Search Results for author: Zifan Wang

Found 17 papers, 7 papers with code

Improving Robust Generalization by Direct PAC-Bayesian Bound Minimization

no code implementations22 Nov 2022 Zifan Wang, Nan Ding, Tomer Levinboim, Xi Chen, Radu Soricut

Recent research in robust optimization has shown an overfitting-like phenomenon in which models trained against adversarial attacks exhibit higher robustness on the training set compared to the test set.

Adversarial Robustness

A Zeroth-Order Momentum Method for Risk-Averse Online Convex Games

no code implementations6 Sep 2022 Zifan Wang, Yi Shen, Zachary I. Bell, Scott Nivison, Michael M. Zavlanos, Karl H. Johansson

Specifically, the agents use the conditional value at risk (CVaR) as a risk measure and rely on bandit feedback in the form of the cost values of the selected actions at every episode to estimate their CVaR values and update their actions.

On the Perils of Cascading Robust Classifiers

1 code implementation1 Jun 2022 Ravi Mangal, Zifan Wang, Chi Zhang, Klas Leino, Corina Pasareanu, Matt Fredrikson

We present \emph{cascade attack} (CasA), an adversarial attack against cascading ensembles, and show that: (1) there exists an adversarial input for up to 88\% of the samples where the ensemble claims to be certifiably robust and accurate; and (2) the accuracy of a cascading ensemble under our attack is as low as 11\% when it claims to be certifiably robust and accurate on 97\% of the test set.

Adversarial Attack

Faithful Explanations for Deep Graph Models

no code implementations24 May 2022 Zifan Wang, Yuhang Yao, Chaoran Zhang, Han Zhang, Youjie Kang, Carlee Joe-Wong, Matt Fredrikson, Anupam Datta

Second, our analytical and empirical results demonstrate that feature attribution methods cannot capture the nonlinear effect of edge features, while existing subgraph explanation methods are not faithful.

Anomaly Detection

Risk-Averse No-Regret Learning in Online Convex Games

no code implementations16 Mar 2022 Zifan Wang, Yi Shen, Michael M. Zavlanos

To address this challenge, we propose a new online risk-averse learning algorithm that relies on one-point zeroth-order estimation of the CVaR gradients computed using CVaR values that are estimated by appropriately sampling the cost functions.

On Optimizing Shared-ride Mobility Services with Walking Legs

no code implementations29 Jan 2022 Zifan Wang, Michael F Hyland, Younghun Bahk, Navjyoth JS Sarma

Shared-ride mobility services that incorporate traveler walking legs aim to reduce vehicle-kilometers-travelled (VKT), vehicle-hours-travelled (VHT), request rejections, fleet size, or some combination of these factors, compared to door-to-door (D2D) shared-ride services.

Context-Aware Compilation of DNN Training Pipelines across Edge and Cloud

1 code implementation Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2021 Dixi Yao, Liyao Xiang, Zifan Wang, Jiayu Xu, Chao Li, Xinbing Wang

Experimental results show that our system not only adapts well to, but also draws on the varying contexts, delivering a practical and efficient solution to edge-cloud model training.

Ranked #2 on Recommendation Systems on MovieLens 1M (Precision metric)

Image Classification Image Generation +4

Consistent Counterfactuals for Deep Models

no code implementations ICLR 2022 Emily Black, Zifan Wang, Matt Fredrikson, Anupam Datta

Counterfactual examples are one of the most commonly-cited methods for explaining the predictions of machine learning models in key areas such as finance and medical diagnosis.

Medical Diagnosis

Robust Models Are More Interpretable Because Attributions Look Normal

1 code implementation20 Mar 2021 Zifan Wang, Matt Fredrikson, Anupam Datta

Recent work has found that adversarially-robust deep networks used for image classification are more interpretable: their feature attributions tend to be sharper, and are more concentrated on the objects associated with the image's ground-truth class.

Image Classification

Globally-Robust Neural Networks

1 code implementation16 Feb 2021 Klas Leino, Zifan Wang, Matt Fredrikson

We show that widely-used architectures can be easily adapted to this objective by incorporating efficient global Lipschitz bounds into the network, yielding certifiably-robust models by construction that achieve state-of-the-art verifiable accuracy.

Influence Patterns for Explaining Information Flow in BERT

no code implementations NeurIPS 2021 Kaiji Lu, Zifan Wang, Piotr Mardziel, Anupam Datta

While attention is all you need may be proving true, we do not know why: attention-based transformer models such as BERT are superior but how information flows from input tokens to output predictions are unclear.

ABSTRACTING INFLUENCE PATHS FOR EXPLAINING (CONTEXTUALIZATION OF) BERT MODELS

no code implementations28 Sep 2020 Kaiji Lu, Zifan Wang, Piotr Mardziel, Anupam Datta

While “attention is all you need” may be proving true, we do not yet know why: attention-based transformer models such as BERT are superior but how they contextualize information even for simple grammatical rules such as subject-verb number agreement(SVA) is uncertain.

Reconstructing Actions To Explain Deep Reinforcement Learning

no code implementations17 Sep 2020 Xuan Chen, Zifan Wang, Yucai Fan, Bonan Jin, Piotr Mardziel, Carlee Joe-Wong, Anupam Datta

Feature attribution has been a foundational building block for explaining the input feature importance in supervised learning with Deep Neural Network (DNNs), but face new challenges when applied to deep Reinforcement Learning (RL). We propose a new approach to explaining deep RL actions by defining a class of \emph{action reconstruction} functions that mimic the behavior of a network in deep RL.

Atari Games Feature Importance +1

Smoothed Geometry for Robust Attribution

1 code implementation NeurIPS 2020 Zifan Wang, Haofan Wang, Shakul Ramkumar, Matt Fredrikson, Piotr Mardziel, Anupam Datta

Feature attributions are a popular tool for explaining the behavior of Deep Neural Networks (DNNs), but have recently been shown to be vulnerable to attacks that produce divergent explanations for nearby inputs.

Towards Frequency-Based Explanation for Robust CNN

1 code implementation6 May 2020 Zifan Wang, Yilin Yang, Ankit Shrivastava, Varun Rawal, Zihao Ding

We show that the vulnerability of the model against tiny distortions is a result of the model is relying on the high-frequency features, the target features of the adversarial (black and white-box) attackers, to make the prediction.

Association

Interpreting Interpretations: Organizing Attribution Methods by Criteria

no code implementations19 Feb 2020 Zifan Wang, Piotr Mardziel, Anupam Datta, Matt Fredrikson

In this work we expand the foundationsof human-understandable concepts with which attributionscan be interpreted beyond "importance" and its visualization; we incorporate the logical concepts of necessity andsufficiency, and the concept of proportionality.

Image Classification

Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks

8 code implementations3 Oct 2019 Haofan Wang, Zifan Wang, Mengnan Du, Fan Yang, Zijian Zhang, Sirui Ding, Piotr Mardziel, Xia Hu

Recently, increasing attention has been drawn to the internal mechanisms of convolutional neural networks, and the reason why the network makes specific decisions.

Adversarial Attack Decision Making +1

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