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Jianbo Chen

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6 papers

2 author rows

JMLR Journal 2020 Journal Article

Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data

Puyudi Yang
Jianbo Chen
Cho-Jui Hsieh
Jane-Ling Wang
Michael I. Jordan

We present a probabilistic framework for studying adversarial attacks on discrete data. Based on this framework, we derive a perturbation-based method, Greedy Attack, and a scalable learning-based method, Gumbel Attack, that illustrate various tradeoffs in the design of attacks. We demonstrate the effectiveness of these methods using both quantitative metrics and human evaluation on various state-of-the-art models for text classification, including a word-based CNN, a character-based CNN and an LSTM. As an example of our results, we show that the accuracy of character-based convolutional networks drops to the level of random selection by modifying only five characters through Greedy Attack. [abs] [ pdf ][ bib ] &copy JMLR 2020. ( edit, beta )

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AAAI Conference 2020 Conference Paper

LS-Tree: Model Interpretation When the Data Are Linguistic

Jianbo Chen
Michael Jordan

We study the problem of interpreting trained classiﬁcation models in the setting of linguistic data sets. Leveraging a parse tree, we propose to assign least-squares-based importance scores to each word of an instance by exploiting syntactic constituency structure. We establish an axiomatic characterization of these importance scores by relating them to the Banzhaf value in coalitional game theory. Based on these importance scores, we develop a principled method for detecting and quantifying interactions between words in a sentence. We demonstrate that the proposed method can aid in interpretability and diagnostics for several widely-used language models.

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AAAI Conference 2020 Conference Paper

ML-LOO: Detecting Adversarial Examples with Feature Attribution

Puyudi Yang
Jianbo Chen
Cho-Jui Hsieh
Jane-Ling Wang
Michael Jordan

Deep neural networks obtain state-of-the-art performance on a series of tasks. However, they are easily fooled by adding a small adversarial perturbation to the input. The perturbation is often imperceptible to humans on image data. We observe a signiﬁcant difference in feature attributions between adversarially crafted examples and original examples. Based on this observation, we introduce a new framework to detect adversarial examples through thresholding a scale estimate of feature attribution scores. Furthermore, we extend our method to include multi-layer feature attributions in order to tackle attacks that have mixed conﬁdence levels. As demonstrated in extensive experiments, our method achieves superior performances in distinguishing adversarial examples from popular attack methods on a variety of real data sets compared to stateof-the-art detection methods. In particular, our method is able to detect adversarial examples of mixed conﬁdence levels, and transfer between different attacking methods. We also show that our method achieves competitive performance even when the attacker has complete access to the detector.

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ICML Conference 2018 Conference Paper

Learning to Explain: An Information-Theoretic Perspective on Model Interpretation

Jianbo Chen
Le Song
Martin J. Wainwright
Michael I. Jordan

We introduce instancewise feature selection as a methodology for model interpretation. Our method is based on learning a function to extract a subset of features that are most informative for each given example. This feature selector is trained to maximize the mutual information between selected features and the response variable, where the conditional distribution of the response variable given the input is the model to be explained. We develop an efficient variational approximation to the mutual information, and show the effectiveness of our method on a variety of synthetic and real data sets using both quantitative metrics and human evaluation.

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NeurIPS Conference 2017 Conference Paper

Kernel Feature Selection via Conditional Covariance Minimization

Jianbo Chen
Mitchell Stern
Martin Wainwright
Michael Jordan

We propose a method for feature selection that employs kernel-based measures of independence to find a subset of covariates that is maximally predictive of the response. Building on past work in kernel dimension reduction, we show how to perform feature selection via a constrained optimization problem involving the trace of the conditional covariance operator. We prove various consistency results for this procedure, and also demonstrate that our method compares favorably with other state-of-the-art algorithms on a variety of synthetic and real data sets.

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NeurIPS Conference 2017 Conference Paper

Non-convex Finite-Sum Optimization Via SCSG Methods

Lihua Lei
Cheng Ju
Jianbo Chen
Michael Jordan

We develop a class of algorithms, as variants of the stochastically controlled stochastic gradient (SCSG) methods, for the smooth nonconvex finite-sum optimization problem. Only assuming the smoothness of each component, the complexity of SCSG to reach a stationary point with $E \|\nabla f(x)\|^{2}\le \epsilon$ is $O(\min\{\epsilon^{-5/3}, \epsilon^{-1}n^{2/3}\})$, which strictly outperforms the stochastic gradient descent. Moreover, SCSG is never worse than the state-of-the-art methods based on variance reduction and it significantly outperforms them when the target accuracy is low. A similar acceleration is also achieved when the functions satisfy the Polyak-Lojasiewicz condition. Empirical experiments demonstrate that SCSG outperforms stochastic gradient methods on training multi-layers neural networks in terms of both training and validation loss.

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