Rui Shu

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NC State University ∙ Stanford University

Omni: Automated Ensemble with Unexpected Models against Adversarial Evasion Attack

BACKGROUND: Machine learning-based security detection models have become...
11/23/2020 ∙ by Rui Shu, et al. ∙ 0 ∙ share

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Predicting Project Health for Open Source Projects (using the DECART Hyperparameter Optimizer)

Software developed on public platforms are a source of data that can be ...
06/12/2020 ∙ by Tianpei Xia, et al. ∙ 0 ∙ share

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Predictive Coding for Locally-Linear Control

High-dimensional observations and unknown dynamics are major challenges ...
03/02/2020 ∙ by Rui Shu, et al. ∙ 5 ∙ share

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Sequential Model Optimization for Software Process Control

Many methods have been proposed to estimate how much effort is required ...
12/09/2019 ∙ by Tianpei Xia, et al. ∙ 0 ∙ share

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Improved Recognition of Security Bugs via Dual Hyperparameter Optimization

Background: Security bugs need to be handled by small groups of engineer...
11/04/2019 ∙ by Rui Shu, et al. ∙ 0 ∙ share

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Fair Generative Modeling via Weak Supervision

Real-world datasets are often biased with respect to key demographic fac...
10/26/2019 ∙ by Aditya Grover, et al. ∙ 25 ∙ share

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Weakly Supervised Disentanglement with Guarantees

Learning disentangled representations that correspond to factors of vari...
10/22/2019 ∙ by Rui Shu, et al. ∙ 12 ∙ share

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Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control

Many real-world sequential decision-making problems can be formulated as...
09/04/2019 ∙ by Nir Levine, et al. ∙ 25 ∙ share

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AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows

Given unpaired data from multiple domains, a key challenge is to efficie...
05/30/2019 ∙ by Aditya Grover, et al. ∙ 6 ∙ share

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Better Security Bug Report Classification via Hyperparameter Optimization

When security bugs are detected, they should be (a) discussed privately ...
05/16/2019 ∙ by Rui Shu, et al. ∙ 0 ∙ share

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Training Variational Autoencoders with Buffered Stochastic Variational Inference

The recognition network in deep latent variable models such as variation...
02/27/2019 ∙ by Rui Shu, et al. ∙ 18 ∙ share

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Amortized Inference Regularization

The variational autoencoder (VAE) is a popular model for density estimat...
05/23/2018 ∙ by Rui Shu, et al. ∙ 12 ∙ share

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Generative Adversarial Examples

Adversarial examples are typically constructed by perturbing an existing...
05/21/2018 ∙ by Yang Song, et al. ∙ 8 ∙ share

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A DIRT-T Approach to Unsupervised Domain Adaptation

Domain adaptation refers to the problem of leveraging labeled data in a ...
02/23/2018 ∙ by Rui Shu, et al. ∙ 0 ∙ share

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Automated Attribution and Intertextual Analysis

In this work, we employ quantitative methods from the realm of statistic...
05/03/2014 ∙ by James Brofos, et al. ∙ 0 ∙ share

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Rui Shu

Featured Co-authors

Stefano Ermon

Mykel J. Kochenderfer

Ang Li

Tim Menzies

Mohammad Ghavamzadeh

Yang Song

Abhishek Kumar

Yinlam Chow

Ben Poole

Aditya Grover

Shengjia Zhao

Omni: Automated Ensemble with Unexpected Models against Adversarial Evasion Attack

Predicting Project Health for Open Source Projects (using the DECART Hyperparameter Optimizer)

Predictive Coding for Locally-Linear Control

Sequential Model Optimization for Software Process Control

Improved Recognition of Security Bugs via Dual Hyperparameter Optimization

Fair Generative Modeling via Weak Supervision

Weakly Supervised Disentanglement with Guarantees

Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control

AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows

Better Security Bug Report Classification via Hyperparameter Optimization

Training Variational Autoencoders with Buffered Stochastic Variational Inference

Amortized Inference Regularization

Generative Adversarial Examples

A DIRT-T Approach to Unsupervised Domain Adaptation

Automated Attribution and Intertextual Analysis