Tensor network subspace identification of polynomial state space models. - Kim Batselier; Ching Yun Ko; Ngai Wong 001 (Autom., 2018)

Fast and Accurate Tensor Completion with Tensor Trains: A System Identification Approach. - Ching Yun Ko; Kim Batselier; Wenjian Yu; Ngai Wong 001 (CoRR, 2018)

Deep Compression of Sum-Product Networks on Tensor Networks. - Ching Yun Ko; Cong Chen 003; Yuke Zhang; Kim Batselier; Ngai Wong 001 (CoRR, 2018)

Extended Kalman Filtering with Low-Rank Tensor Networks for MIMO Volterra System Identification. - Kim Batselier; Ching-Yun Ko; Ngai Wong 001 (CDC, 2019)

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

MiSC: Mixed Strategies Crowdsourcing. - Ching Yun Ko; Rui Lin; Shu Li; Ngai Wong 001 (IJCAI, 2019)

Matrix Product Operator Restricted Boltzmann Machines. - Cong Chen 003; Kim Batselier; Ching-Yun Ko; Ngai Wong 001 (IJCNN, 2019)

A Support Tensor Train Machine. - Cong Chen 003; Kim Batselier; Ching-Yun Ko; Ngai Wong 001 (IJCNN, 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)

Fastened CROWN: Tightened Neural Network Robustness Certificates. - Zhaoyang Lyu; Ching-Yun Ko; Zhifeng Kong; Ngai Wong 001; Dahua Lin; Luca Daniel (AAAI, 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)

HOTCAKE: Higher Order Tucker Articulated Kernels for Deeper CNN Compression. - Rui Lin; Ching-Yun Ko; Zhuolun He; Cong Chen 003; Yuan Cheng; Hao Yu 001; Graziano Chesi; Ngai Wong 001 (CoRR, 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)

Fast and Accurate Tensor Completion With Total Variation Regularized Tensor Trains. - Ching-Yun Ko; Kim Batselier; Luca Daniel; Wenjian Yu; Ngai Wong 001 (IEEE Trans. Image Process., 2020)

Deep Model Compression and Inference Speedup of Sum-Product Networks on Tensor Trains. - Ching-Yun Ko; Cong Chen 003; Zhuolun He; Yuke Zhang; Kim Batselier; Ngai Wong 001 (IEEE Trans. Neural Networks Learn. Syst., 2020)

Hidden Cost of Randomized Smoothing. - Jeet Mohapatra; Ching-Yun Ko; Lily Weng; Pin-Yu Chen; Sijia Liu 001; Luca Daniel (AISTATS, 2021)

Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework. - Ching-Yun Ko; Jeet Mohapatra; Sijia Liu 001; Pin-Yu Chen; Luca Daniel; Lily Weng (ICML, 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)

Visual Pre-training for Navigation: What Can We Learn from Noise? - Yanwei Wang; Ching-Yun Ko (CoRR, 2022)

SynBench: Task-Agnostic Benchmarking of Pretrained Representations using Synthetic Data. - Ching-Yun Ko; Pin-Yu Chen; Jeet Mohapatra; Payel Das; Luca Daniel (CoRR, 2022)

Visual Pre-Training for Navigation: What Can We Learn from Noise? - Yanwei Wang; Ching-Yun Ko; Pulkit Agrawal 001 (IROS, 2023)

Sample-Specific Debiasing for Better Image-Text Models. - Peiqi Wang; Yingcheng Liu; Ching-Yun Ko; William M. Wells III; Seth J. Berkowitz; Steven Horng; Polina Golland (MLHC, 2023)

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, 2024)

Large Language Models can be Strong Self-Detoxifiers. - Ching-Yun Ko; Pin-Yu Chen; Payel Das; Youssef Mroueh; Soham Dan; Georgios Kollias; Subhajit Chaudhury; Tejaswini Pedapati; Luca Daniel (CoRR, 2024)

Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models. - Chung-Ting Tsai 002; Ching-Yun Ko; I-Hsin Chung; Yu-Chiang Frank Wang; Pin-Yu Chen (CoRR, 2024)

Understanding and Improving Representational Robustness of Machine Learning Models - Ching Yun Ko (2024)

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 (ICLR, 2025)

Attention Tracker: Detecting Prompt Injection Attacks in LLMs. - Kuo-Han Hung; Ching-Yun Ko; Ambrish Rawat; I-Hsin Chung; Winston H. Hsu; Pin-Yu Chen (NAACL (Findings), 2025)

STAR: Spectral Truncation and Rescale for Model Merging. - Yu-Ang Lee; Ching-Yun Ko; Tejaswini Pedapati; I-Hsin Chung; Mi-Yen Yeh; Pin-Yu Chen (NAACL (Short Papers), 2025)

Can Memory-Augmented Language Models Generalize on Reasoning-in-a-Haystack Tasks? - Payel Das; Ching-Yun Ko; Sihui Dai; Georgios Kollias; Subhajit Chaudhury; Aurélie C. Lozano (CoRR, 2025)