farahani, Natalia Ponomareva, Alex Kurakin, Matthew Jagielski, Milad Nasr
We address the challenge of ensuring differential privacy (DP) guarantees in training deep retrieval systems.
1 code implementation • 1 Mar 2023 • Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H. Brendan McMahan, Sergei Vassilvitskii, Steve Chien, Abhradeep Thakurta
However, while some adoption of DP has happened in industry, attempts to apply DP to real world complex ML models are still few and far between.
5 code implementations • ICLR 2022 • David Berthelot, Rebecca Roelofs, Kihyuk Sohn, Nicholas Carlini, Alex Kurakin
We extend semi-supervised learning to the problem of domain adaptation to learn significantly higher-accuracy models that train on one data distribution and test on a different one.
Semi-supervised Domain Adaptation Unsupervised Domain Adaptation
1 code implementation • ICLR 2020 • David Berthelot, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, Colin Raffel
We improve the recently-proposed ``MixMatch semi-supervised learning algorithm by introducing two new techniques: distribution alignment and augmentation anchoring.
27 code implementations • NeurIPS 2020 • Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Han Zhang, Colin Raffel
Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model's performance.
3 code implementations • 21 Nov 2019 • David Berthelot, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, Colin Raffel
Distribution alignment encourages the marginal distribution of predictions on unlabeled data to be close to the marginal distribution of ground-truth labels.
no code implementations • 3 Sep 2019 • Matthew Jagielski, Nicholas Carlini, David Berthelot, Alex Kurakin, Nicolas Papernot
In a model extraction attack, an adversary steals a copy of a remotely deployed machine learning model, given oracle prediction access.
no code implementations • NeurIPS 2018 • Gamaleldin F. Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alex Kurakin, Ian Goodfellow, Jascha Sohl-Dickstein
Machine learning models are vulnerable to adversarial examples: small changes to images can cause computer vision models to make mistakes such as identifying a school bus as an ostrich.
2 code implementations • ICML 2017 • Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc Le, Alex Kurakin
Neural networks have proven effective at solving difficult problems but designing their architectures can be challenging, even for image classification problems alone.
Ranked #117 on Image Classification on CIFAR-10