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2020 – today
- 2024
[c30]Wang Zhang, Ziwen Martin Ma, Subhro Das, Tsui-Wei Lily Weng, Alexandre Megretski, Luca Daniel, Lam M. Nguyen:
One Step Closer to Unbiased Aleatoric Uncertainty Estimation. AAAI 2024: 16857-16864- [c29]Tsui-Wei Lily Weng:
Towards Trustworthy Deep Learning. AAAI 2024: 22682 - [c28]Avni Kothari, Bogdan Kulynych, Tsui-Wei Weng, Berk Ustun
:
Prediction without Preclusion: Recourse Verification with Reachable Sets. ICLR 2024 - [c27]Ge Yan, Yaniv Romano, Tsui-Wei Weng:
Provably Robust Conformal Prediction with Improved Efficiency. ICLR 2024 - [c26]Tuomas P. Oikarinen, Tsui-Wei Weng:
Linear Explanations for Individual Neurons. ICML 2024 - [c25]Chung-En Sun, Sicun Gao, Tsui-Wei Weng:
Breaking the Barrier: Enhanced Utility and Robustness in Smoothed DRL Agents. ICML 2024 - [c24]Tung-Yu Wu, Yu-Xiang Lin, Tsui-Wei Weng:
AND: Audio Network Dissection for Interpreting Deep Acoustic Models. ICML 2024 - [i40]Nicholas Bai, Rahul A. Iyer, Tuomas P. Oikarinen, Tsui-Wei Weng:
Describe-and-Dissect: Interpreting Neurons in Vision Networks with Language Models. CoRR abs/2403.13771 (2024) - [i39]Ge Yan, Yaniv Romano, Tsui-Wei Weng:
Provably Robust Conformal Prediction with Improved Efficiency. CoRR abs/2404.19651 (2024) - [i38]Tuomas P. Oikarinen, Tsui-Wei Weng:
Linear Explanations for Individual Neurons. CoRR abs/2405.06855 (2024) - [i37]Tung-Yu Wu, Yu-Xiang Lin, Tsui-Wei Weng:
AND: Audio Network Dissection for Interpreting Deep Acoustic Models. CoRR abs/2406.16990 (2024) - [i36]Chung-En Sun, Sicun Gao, Tsui-Wei Weng:
Breaking the Barrier: Enhanced Utility and Robustness in Smoothed DRL Agents. CoRR abs/2406.18062 (2024) - [i35]Chung-En Sun, Tuomas P. Oikarinen, Tsui-Wei Weng:
Crafting Large Language Models for Enhanced Interpretability. CoRR abs/2407.04307 (2024) - [i34]Divyansh Srivastava, Ge Yan, Tsui-Wei Weng:
VLG-CBM: Training Concept Bottleneck Models with Vision-Language Guidance. CoRR abs/2408.01432 (2024) - 2023
- [j2]Wei Huang, Chunrui Liu, Yilan Chen, Richard Yi Da Xu, Miao Zhang, Tsui-Wei Weng:
Analyzing Deep PAC-Bayesian Learning with Neural Tangent Kernel: Convergence, Analytic Generalization Bound, and Efficient Hyperparameter Selection. Trans. Mach. Learn. Res. 2023 (2023) - [c23]Divyansh Srivastava, Tuomas P. Oikarinen, Tsui-Wei Weng:
Corrupting Neuron Explanations of Deep Visual Features. ICCV 2023: 1877-1886 - [c22]Linbo Liu, Trong Nghia Hoang, Lam M. Nguyen, Tsui-Wei Weng:
Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches. ICDM 2023: 1145-1150 - [c21]Nhan H. Pham, Lam M. Nguyen, Jie Chen, Hoang Thanh Lam, Subhro Das, Tsui-Wei Weng:
Attacking c-MARL More Effectively: A Data Driven Approach. ICDM 2023: 1271-1276 - [c20]Alex Gu, Songtao Lu, Parikshit Ram, Tsui-Wei Weng:
Min-Max Multi-objective Bilevel Optimization with Applications in Robust Machine Learning. ICLR 2023 - [c19]Tuomas P. Oikarinen, Subhro Das, Lam M. Nguyen, Tsui-Wei Weng:
Label-free Concept Bottleneck Models. ICLR 2023 - [c18]Tuomas P. Oikarinen, Tsui-Wei Weng:
CLIP-Dissect: Automatic Description of Neuron Representations in Deep Vision Networks. ICLR 2023 - [c17]Wang Zhang, Tsui-Wei Weng, Subhro Das, Alexandre Megretski, Luca Daniel, Lam M. Nguyen:
ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction. ICML 2023: 41694-41714 - [i33]Alex Gu, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel:
Certified Interpretability Robustness for Class Activation Mapping. CoRR abs/2301.11324 (2023) - [i32]Wang Zhang, Tsui-Wei Weng, Subhro Das, Alexandre Megretski, Luca Daniel, Lam M. Nguyen:
ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction. CoRR abs/2302.05783 (2023) - [i31]Ligong Han, Seungwook Han, Shivchander Sudalairaj, Charlotte Loh, Rumen Dangovski, Fei Deng, Pulkit Agrawal, Dimitris N. Metaxas, Leonid Karlinsky, Tsui-Wei Weng, Akash Srivastava:
Constructive Assimilation: Boosting Contrastive Learning Performance through View Generation Strategies. CoRR abs/2304.00601 (2023) - [i30]Tuomas P. Oikarinen, Subhro Das, Lam M. Nguyen, Tsui-Wei Weng:
Label-Free Concept Bottleneck Models. CoRR abs/2304.06129 (2023) - [i29]Mohammad Ali Khan, Tuomas P. Oikarinen, Tsui-Wei Weng:
Concept-Monitor: Understanding DNN training through individual neurons. CoRR abs/2304.13346 (2023) - [i28]Avni Kothari, Bogdan Kulynych, Tsui-Wei Weng, Berk Ustun:
Prediction without Preclusion: Recourse Verification with Reachable Sets. CoRR abs/2308.12820 (2023) - [i27]Justin Lee, Tuomas P. Oikarinen, Arjun Chatha, Keng-Chi Chang, Yilan Chen, Tsui-Wei Weng:
The Importance of Prompt Tuning for Automated Neuron Explanations. CoRR abs/2310.06200 (2023) - [i26]Linbo Liu, Trong Nghia Hoang, Lam M. Nguyen, Tsui-Wei Weng:
Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches. CoRR abs/2310.07780 (2023) - [i25]Divyansh Srivastava, Tuomas P. Oikarinen, Tsui-Wei Weng:
Corrupting Neuron Explanations of Deep Visual Features. CoRR abs/2310.16332 (2023) - [i24]Wang Zhang, Ziwen Martin Ma, Subhro Das, Tsui-Wei Weng, Alexandre Megretski, Luca Daniel, Lam M. Nguyen:
One step closer to unbiased aleatoric uncertainty estimation. CoRR abs/2312.10469 (2023) - 2022
- [j1]Lam M. Nguyen, Marten van Dijk, Dzung T. Phan, Phuong Ha Nguyen, Tsui-Wei Weng, Jayant R. Kalagnanam:
Finite-sum smooth optimization with SARAH. Comput. Optim. Appl. 82(3): 561-593 (2022) - [c16]Asaf Gendler, Tsui-Wei Weng, Luca Daniel, Yaniv Romano:
Adversarially Robust Conformal Prediction. ICLR 2022 - [c15]Zhizhen Qin, Tsui-Wei Weng, Sicun Gao:
Quantifying Safety of Learning-based Self-Driving Control Using Almost-Barrier Functions. IROS 2022: 12903-12910 - [c14]Wei Liao, Ching-Yun Ko, Tsui-Wei Weng, Luca Daniel, Joel Voldman:
Facile Prediction of Neutrophil Activation State from Microscopy Images: A New Dataset and Comparative Deep Learning Approaches. ISBI 2022: 1-5 - [i23]Nhan H. Pham, Lam M. Nguyen, Jie Chen, Hoang Thanh Lam, Subhro Das, Tsui-Wei Weng:
Evaluating Robustness of Cooperative MARL: A Model-based Approach. CoRR abs/2202.03558 (2022) - [i22]Tuomas P. Oikarinen, Tsui-Wei Weng:
CLIP-Dissect: Automatic Description of Neuron Representations in Deep Vision Networks. CoRR abs/2204.10965 (2022) - [i21]Zhizhen Qin, Tsui-Wei Weng, Sicun Gao:
Quantifying Safety of Learning-based Self-Driving Control Using Almost-Barrier Functions. CoRR abs/2207.13891 (2022) - [i20]Chester Holtz, Tsui-Wei Weng, Gal Mishne:
Learning Sample Reweighting for Accuracy and Adversarial Robustness. CoRR abs/2210.11513 (2022) - 2021
- [c13]Ren Wang, Kaidi Xu, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Chuang Gan, Meng Wang:
On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning. ICLR 2021 - [c12]Yilan Chen, Wei Huang, Lam M. Nguyen, Tsui-Wei Weng:
On the Equivalence between Neural Network and Support Vector Machine. NeurIPS 2021: 23478-23490 - [c11]Tuomas P. Oikarinen, Wang Zhang, Alexandre Megretski, Luca Daniel, Tsui-Wei Weng:
Robust Deep Reinforcement Learning through Adversarial Loss. NeurIPS 2021: 26156-26167 - [i19]Akhilan Boopathy, Tsui-Wei Weng, Sijia Liu, Pin-Yu Chen, Gaoyuan Zhang, Luca Daniel:
Fast Training of Provably Robust Neural Networks by SingleProp. CoRR abs/2102.01208 (2021) - [i18]Ren Wang, Kaidi Xu, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Chuang Gan, Meng Wang:
On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning. CoRR abs/2102.10454 (2021) - [i17]Yilan Chen, Wei Huang, Lam M. Nguyen, Tsui-Wei Weng:
On the Equivalence between Neural Network and Support Vector Machine. CoRR abs/2111.06063 (2021) - 2020
- [c10]Tsui-Wei Weng, Pu Zhao, Sijia Liu, Pin-Yu Chen, Xue Lin, Luca Daniel:
Towards Certificated Model Robustness Against Weight Perturbations. AAAI 2020: 6356-6363 - [c9]Jeet Mohapatra, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel:
Towards Verifying Robustness of Neural Networks Against A Family of Semantic Perturbations. CVPR 2020: 241-249 - [c8]Tsui-Wei Weng, Krishnamurthy (Dj) Dvijotham, Jonathan Uesato, Kai Xiao, Sven Gowal, Robert Stanforth, Pushmeet Kohli:
Toward Evaluating Robustness of Deep Reinforcement Learning with Continuous Control. ICLR 2020 - [c7]Jeet Mohapatra, Ching-Yun Ko, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel:
Higher-Order Certification For Randomized Smoothing. NeurIPS 2020 - [i16]Jeet Mohapatra, Ching-Yun Ko, Tsui-Wei Weng, Sijia Liu, Pin-Yu Chen, Luca Daniel:
Rethinking Randomized Smoothing for Adversarial Robustness. CoRR abs/2003.01249 (2020) - [i15]Tuomas P. Oikarinen, Tsui-Wei Weng, Luca Daniel:
Robust Deep Reinforcement Learning through Adversarial Loss. CoRR abs/2008.01976 (2020) - [i14]Jeet Mohapatra, Ching-Yun Ko, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel:
Higher-Order Certification for Randomized Smoothing. CoRR abs/2010.06651 (2020)
2010 – 2019
- 2019
- [c6]Akhilan Boopathy, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel:
CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks. AAAI 2019: 3240-3247 - [c5]Kaidi Xu, Hongge Chen, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Mingyi Hong, Xue Lin:
Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective. IJCAI 2019: 3961-3967 - [i13]Lam M. Nguyen, Marten van Dijk, Dzung T. Phan, Phuong Ha Nguyen, Tsui-Wei Weng, Jayant R. Kalagnanam:
Optimal Finite-Sum Smooth Non-Convex Optimization with SARAH. CoRR abs/1901.07648 (2019) - [i12]Ching-Yun Ko, Zhaoyang Lyu, Tsui-Wei Weng, Luca Daniel, Ngai Wong, Dahua Lin:
POPQORN: Quantifying Robustness of Recurrent Neural Networks. CoRR abs/1905.07387 (2019) - [i11]Kaidi Xu, Hongge Chen, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Mingyi Hong, Xue Lin:
Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective. CoRR abs/1906.04214 (2019) - [i10]Yuh-Shyang Wang, Tsui-Wei Weng, Luca Daniel:
Verification of Neural Network Control Policy Under Persistent Adversarial Perturbation. CoRR abs/1908.06353 (2019) - [i9]Jeet Mohapatra, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel:
Towards Verifying Robustness of Neural Networks Against Semantic Perturbations. CoRR abs/1912.09533 (2019) - 2018
- [c4]Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen, Aurélie C. Lozano, Cho-Jui Hsieh, Luca Daniel:
On Extensions of Clever: A Neural Network Robustness Evaluation Algorithm. GlobalSIP 2018: 1159-1163 - [c3]Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, Dong Su, Yupeng Gao, Cho-Jui Hsieh, Luca Daniel:
Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach. ICLR (Poster) 2018 - [c2]Tsui-Wei Weng, Huan Zhang, Hongge Chen, Zhao Song, Cho-Jui Hsieh, Luca Daniel, Duane S. Boning, Inderjit S. Dhillon:
Towards Fast Computation of Certified Robustness for ReLU Networks. ICML 2018: 5273-5282 - [c1]Huan Zhang, Tsui-Wei Weng, Pin-Yu Chen, Cho-Jui Hsieh, Luca Daniel:
Efficient Neural Network Robustness Certification with General Activation Functions. NeurIPS 2018: 4944-4953 - [i8]Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, Dong Su, Yupeng Gao, Cho-Jui Hsieh, Luca Daniel:
Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach. CoRR abs/1801.10578 (2018) - [i7]Tsui-Wei Weng, Huan Zhang, Hongge Chen, Zhao Song, Cho-Jui Hsieh, Duane S. Boning, Inderjit S. Dhillon, Luca Daniel:
Towards Fast Computation of Certified Robustness for ReLU Networks. CoRR abs/1804.09699 (2018) - [i6]Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen, Aurélie C. Lozano, Cho-Jui Hsieh, Luca Daniel:
On Extensions of CLEVER: A Neural Network Robustness Evaluation Algorithm. CoRR abs/1810.08640 (2018) - [i5]Huan Zhang, Tsui-Wei Weng, Pin-Yu Chen, Cho-Jui Hsieh, Luca Daniel:
Efficient Neural Network Robustness Certification with General Activation Functions. CoRR abs/1811.00866 (2018) - [i4]Akhilan Boopathy, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel:
CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks. CoRR abs/1811.12395 (2018) - [i3]Tsui-Wei Weng, Pin-Yu Chen, Lam M. Nguyen, Mark S. Squillante, Ivan V. Oseledets, Luca Daniel:
PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach. CoRR abs/1812.08329 (2018) - 2016
- [i2]Zheng Zhang, Tsui-Wei Weng, Luca Daniel:
A Big-Data Approach to Handle Process Variations: Uncertainty Quantification by Tensor Recovery. CoRR abs/1603.06119 (2016) - [i1]Zheng Zhang, Tsui-Wei Weng, Luca Daniel:
A Big-Data Approach to Handle Many Process Variations: Tensor Recovery and Applications. CoRR abs/1611.02256 (2016)
Coauthor Index
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last updated on 2024-09-13 01:41 CEST by the dblp team
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