Daniel, Luca

26 publications

ICLR 2025 Large Language Models Can Become Strong Self-Detoxifiers Ching-Yun Ko, Pin-Yu Chen, Payel Das, Youssef Mroueh, Soham Dan, Georgios Kollias, Subhajit Chaudhury, Tejaswini Pedapati, Luca Daniel

NeurIPSW 2024 Guaranteeing Conservation Laws with Projection in Physics-Informed Neural Networks Anthony Baez, Wang Zhang, Ziwen Ma, Subhro Das, Lam M. Nguyen, Luca Daniel

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

ICML 2024 What Would Gauss Say About Representations? Probing Pretrained Image Models Using Synthetic Gaussian Benchmarks Ching-Yun Ko, Pin-Yu Chen, Payel Das, Jeet Mohapatra, Luca Daniel

ICML 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

ICMLW 2023 On Robustness-Accuracy Characterization of Large Language Models Using Synthetic Datasets Ching-Yun Ko, Pin-Yu Chen, Payel Das, Yung-Sung Chuang, Luca Daniel

ICMLW 2022 Fast Convergence for Unstable Reinforcement Learning Problems by Logarithmic Mapping Wang Zhang, Lam M. Nguyen, Subhro Das, Alexandre Megretski, Luca Daniel, Tsui-Wei Weng

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

ECCV 2022 Learning from Multiple Annotator Noisy Labels via Sample-Wise Label Fusion Zhengqi Gao, Fan-Keng Sun, Mingran Yang, Sucheng Ren, Zikai Xiong, Marc Engeler, Antonio Burazer, Linda Wildling, Luca Daniel, Duane S. Boning

ICML 2022 Revisiting Contrastive Learning Through the Lens of Neighborhood Component Analysis: An Integrated Framework Ching-Yun Ko, Jeet Mohapatra, Sijia Liu, Pin-Yu Chen, Luca Daniel, Lily Weng

NeurIPSW 2022 SynBench: Task-Agnostic Benchmarking of Pretrained Representations Using Synthetic Data Ching-Yun Ko, Pin-Yu Chen, Jeet Mohapatra, Payel Das, Luca Daniel

AISTATS 2021 Hidden Cost of Randomized Smoothing Jeet Mohapatra, Ching-Yun Ko, Lily Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel

AAAI 2021 Fast Training of Provably Robust Neural Networks by SingleProp Akhilan Boopathy, Lily Weng, Sijia Liu, Pin-Yu Chen, Gaoyuan Zhang, Luca Daniel

NeurIPS 2021 Robust Deep Reinforcement Learning Through Adversarial Loss Tuomas Oikarinen, Wang Zhang, Alexandre Megretski, Luca Daniel, Tsui-Wei Weng

AAAI 2020 Fastened CROWN: Tightened Neural Network Robustness Certificates Zhaoyang Lyu, Ching-Yun Ko, Zhifeng Kong, Ngai Wong, Dahua Lin, Luca Daniel

NeurIPS 2020 Higher-Order Certification for Randomized Smoothing Jeet Mohapatra, Ching-Yun Ko, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel

ICML 2020 Neural Network Control Policy Verification with Persistent Adversarial Perturbation Yuh-Shyang Wang, Lily Weng, Luca Daniel

ICML 2020 Proper Network Interpretability Helps Adversarial Robustness in Classification Akhilan Boopathy, Sijia Liu, Gaoyuan Zhang, Cynthia Liu, Pin-Yu Chen, Shiyu Chang, Luca Daniel

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

CVPR 2020 Towards Verifying Robustness of Neural Networks Against a Family of Semantic Perturbations Jeet Mohapatra, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel

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

ICML 2019 POPQORN: Quantifying Robustness of Recurrent Neural Networks Ching-Yun Ko, Zhaoyang Lyu, Lily Weng, Luca Daniel, Ngai Wong, Dahua Lin

ICML 2019 PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach Lily Weng, Pin-Yu Chen, Lam Nguyen, Mark Squillante, Akhilan Boopathy, Ivan Oseledets, Luca Daniel

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

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

ICML 2018 Towards Fast Computation of Certified Robustness for ReLU Networks Lily Weng, Huan Zhang, Hongge Chen, Zhao Song, Cho-Jui Hsieh, Luca Daniel, Duane Boning, Inderjit Dhillon