Search Results for author: Yihan Wang

Found 13 papers, 7 papers with code

On Lp-norm Robustness of Ensemble Decision Stumps and Trees

no code implementations ICML 2020 Yihan Wang, huan zhang, Hongge Chen, Duane Boning, Cho-Jui Hsieh

In this paper, we study the robustness verification and defense with respect to general $\ell_p$ norm perturbation for ensemble trees and stumps.

Lite Pose: Efficient Architecture Design for 2D Human Pose Estimation

1 code implementation CVPR 2022 Yihan Wang, Muyang Li, Han Cai, Wei-Ming Chen, Song Han

Inspired by this finding, we design LitePose, an efficient single-branch architecture for pose estimation, and introduce two simple approaches to enhance the capacity of LitePose, including Fusion Deconv Head and Large Kernel Convs.

Multi-Person Pose Estimation

Adversarial Parameter Attack on Deep Neural Networks

no code implementations20 Mar 2022 Lijia Yu, Yihan Wang, Xiao-Shan Gao

In this paper, a new parameter perturbation attack on DNNs, called adversarial parameter attack, is proposed, in which small perturbations to the parameters of the DNN are made such that the accuracy of the attacked DNN does not decrease much, but its robustness becomes much lower.

On the Convergence of Certified Robust Training with Interval Bound Propagation

no code implementations ICLR 2022 Yihan Wang, Zhouxing Shi, Quanquan Gu, Cho-Jui Hsieh

Interval Bound Propagation (IBP) is so far the base of state-of-the-art methods for training neural networks with certifiable robustness guarantees when potential adversarial perturbations present, while the convergence of IBP training remains unknown in existing literature.

A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks

no code implementations29 Sep 2021 huan zhang, Shiqi Wang, Kaidi Xu, Yihan Wang, Suman Jana, Cho-Jui Hsieh, J Zico Kolter

In this work, we formulate an adversarial attack using a branch-and-bound (BaB) procedure on ReLU neural networks and search adversarial examples in the activation space corresponding to binary variables in a mixed integer programming (MIP) formulation.

Adversarial Attack

Interactive Plot Manipulation using Natural Language

no code implementations NAACL 2021 Yihan Wang, Yutong Shao, Ndapa Nakashole

This plotting model while accurate in most cases, still makes errors, therefore, the system allows a feedback mode, wherein the user is presented with a top-k list of plots, among which the user can pick the desired one.

Fast Certified Robust Training with Short Warmup

1 code implementation NeurIPS 2021 Zhouxing Shi, Yihan Wang, huan zhang, JinFeng Yi, Cho-Jui Hsieh

Despite that state-of-the-art (SOTA) methods including interval bound propagation (IBP) and CROWN-IBP have per-batch training complexity similar to standard neural network training, they usually use a long warmup schedule with hundreds or thousands epochs to reach SOTA performance and are thus still costly.

Adversarial Defense

DOP: Off-Policy Multi-Agent Decomposed Policy Gradients

no code implementations ICLR 2021 Yihan Wang, Beining Han, Tonghan Wang, Heng Dong, Chongjie Zhang

In this paper, we investigate causes that hinder the performance of MAPG algorithms and present a multi-agent decomposed policy gradient method (DOP).

Multi-agent Reinforcement Learning Starcraft +1

On $\ell_p$-norm Robustness of Ensemble Stumps and Trees

1 code implementation20 Aug 2020 Yihan Wang, huan zhang, Hongge Chen, Duane Boning, Cho-Jui Hsieh

In this paper, we study the problem of robustness verification and certified defense with respect to general $\ell_p$ norm perturbations for ensemble decision stumps and trees.

Off-Policy Multi-Agent Decomposed Policy Gradients

1 code implementation24 Jul 2020 Yihan Wang, Beining Han, Tonghan Wang, Heng Dong, Chongjie Zhang

In this paper, we investigate causes that hinder the performance of MAPG algorithms and present a multi-agent decomposed policy gradient method (DOP).

Multi-agent Reinforcement Learning Starcraft +1

Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond

5 code implementations NeurIPS 2020 Kaidi Xu, Zhouxing Shi, huan zhang, Yihan Wang, Kai-Wei Chang, Minlie Huang, Bhavya Kailkhura, Xue Lin, Cho-Jui Hsieh

Linear relaxation based perturbation analysis (LiRPA) for neural networks, which computes provable linear bounds of output neurons given a certain amount of input perturbation, has become a core component in robustness verification and certified defense.

Quantization

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