Tsui-Wei Weng

CoRR, 2023

Constructive Assimilation: Boosting Contrastive Learning Performance through View Generation Strategies.

[BibT_eX]

[DOI]

Ligong Han

Seungwook Han

Shivchander Sudalairaj

CoRR, 2023

Certified Interpretability Robustness for Class Activation Mapping.

[BibT_eX]

[DOI]

CoRR, 2023

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

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

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

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Label-free Concept Bottleneck Models.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Min-Max Multi-objective Bilevel Optimization with Applications in Robust Machine Learning.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Attacking c-MARL More Effectively: A Data Driven Approach.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Data Mining, 2023

Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Data Mining, 2023

Corrupting Neuron Explanations of Deep Visual Features.

[BibT_eX]

[DOI]

Divyansh Srivastava

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022

Learning Sample Reweighting for Accuracy and Adversarial Robustness.

[BibT_eX]

[DOI]

Chester Holtz

Gal Mishne

CoRR, 2022

Evaluating Robustness of Cooperative MARL: A Model-based Approach.

[BibT_eX]

[DOI]

CoRR, 2022

Finite-sum smooth optimization with SARAH.

[BibT_eX]

[DOI]

Comput. Optim. Appl., 2022

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

[BibT_eX]

[DOI]

Proceedings of the 19th IEEE International Symposium on Biomedical Imaging, 2022

Quantifying Safety of Learning-based Self-Driving Control Using Almost-Barrier Functions.

[BibT_eX]

[DOI]

Zhizhen Qin

Sicun Gao

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022

Adversarially Robust Conformal Prediction.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Fast Training of Provably Robust Neural Networks by SingleProp.

[BibT_eX]

[DOI]

CoRR, 2021

Robust Deep Reinforcement Learning through Adversarial Loss.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

On the Equivalence between Neural Network and Support Vector Machine.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

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

[BibT_eX]

[DOI]

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Robust Deep Reinforcement Learning through Adversarial Loss.

[BibT_eX]

[DOI]

CoRR, 2020

Rethinking Randomized Smoothing for Adversarial Robustness.

[BibT_eX]

[DOI]

CoRR, 2020

Higher-Order Certification For Randomized Smoothing.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Toward Evaluating Robustness of Deep Reinforcement Learning with Continuous Control.

[BibT_eX]

[DOI]

Krishnamurthy (Dj) Dvijotham

Proceedings of the 8th International Conference on Learning Representations, 2020

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

[BibT_eX]

[DOI]

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Towards Certificated Model Robustness Against Weight Perturbations.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Towards Verifying Robustness of Neural Networks Against Semantic Perturbations.

[BibT_eX]

[DOI]

CoRR, 2019

Verification of Neural Network Control Policy Under Persistent Adversarial Perturbation.

[BibT_eX]

[DOI]

Yuh-Shyang Wang

CoRR, 2019

POPQORN: Quantifying Robustness of Recurrent Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2019

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

[BibT_eX]

[DOI]

CoRR, 2019

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

[BibT_eX]

[DOI]

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

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

[BibT_eX]

[DOI]

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

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

[BibT_eX]

[DOI]

CoRR, 2018

Efficient Neural Network Robustness Certification with General Activation Functions.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Towards Fast Computation of Certified Robustness for ReLU Networks.

[BibT_eX]

[DOI]

Proceedings of the 35th International Conference on Machine Learning, 2018

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

[BibT_eX]

[DOI]

Proceedings of the 6th International Conference on Learning Representations, 2018

On Extensions of Clever: A Neural Network Robustness Evaluation Algorithm.

[BibT_eX]

[DOI]

Proceedings of the 2018 IEEE Global Conference on Signal and Information Processing, 2018

2016

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

[BibT_eX]

[DOI]

Zheng Zhang

CoRR, 2016

A Big-Data Approach to Handle Process Variations: Uncertainty Quantification by Tensor Recovery.

[BibT_eX]

[DOI]

Zheng Zhang