no code implementations • 4 Jul 2022 • Chuan Guo, Xinxin Xuo, Sen Wang, Li Cheng
Our approach is flexible, could be used for both text2motion and motion2text tasks.
1 code implementation • ICLR 2022 • Wei Ji, Jingjing Li, Qi Bi, Chuan Guo, Jie Liu, Li Cheng
The laborious and time-consuming manual annotation has become a real bottleneck in various practical scenarios.
no code implementations • 25 Mar 2022 • Elvis Dohmatob, Chuan Guo, Morgane Goibert
Finally, we show that if a decision-region is compact, then it admits a universal adversarial perturbation with $L_2$ norm which is $\sqrt{d}$ times smaller than the typical $L_2$ norm of a data point.
1 code implementation • 15 Mar 2022 • Kamalika Chaudhuri, Chuan Guo, Mike Rabbat
Federated data analytics is a framework for distributed data analysis where a server compiles noisy responses from a group of distributed low-bandwidth user devices to estimate aggregate statistics.
no code implementations • 25 Feb 2022 • Ruihan Wu, Jin Peng Zhou, Kilian Q. Weinberger, Chuan Guo
Label differential privacy (LDP) is a popular framework for training private ML models on datasets with public features and sensitive private labels.
1 code implementation • 28 Jan 2022 • Chuan Guo, Brian Karrer, Kamalika Chaudhuri, Laurens van der Maaten
Differential privacy is widely accepted as the de facto method for preventing data leakage in ML, and conventional wisdom suggests that it offers strong protection against privacy attacks.
no code implementations • 4 Jan 2022 • Antonio Ginart, Laurens van der Maaten, James Zou, Chuan Guo
Recent data-extraction attacks have exposed that language models can memorize some training samples verbatim.
1 code implementation • CVPR 2022 • Chuan Guo, Shihao Zou, Xinxin Zuo, Sen Wang, Wei Ji, Xingyu Li, Li Cheng
Automated generation of 3D human motions from text is a challenging problem.
1 code implementation • NeurIPS 2021 • Yiyou Sun, Chuan Guo, Yixuan Li
Out-of-distribution (OOD) detection has received much attention lately due to its practical importance in enhancing the safe deployment of neural networks.
1 code implementation • ICLR 2022 • Lauren Watson, Chuan Guo, Graham Cormode, Alex Sablayrolles
The vulnerability of machine learning models to membership inference attacks has received much attention in recent years.
no code implementations • 12 Nov 2021 • Chuan Guo, Xinxin Zuo, Sen Wang, Xinshuang Liu, Shihao Zou, Minglun Gong, Li Cheng
Action2motion stochastically generates plausible 3D pose sequences of a prescribed action category, which are processed and rendered by motion2video to form 2D videos.
no code implementations • NeurIPS 2021 • Weizhe Hua, Yichi Zhang, Chuan Guo, Zhiru Zhang, G. Edward Suh
Neural network robustness has become a central topic in machine learning in recent years.
1 code implementation • ICCV 2021 • Shihao Zou, Chuan Guo, Xinxin Zuo, Sen Wang, Pengyu Wang, Xiaoqin Hu, Shoushun Chen, Minglun Gong, Li Cheng
Event camera is an emerging imaging sensor for capturing dynamics of moving objects as events, which motivates our work in estimating 3D human pose and shape from the event signals.
no code implementations • 15 Aug 2021 • Shihao Zou, Xinxin Zuo, Sen Wang, Yiming Qian, Chuan Guo, Li Cheng
This paper focuses on a new problem of estimating human pose and shape from single polarization images.
no code implementations • NeurIPS 2021 • Ruihan Wu, Chuan Guo, Yi Su, Kilian Q. Weinberger
Machine learning models often encounter distribution shifts when deployed in the real world.
no code implementations • 5 May 2021 • Hanieh Hashemi, Yongqin Wang, Chuan Guo, Murali Annavaram
This learning setting presents, among others, two unique challenges: how to protect privacy of the clients' data during training, and how to ensure integrity of the trained model.
1 code implementation • EMNLP 2021 • Chuan Guo, Alexandre Sablayrolles, Hervé Jégou, Douwe Kiela
We propose the first general-purpose gradient-based attack against transformer models.
1 code implementation • NeurIPS 2021 • Ruihan Wu, Chuan Guo, Awni Hannun, Laurens van der Maaten
Machine-learning systems such as self-driving cars or virtual assistants are composed of a large number of machine-learning models that recognize image content, transcribe speech, analyze natural language, infer preferences, rank options, etc.
1 code implementation • 23 Feb 2021 • Awni Hannun, Chuan Guo, Laurens van der Maaten
This information leaks either through the model itself or through predictions made by the model.
1 code implementation • 9 Feb 2021 • Ruihan Wu, Chuan Guo, Felix Wu, Rahul Kidambi, Laurens van der Maaten, Kilian Q. Weinberger
We develop a novel approach for paper bidding and assignment that is much more robust against such attacks.
1 code implementation • 30 Jul 2020 • Chuan Guo, Xinxin Zuo, Sen Wang, Shihao Zou, Qingyao Sun, Annan Deng, Minglun Gong, Li Cheng
Action recognition is a relatively established task, where givenan input sequence of human motion, the goal is to predict its ac-tion category.
no code implementations • 30 Apr 2020 • Shihao Zou, Xinxin Zuo, Yiming Qian, Sen Wang, Chuan Guo, Chi Xu, Minglun Gong, Li Cheng
Polarization images are known to be able to capture polarized reflected lights that preserve rich geometric cues of an object, which has motivated its recent applications in reconstructing detailed surface normal of the objects of interest.
no code implementations • 24 Feb 2020 • Chuan Guo, Ruihan Wu, Kilian Q. Weinberger
Modern neural networks often contain significantly more parameters than the size of their training data.
no code implementations • 9 Jan 2020 • Chuan Guo, Awni Hannun, Brian Knott, Laurens van der Maaten, Mark Tygert, Ruiyu Zhu
Secure multiparty computations enable the distribution of so-called shares of sensitive data to multiple parties such that the multiple parties can effectively process the data while being unable to glean much information about the data (at least not without collusion among all parties to put back together all the shares).
no code implementations • ICLR 2020 • Chuan Guo, Ruihan Wu, Kilian Q. Weinberger
The complexity of large-scale neural networks can lead to poor understanding of their internal details.
no code implementations • NeurIPS 2019 • Chuan Guo, Ali Mousavi, Xiang Wu, Daniel N. Holtmann-Rice, Satyen Kale, Sashank Reddi, Sanjiv Kumar
In extreme classification settings, embedding-based neural network models are currently not competitive with sparse linear and tree-based methods in terms of accuracy.
1 code implementation • ICML 2020 • Chuan Guo, Tom Goldstein, Awni Hannun, Laurens van der Maaten
Good data stewardship requires removal of data at the request of the data's owner.
1 code implementation • NeurIPS 2019 • Tao Yu, Shengyuan Hu, Chuan Guo, Wei-Lun Chao, Kilian Q. Weinberger
Natural images are virtually surrounded by low-density misclassified regions that can be efficiently discovered by gradient-guided search --- enabling the generation of adversarial images.
3 code implementations • ICLR 2019 • Chuan Guo, Jacob R. Gardner, Yurong You, Andrew Gordon Wilson, Kilian Q. Weinberger
We propose an intriguingly simple method for the construction of adversarial images in the black-box setting.
1 code implementation • 24 Sep 2018 • Chuan Guo, Jared S. Frank, Kilian Q. Weinberger
In this paper we propose to restrict the search for adversarial images to a low frequency domain.
4 code implementations • ICLR 2018 • Qiantong Xu, Gao Huang, Yang Yuan, Chuan Guo, Yu Sun, Felix Wu, Kilian Weinberger
Evaluating generative adversarial networks (GANs) is inherently challenging.
1 code implementation • ICLR 2018 • Chuan Guo, Mayank Rana, Moustapha Cisse, Laurens van der Maaten
This paper investigates strategies that defend against adversarial-example attacks on image-classification systems by transforming the inputs before feeding them to the system.
19 code implementations • ICML 2017 • Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger
Confidence calibration -- the problem of predicting probability estimates representative of the true correctness likelihood -- is important for classification models in many applications.
1 code implementation • NeurIPS 2016 • Gao Huang, Chuan Guo, Matt J. Kusner, Yu Sun, Fei Sha, Kilian Q. Weinberger
Accurately measuring the similarity between text documents lies at the core of many real world applications of machine learning.