A Big-Data Approach to Handle Process Variations: Uncertainty Quantification by Tensor Recovery. - Zheng Zhang 005; Tsui-Wei Weng; Luca Daniel (CoRR, 2016)

A Big-Data Approach to Handle Many Process Variations: Tensor Recovery and Applications. - Zheng Zhang 005; Tsui-Wei Weng; Luca Daniel (CoRR, 2016)

On Extensions of Clever: A Neural Network Robustness Evaluation Algorithm. - Tsui-Wei Weng; Huan Zhang 001; Pin-Yu Chen; Aurélie C. Lozano; Cho-Jui Hsieh; Luca Daniel (GlobalSIP, 2018)

Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach. - Tsui-Wei Weng; Huan Zhang 001; Pin-Yu Chen; Jinfeng Yi; Dong Su; Yupeng Gao; Cho-Jui Hsieh; Luca Daniel (ICLR (Poster), 2018)

Towards Fast Computation of Certified Robustness for ReLU Networks. - Tsui-Wei Weng; Huan Zhang 001; Hongge Chen; Zhao Song 002; Cho-Jui Hsieh; Luca Daniel; Duane S. Boning; Inderjit S. Dhillon (ICML, 2018)

Efficient Neural Network Robustness Certification with General Activation Functions. - Huan Zhang 001; Tsui-Wei Weng; Pin-Yu Chen; Cho-Jui Hsieh; Luca Daniel (NeurIPS, 2018)

PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach. - Tsui-Wei Weng; Pin-Yu Chen; Lam M. Nguyen; Mark S. Squillante; Ivan V. Oseledets; Luca Daniel (CoRR, 2018)

CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks. - Akhilan Boopathy; Tsui-Wei Weng; Pin-Yu Chen; Sijia Liu 001; Luca Daniel (AAAI, 2019)

Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective. - Kaidi Xu; Hongge Chen; Sijia Liu 001; Pin-Yu Chen; Tsui-Wei Weng; Mingyi Hong 001; Xue Lin 001 (IJCAI, 2019)

Optimal Finite-Sum Smooth Non-Convex Optimization with SARAH. - Lam M. Nguyen; Marten van Dijk; Dzung T. Phan; Phuong Ha Nguyen; Tsui-Wei Weng; Jayant R. Kalagnanam (CoRR, 2019)

POPQORN: Quantifying Robustness of Recurrent Neural Networks. - Ching-Yun Ko; Zhaoyang Lyu; Tsui-Wei Weng; Luca Daniel; Ngai Wong 001; Dahua Lin (CoRR, 2019)

Verification of Neural Network Control Policy Under Persistent Adversarial Perturbation. - Yuh-Shyang Wang; Tsui-Wei Weng; Luca Daniel (CoRR, 2019)

Towards Verifying Robustness of Neural Networks Against Semantic Perturbations. - Jeet Mohapatra; Tsui-Wei Weng; Pin-Yu Chen; Sijia Liu 001; Luca Daniel (CoRR, 2019)

Towards Certificated Model Robustness Against Weight Perturbations. - Tsui-Wei Weng; Pu Zhao 001; Sijia Liu 001; Pin-Yu Chen; Xue Lin 001; Luca Daniel (AAAI, 2020)

Towards Verifying Robustness of Neural Networks Against A Family of Semantic Perturbations. - Jeet Mohapatra; Tsui-Wei Weng; Pin-Yu Chen; Sijia Liu 001; Luca Daniel (CVPR, 2020)

Toward Evaluating Robustness of Deep Reinforcement Learning with Continuous Control. - Tsui-Wei Weng; Krishnamurthy (Dj) Dvijotham; Jonathan Uesato; Kai Xiao; Sven Gowal; Robert Stanforth; Pushmeet Kohli (ICLR, 2020)

Higher-Order Certification For Randomized Smoothing. - Jeet Mohapatra; Ching-Yun Ko; Tsui-Wei Weng; Pin-Yu Chen; Sijia Liu 001; Luca Daniel (NeurIPS, 2020)

Rethinking Randomized Smoothing for Adversarial Robustness. - Jeet Mohapatra; Ching-Yun Ko; Tsui-Wei Weng; Sijia Liu 001; Pin-Yu Chen; Luca Daniel (CoRR, 2020)

Robust Deep Reinforcement Learning through Adversarial Loss. - Tuomas P. Oikarinen; Tsui-Wei Weng; Luca Daniel (CoRR, 2020)

On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning. - Ren Wang 008; Kaidi Xu; Sijia Liu 001; Pin-Yu Chen; Tsui-Wei Weng; Chuang Gan 001; Meng Wang 003 (ICLR, 2021)

On the Equivalence between Neural Network and Support Vector Machine. - Yilan Chen 002; Wei Huang 034; Lam M. Nguyen; Tsui-Wei Weng (NeurIPS, 2021)

Robust Deep Reinforcement Learning through Adversarial Loss. - Tuomas P. Oikarinen; Wang Zhang; Alexandre Megretski; Luca Daniel; Tsui-Wei Weng (NeurIPS, 2021)

Fast Training of Provably Robust Neural Networks by SingleProp. - Akhilan Boopathy; Tsui-Wei Weng; Sijia Liu 001; Pin-Yu Chen; Gaoyuan Zhang; Luca Daniel (CoRR, 2021)

Adversarially Robust Conformal Prediction. - Asaf Gendler; Tsui-Wei Weng; Luca Daniel; Yaniv Romano (ICLR, 2022)

Quantifying Safety of Learning-based Self-Driving Control Using Almost-Barrier Functions. - Zhizhen Qin; Tsui-Wei Weng; Sicun Gao (IROS, 2022)

Facile Prediction of Neutrophil Activation State from Microscopy Images: A New Dataset and Comparative Deep Learning Approaches. - Wei Liao; Ching-Yun Ko; Tsui-Wei Weng; Luca Daniel; Joel Voldman (ISBI, 2022)

Finite-sum smooth optimization with SARAH. - Lam M. Nguyen; Marten van Dijk; Dzung T. Phan; Phuong Ha Nguyen; Tsui-Wei Weng; Jayant R. Kalagnanam (Comput. Optim. Appl., 2022)

Evaluating Robustness of Cooperative MARL: A Model-based Approach. - Nhan H. Pham; Lam M. Nguyen; Jie Chen; Hoang Thanh Lam; Subhro Das; Tsui-Wei Weng (CoRR, 2022)

Learning Sample Reweighting for Accuracy and Adversarial Robustness. - Chester Holtz; Tsui-Wei Weng; Gal Mishne (CoRR, 2022)

Corrupting Neuron Explanations of Deep Visual Features. - Divyansh Srivastava; Tuomas P. Oikarinen; Tsui-Wei Weng (ICCV, 2023)

Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches. - Linbo Liu; Trong Nghia Hoang; Lam M. Nguyen; Tsui-Wei Weng (ICDM, 2023)

Attacking c-MARL More Effectively: A Data Driven Approach. - Nhan H. Pham; Lam M. Nguyen; Jie Chen; Hoang Thanh Lam; Subhro Das; Tsui-Wei Weng (ICDM, 2023)

Min-Max Multi-objective Bilevel Optimization with Applications in Robust Machine Learning. - Alex Gu; Songtao Lu; Parikshit Ram; Tsui-Wei Weng (ICLR, 2023)

Label-free Concept Bottleneck Models. - Tuomas P. Oikarinen; Subhro Das; Lam M. Nguyen; Tsui-Wei Weng (ICLR, 2023)

CLIP-Dissect: Automatic Description of Neuron Representations in Deep Vision Networks. - Tuomas P. Oikarinen; Tsui-Wei Weng (ICLR, 2023)

ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction. - Wang Zhang; Tsui-Wei Weng; Subhro Das; Alexandre Megretski; Luca Daniel; Lam M. Nguyen (ICML, 2023)

Certified Interpretability Robustness for Class Activation Mapping. - Alex Gu; Tsui-Wei Weng; Pin-Yu Chen; Sijia Liu 001; Luca Daniel (CoRR, 2023)

Constructive Assimilation: Boosting Contrastive Learning Performance through View Generation Strategies. - Ligong Han; Seungwook Han; Shivchander Sudalairaj; Charlotte Loh; Rumen Dangovski; Fei Deng 001; Pulkit Agrawal 001; Dimitris N. Metaxas; Leonid Karlinsky; Tsui-Wei Weng; Akash Srivastava (CoRR, 2023)

Concept-Monitor: Understanding DNN training through individual neurons. - Mohammad Ali Khan; Tuomas P. Oikarinen; Tsui-Wei Weng (CoRR, 2023)

The Importance of Prompt Tuning for Automated Neuron Explanations. - Justin Lee; Tuomas P. Oikarinen; Arjun Chatha; Keng-Chi Chang; Yilan Chen 002; Tsui-Wei Weng (CoRR, 2023)

Analyzing Deep PAC-Bayesian Learning with Neural Tangent Kernel: Convergence, Analytic Generalization Bound, and Efficient Hyperparameter Selection. - Wei Huang 034; Chunrui Liu; Yilan Chen 002; Richard Yi Da Xu; Miao Zhang; Tsui-Wei Weng (Trans. Mach. Learn. Res., 2023)

Towards Trustworthy Deep Learning. - Tsui-Wei Lily Weng (AAAI, 2024)

One Step Closer to Unbiased Aleatoric Uncertainty Estimation. - Wang Zhang; Ziwen Martin Ma; Subhro Das; Tsui-Wei Lily Weng; Alexandre Megretski; Luca Daniel; Lam M. Nguyen (AAAI, 2024)

Interpretability-Guided Test-Time Adversarial Defense. - Akshay R. Kulkarni; Tsui-Wei Weng (ECCV (37), 2024)

Prediction without Preclusion: Recourse Verification with Reachable Sets. - Avni Kothari; Bogdan Kulynych; Tsui-Wei Weng; Berk Ustun (ICLR, 2024)

Provably Robust Conformal Prediction with Improved Efficiency. - Ge Yan; Yaniv Romano; Tsui-Wei Weng (ICLR, 2024)

Linear Explanations for Individual Neurons. - Tuomas P. Oikarinen; Tsui-Wei Weng (ICML, 2024)

Breaking the Barrier: Enhanced Utility and Robustness in Smoothed DRL Agents. - Chung-En Sun; Sicun Gao; Tsui-Wei Weng (ICML, 2024)

AND: Audio Network Dissection for Interpreting Deep Acoustic Models. - Tung-Yu Wu; Yu-Xiang Lin; Tsui-Wei Weng (ICML, 2024)

Describe-and-Dissect: Interpreting Neurons in Vision Networks with Language Models. - Nicholas Bai; Rahul A. Iyer; Tuomas P. Oikarinen; Tsui-Wei Weng (CoRR, 2024)

Crafting Large Language Models for Enhanced Interpretability. - Chung-En Sun; Tuomas P. Oikarinen; Tsui-Wei Weng (CoRR, 2024)

VLG-CBM: Training Concept Bottleneck Models with Vision-Language Guidance. - Divyansh Srivastava; Ge Yan; Tsui-Wei Weng (CoRR, 2024)

Abstracted Shapes as Tokens - A Generalizable and Interpretable Model for Time-series Classification. - Yunshi Wen; Tengfei Ma 001; Tsui-Wei Weng; Lam M. Nguyen; Anak Agung Julius (CoRR, 2024)

Concept-Driven Continual Learning. - Sin-Han Yang; Tuomas P. Oikarinen; Tsui-Wei Weng (Trans. Mach. Learn. Res., 2024)

Interpretable Generative Models through Post-hoc Concept Bottlenecks. - Akshay R. Kulkarni; Ge Yan; Chung-En Sun; Tuomas P. Oikarinen; Tsui-Wei Weng (CVPR, 2025)

Concept Bottleneck Large Language Models. - Chung-En Sun; Tuomas P. Oikarinen; Berk Ustun; Tsui-Wei Weng (ICLR, 2025)

Effective Skill Unlearning through Intervention and Abstention. - Yongce Li; Chung-En Sun; Tsui-Wei Weng (NAACL (Long Papers), 2025)

Iterative Self-Tuning LLMs for Enhanced Jailbreaking Capabilities. - Chung-En Sun; Xiaodong Liu 003; Weiwei Yang; Tsui-Wei Weng; Hao Cheng 002; Aidan San; Michel Galley; Jianfeng Gao 001 (NAACL (Long Papers), 2025)

SAND: Enhancing Open-Set Neuron Descriptions through Spatial Awareness. - Anvita A. Srinivas; Tuomas P. Oikarinen; Divyansh Srivastava; Wei-Hung Weng; Tsui-Wei Weng (WACV, 2025)

Interpreting Neurons in Deep Vision Networks with Language Models. - Nicholas Bai; Rahul A. Iyer; Tuomas P. Oikarinen; Akshay R. Kulkarni; Tsui-Wei Weng (Trans. Mach. Learn. Res., 2025)

Understanding Fixed Predictions via Confined Regions. - Connor Lawless; Tsui-Wei Weng; Berk Ustun; Madeleine Udell (CoRR, 2025)

Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data. - Pei-Yau Weng; Minh Hoang; Lam M. Nguyen; My T. Thai; Tsui-Wei Weng; Trong Nghia Hoang (CoRR, 2025)

RAT: Boosting Misclassification Detection Ability without Extra Data. - Ge Yan; Tsui-Wei Weng (CoRR, 2025)

ThinkEdit: Interpretable Weight Editing to Mitigate Overly Short Thinking in Reasoning Models. - Chung-En Sun; Ge Yan; Tsui-Wei Weng (CoRR, 2025)

Anti-Sensing: Defense against Unauthorized Radar-based Human Vital Sign Sensing with Physically Realizable Wearable Oscillators. - Md. Farhan Tasnim Oshim; Nigel Doering; Bashima Islam; Tsui-Wei Weng; Tauhidur Rahman (CoRR, 2025)

Evaluating Neuron Explanations: A Unified Framework with Sanity Checks. - Tuomas P. Oikarinen; Ge Yan; Tsui-Wei Weng (CoRR, 2025)

Rethinking Crowd-Sourced Evaluation of Neuron Explanations. - Tuomas P. Oikarinen; Ge Yan; Akshay R. Kulkarni; Tsui-Wei Weng (CoRR, 2025)

Statistical Inference for Responsiveness Verification. - Seung Hyun Cheon; Meredith Stewart; Bogdan Kulynych; Tsui-Wei Weng; Berk Ustun (CoRR, 2025)

Graph Concept Bottleneck Models. - Haotian Xu; Tsui-Wei Weng; Lam M. Nguyen; Tengfei Ma 001 (CoRR, 2025)