C
Cho-Jui Hsieh
Researcher at University of California, Los Angeles
Publications - 355
Citations - 29087
Cho-Jui Hsieh is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Robustness (computer science) & Computer science. The author has an hindex of 60, co-authored 301 publications receiving 22410 citations. Previous affiliations of Cho-Jui Hsieh include Amazon.com & University of California, Davis.
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The Limitations of Adversarial Training and the Blind-Spot Attack
TL;DR: It is shown that the effectiveness of adversarial training has a strong correlation with the distance between a test point and the manifold of training data embedded by the network, and blind-spots also exist on provable defenses including (Wong & Kolter, 2018) and (Sinha et al., 2018).
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PU Learning for Matrix Completion
TL;DR: In this article, the authors considered the PU matrix completion problem when the observations are one-bit measurements of some underlying matrix M, and in particular the observed samples consist only of ones and no zeros.
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Convergence of Adversarial Training in Overparametrized Neural Networks
TL;DR: This paper provides a partial answer to the success of adversarial training, by showing that it converges to a network where the surrogate loss with respect to the the attack algorithm is within $\epsilon$ of the optimal robust loss.
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Robust Decision Trees Against Adversarial Examples
TL;DR: The proposed algorithms can substantially improve the robustness of tree-based models against adversarial examples and present efficient implementations for classical information gain based trees as well as state-of-the-art tree boosting models such as XGBoost.
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Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise
TL;DR: It is demonstrated that the Neural SDE network can achieve better generalization than the Neural ODE and is more resistant to adversarial and non-adversarial input perturbations.