Search Results for author: Chuan Guo

Found 53 papers, 32 papers with code

Guarantees of confidentiality via Hammersley-Chapman-Robbins bounds

1 code implementation3 Apr 2024 Kamalika Chaudhuri, Chuan Guo, Laurens van der Maaten, Saeed Mahloujifar, Mark Tygert

The HCR bounds appear to be insufficient on their own to guarantee confidentiality of the inputs to inference with standard deep neural nets, "ResNet-18" and "Swin-T," pre-trained on the data set, "ImageNet-1000," which contains 1000 classes.

Image Classification

DP-RDM: Adapting Diffusion Models to Private Domains Without Fine-Tuning

no code implementations21 Mar 2024 Jonathan Lebensold, Maziar Sanjabi, Pietro Astolfi, Adriana Romero-Soriano, Kamalika Chaudhuri, Mike Rabbat, Chuan Guo

Text-to-image diffusion models have been shown to suffer from sample-level memorization, possibly reproducing near-perfect replica of images that they are trained on, which may be undesirable.

Memorization Retrieval

Privacy Amplification for the Gaussian Mechanism via Bounded Support

no code implementations7 Mar 2024 Shengyuan Hu, Saeed Mahloujifar, Virginia Smith, Kamalika Chaudhuri, Chuan Guo

Data-dependent privacy accounting frameworks such as per-instance differential privacy (pDP) and Fisher information loss (FIL) confer fine-grained privacy guarantees for individuals in a fixed training dataset.

Differentially Private Representation Learning via Image Captioning

no code implementations4 Mar 2024 Tom Sander, Yaodong Yu, Maziar Sanjabi, Alain Durmus, Yi Ma, Kamalika Chaudhuri, Chuan Guo

In this work, we show that effective DP representation learning can be done via image captioning and scaling up to internet-scale multimodal datasets.

Image Captioning Representation Learning

Déjà Vu Memorization in Vision-Language Models

no code implementations3 Feb 2024 Bargav Jayaraman, Chuan Guo, Kamalika Chaudhuri

Vision-Language Models (VLMs) have emerged as the state-of-the-art representation learning solution, with myriads of downstream applications such as image classification, retrieval and generation.

Image Classification Memorization +2

Generative Human Motion Stylization in Latent Space

no code implementations24 Jan 2024 Chuan Guo, Yuxuan Mu, Xinxin Zuo, Peng Dai, Youliang Yan, Juwei Lu, Li Cheng

Building upon this, we present a novel generative model that produces diverse stylization results of a single motion (latent) code.

MotionMix: Weakly-Supervised Diffusion for Controllable Motion Generation

1 code implementation20 Jan 2024 Nhat M. Hoang, Kehong Gong, Chuan Guo, Michael Bi Mi

Specifically, we separate the denoising objectives of a diffusion model into two stages: obtaining conditional rough motion approximations in the initial $T-T^*$ steps by learning the noisy annotated motions, followed by the unconditional refinement of these preliminary motions during the last $T^*$ steps using unannotated motions.


MoMask: Generative Masked Modeling of 3D Human Motions

1 code implementation29 Nov 2023 Chuan Guo, Yuxuan Mu, Muhammad Gohar Javed, Sen Wang, Li Cheng

For the base-layer motion tokens, a Masked Transformer is designated to predict randomly masked motion tokens conditioned on text input at training stage.

Human motion prediction Motion Forecasting +2

Large-Scale Public Data Improves Differentially Private Image Generation Quality

no code implementations4 Aug 2023 Ruihan Wu, Chuan Guo, Kamalika Chaudhuri

In this work, we look at how to use generic large-scale public data to improve the quality of differentially private image generation in Generative Adversarial Networks (GANs), and provide an improved method that uses public data effectively.

Image Generation

ViP: A Differentially Private Foundation Model for Computer Vision

1 code implementation15 Jun 2023 Yaodong Yu, Maziar Sanjabi, Yi Ma, Kamalika Chaudhuri, Chuan Guo

In this work, we propose as a mitigation measure a recipe to train foundation vision models with differential privacy (DP) guarantee.

"Private Prediction Strikes Back!'' Private Kernelized Nearest Neighbors with Individual Renyi Filter

1 code implementation12 Jun 2023 Yuqing Zhu, Xuandong Zhao, Chuan Guo, Yu-Xiang Wang

Most existing approaches of differentially private (DP) machine learning focus on private training.

Do SSL Models Have Déjà Vu? A Case of Unintended Memorization in Self-supervised Learning

1 code implementation NeurIPS 2023 Casey Meehan, Florian Bordes, Pascal Vincent, Kamalika Chaudhuri, Chuan Guo

Self-supervised learning (SSL) algorithms can produce useful image representations by learning to associate different parts of natural images with one another.

Memorization Self-Supervised Learning

Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design

1 code implementation8 Nov 2022 Chuan Guo, Kamalika Chaudhuri, Pierre Stock, Mike Rabbat

In private federated learning (FL), a server aggregates differentially private updates from a large number of clients in order to train a machine learning model.

Federated Learning

Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano

no code implementations24 Oct 2022 Chuan Guo, Alexandre Sablayrolles, Maziar Sanjabi

Differential privacy (DP) is by far the most widely accepted framework for mitigating privacy risks in machine learning.

Learning to Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning

1 code implementation19 Oct 2022 Ruihan Wu, Xiangyu Chen, Chuan Guo, Kilian Q. Weinberger

Gradient inversion attack enables recovery of training samples from model gradients in federated learning (FL), and constitutes a serious threat to data privacy.

Federated Learning

Measuring and Controlling Split Layer Privacy Leakage Using Fisher Information

no code implementations21 Sep 2022 Kiwan Maeng, Chuan Guo, Sanjay Kariyappa, Edward Suh

Split learning and inference propose to run training/inference of a large model that is split across client devices and the cloud.

Origins of Low-dimensional Adversarial Perturbations

no code implementations25 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.

Privacy-Aware Compression for Federated Data Analysis

1 code implementation15 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.

Federated Learning

Does Label Differential Privacy Prevent Label Inference Attacks?

1 code implementation25 Feb 2022 Ruihan Wu, Jin Peng Zhou, Kilian Q. Weinberger, Chuan Guo

Label differential privacy (label-DP) is a popular framework for training private ML models on datasets with public features and sensitive private labels.

Bounding Training Data Reconstruction in Private (Deep) Learning

1 code implementation28 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.

Submix: Practical Private Prediction for Large-Scale Language Models

no code implementations4 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.

Language Modelling

ReAct: Out-of-distribution Detection With Rectified Activations

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.

Out-of-Distribution Detection

On the Importance of Difficulty Calibration in Membership Inference Attacks

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.

Action2video: Generating Videos of Human 3D Actions

no code implementations12 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.

EventHPE: Event-based 3D Human Pose and Shape Estimation

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.

3D human pose and shape estimation Optical Flow Estimation

Human Pose and Shape Estimation from Single Polarization Images

1 code implementation15 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.

Surface Normal Estimation

Online Adaptation to Label Distribution Shift

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.

Byzantine-Robust and Privacy-Preserving Framework for FedML

no code implementations5 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.

Federated Learning Privacy Preserving

Fixes That Fail: Self-Defeating Improvements in Machine-Learning Systems

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.

BIG-bench Machine Learning Object Detection +1

Measuring Data Leakage in Machine-Learning Models with Fisher Information

1 code implementation23 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.

BIG-bench Machine Learning

Making Paper Reviewing Robust to Bid Manipulation Attacks

1 code implementation9 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.

Action2Motion: Conditioned Generation of 3D Human Motions

1 code implementation30 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.

Action Generation

Polarization Human Shape and Pose Dataset

no code implementations30 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.

On Hiding Neural Networks Inside Neural Networks

no code implementations24 Feb 2020 Chuan Guo, Ruihan Wu, Kilian Q. Weinberger

Modern neural networks often contain significantly more parameters than the size of their training data.

BIG-bench Machine Learning

Secure multiparty computations in floating-point arithmetic

no code implementations9 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).

Mathematical Proofs Privacy Preserving +1

Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces

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.

Attribute Classification +2

A New Defense Against Adversarial Images: Turning a Weakness into a Strength

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.

Adversarial Defense

Simple Black-box Adversarial Attacks

4 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.

Low Frequency Adversarial Perturbation

1 code implementation24 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.

Denoising Speech Recognition

Countering Adversarial Images using Input Transformations

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.

Adversarial Defense General Classification +1

On Calibration of Modern Neural Networks

17 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.

Document Classification General Classification

Supervised Word Mover's Distance

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.

Document Classification General Classification +2

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