Tsui-Wei Weng

发表

Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach pdf

Jinfeng Yi, Cho-Jui Hsieh, Huan Zhang, 2018, ICLR.

Robust Deep Reinforcement Learning through Adversarial Loss

Tuomas P. Oikarinen, Tsui-Wei Weng, L. Daniel, 2020, NeurIPS.

Rethinking Randomized Smoothing for Adversarial Robustness

Pin-Yu Chen, Sijia Liu, Tsui-Wei Weng, 2020, ArXiv.

Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach

Cho-Jui Hsieh, Pin-Yu Chen, Jinfeng Yi, 2018, ICLR.

Neural Network Control Policy Verification with Persistent Adversarial Perturbations

Tsui-Wei Weng, L. Daniel, Yuh-Shyang Wang, 2020 .

Efficient Neural Network Robustness Certification with General Activation Functions

Cho-Jui Hsieh, Tsui-Wei Weng, Pin-Yu Chen, 2018, NeurIPS.

CLIP-Dissect: Automatic Description of Neuron Representations in Deep Vision Networks

Tuomas P. Oikarinen, Tsui-Wei Weng, 2022, ArXiv.

Finite-sum smooth optimization with SARAH

Marten van Dijk, Lam M. Nguyen, Phuong Ha Nguyen, 2019, Computational Optimization and Applications.

On the Equivalence between Neural Network and Support Vector Machine

Lam M. Nguyen, Wei Huang, Tsui-Wei Weng, 2021, NeurIPS.

CLIP-Dissect: Automatic Description of Neuron Representations in Deep Vision Networks

Tuomas P. Oikarinen, Tsui-Wei Weng, 2022, ArXiv.

Adversarially Robust Conformal Prediction

Yaniv Romano, Tsui-Wei Weng, L. Daniel, 2022, ICLR.

Facile Prediction of Neutrophil Activation State from Microscopy Images: A New Dataset and Comparative Deep Learning Approaches

Lucani E. Daniel, J. Voldman, Wei-Duen Liao, 2022, 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI).

On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning pdf

Chuang Gan, Kaidi Xu, Sijia Liu, 2021, ICLR.

Verification of Neural Network Control Policy Under Persistent Adversarial Perturbation pdf

Luca Daniel, Tsui-Wei Weng, Yuh-Shyang Wang, 2019, ArXiv.

Towards Fast Computation of Certified Robustness for ReLU Networks

Inderjit S. Dhillon, Cho-Jui Hsieh, Duane S. Boning, 2018, ICML.

Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective pdf

Sijia Liu, Kaidi Xu, Mingyi Hong, 2019, IJCAI.

PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach pdf

Mark S. Squillante, Lam M. Nguyen, Ivan Oseledets, 2018, ICML.

POPQORN: Quantifying Robustness of Recurrent Neural Networks

Ngai Wong, Dahua Lin, Tsui-Wei Weng, 2019, ICML.

A Big-Data Approach to Handle Many Process Variations: Tensor Recovery and Applications pdf

Tsui-Wei Weng, Luca Daniel, Zheng Zhang, 2016, ArXiv.

Stochastic simulation and robust design optimization of integrated photonic filters

Tsui-Wei Weng, Luca Daniel, Daniele Melati, 2017 .

Fast Training of Provably Robust Neural Networks by SingleProp

Sijia Liu, Gaoyuan Zhang, Tsui-Wei Weng, 2021, AAAI.

Label-Free Concept Bottleneck Models

Tuomas P. Oikarinen, Lam M. Nguyen, Tsui-Wei Weng, 2023, arXiv.org.

Towards Verifying Robustness of Neural Networks Against A Family of Semantic Perturbations

Pin-Yu Chen, Tsui-Wei Weng, Luca Daniel, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Higher-Order Certification for Randomized Smoothing pdf

Pin-Yu Chen, Sijia Liu, Tsui-Wei Weng, 2020, NeurIPS.

Concept-Monitor: Understanding DNN training through individual neurons

Tuomas P. Oikarinen, Mohammad Ali Khan, Tsui-Wei Weng, 2023, ArXiv.

CLIP-Dissect: Automatic Description of Neuron Representations in Deep Vision Networks

Tuomas P. Oikarinen, Tsui-Wei Weng, 2022, ArXiv.

Towards Certificated Model Robustness Against Weight Perturbations

Pu Zhao, Luca Daniel, Pin-Yu Chen, 2020, AAAI.

ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction

Lam M. Nguyen, Lucani E. Daniel, A. Megretski, 2023, ArXiv.

Synthesis Model and Design of a Common-Mode Bandstop Filter (CM-BSF) With an All-Pass Characteristic for High-Speed Differential Signals

Tzong-Lin Wu, Chung-Hao Tsai, Dong-Ho Han, 2014, IEEE Transactions on Microwave Theory and Techniques.

Learning Sample Reweighting for Accuracy and Adversarial Robustness

Tsui-Wei Weng, Gal Mishne, Chester Holtz, 2022, ArXiv.

ON EXTENSIONS OF CLEVER: A NEURAL NETWORK ROBUSTNESS EVALUATION ALGORITHM

Cho-Jui Hsieh, Huan Zhang, Tsui-Wei Weng, 2018, 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

M IN -M AX M ULTI - OBJECTIVE B ILEVEL O PTIMIZATION WITH A PPLICATIONS IN R OBUST M ACHINE L EARNING

Tsui-Wei Weng, P. Ram, Songtao Lu, 2023 .

Evaluating Robustness of Cooperative MARL: A Model-based Approach

Lam M. Nguyen, Hoang Thanh Lam, Nhan H. Pham, 2022, ArXiv.

CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks

Sijia Liu, Tsui-Wei Weng, Pin-Yu Chen, 2018, AAAI.

Toward Evaluating Robustness of Deep Reinforcement Learning with Continuous Control

Pushmeet Kohli, Sven Gowal, Krishnamurthy Dvijotham, 2020, ICLR.

Optimal Finite-Sum Smooth Non-Convex Optimization with SARAH pdf

Marten van Dijk, Lam M. Nguyen, Phuong Ha Nguyen, 2019, ArXiv.

Big-Data Tensor Recovery for High-Dimensional Uncertainty Quantification of Process Variations

Tsui-Wei Weng, Luca Daniel, Tsui-Wei Weng, 2017, IEEE Transactions on Components, Packaging and Manufacturing Technology.

Constructive Assimilation: Boosting Contrastive Learning Performance through View Generation Strategies

Dimitris N. Metaxas, Pulkit Agrawal, Akash Srivastava, 2023, ArXiv.

Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches

Lam M. Nguyen, Tsui-Wei Weng, Linbo Liu, 2023, 2310.07780.

R OBUST R ANDOMIZED S MOOTHING VIA T WO C OST E FFECTIVE A PPROACHES

Lam M. Nguyen, Tsui-Wei Weng, Linbo Liu, 2022 .

Prediction without Preclusion: Recourse Verification with Reachable Sets

Berk Ustun, Tsui-Wei Weng, B. Kulynych, 2023, ArXiv.

Label-Free Concept Bottleneck Models

Tuomas P. Oikarinen, Lam M. Nguyen, Tsui-Wei Weng, 2023, ICLR.

CLIP-Dissect: Automatic Description of Neuron Representations in Deep Vision Networks

Tuomas P. Oikarinen, Tsui-Wei Weng, 2022, ICLR.

Corrupting Neuron Explanations of Deep Visual Features

Tuomas P. Oikarinen, Tsui-Wei Weng, Divyansh Srivastava, 2023, ArXiv.

A big-data approach to handle process variations: Uncertainty quantification by tensor recovery

Tsui-Wei Weng, Luca Daniel, Zheng Zhang, 2016, 2016 IEEE 20th Workshop on Signal and Power Integrity (SPI).

Uncertainty quantification of silicon photonic devices with correlated and non-Gaussian random parameters.

Zhan Su, Tsui-Wei Weng, Luca Daniel, 2015, Optics express.

Fast Stochastic Simulation of Silicon Waveguide with Non-Gaussian Correlated Process Variations

Zhan Su, Tsui-Wei Weng, Luca Daniel, 2014, 2014 Asia Communications and Photonics Conference (ACP).

PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach

Lam M. Nguyen, Pin-Yu Chen, M. Squillante, 2018, ArXiv.

The Importance of Prompt Tuning for Automated Neuron Explanations

Tuomas P. Oikarinen, Tsui-Wei Weng, Keng-Chi Chang, 2023, ArXiv.