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Ching Yun Ko
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2020 – today
- 2024
- [b1]Ching Yun Ko:
Understanding and Improving Representational Robustness of Machine Learning Models. MIT, USA, 2024 - [c13]Ching-Yun Ko, Pin-Yu Chen, Payel Das, Jeet Mohapatra, Luca Daniel:
What Would Gauss Say About Representations? Probing Pretrained Image Models using Synthetic Gaussian Benchmarks. ICML 2024 - [i16]Ching-Yun Ko, Pin-Yu Chen, Payel Das, Youssef Mroueh, Soham Dan, Georgios Kollias, Subhajit Chaudhury, Tejaswini Pedapati, Luca Daniel:
Large Language Models can be Strong Self-Detoxifiers. CoRR abs/2410.03818 (2024) - 2023
- [c12]Yanwei Wang, Ching-Yun Ko, Pulkit Agrawal:
Visual Pre-Training for Navigation: What Can We Learn from Noise? IROS 2023: 3897-3902 - [c11]Peiqi Wang, Yingcheng Liu, Ching-Yun Ko, William M. Wells III, Seth J. Berkowitz, Steven Horng, Polina Golland:
Sample-Specific Debiasing for Better Image-Text Models. MLHC 2023: 788-803 - [i15]Peiqi Wang, Yingcheng Liu, Ching-Yun Ko, William M. Wells III, Seth J. Berkowitz, Steven Horng, Polina Golland:
Sample-Specific Debiasing for Better Image-Text Models. CoRR abs/2304.13181 (2023) - 2022
- [c10]Ching-Yun Ko, Jeet Mohapatra, Sijia Liu, Pin-Yu Chen, Luca Daniel, Lily Weng:
Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework. ICML 2022: 11387-11412 - [c9]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 - [i14]Yanwei Wang, Ching-Yun Ko:
Visual Pre-training for Navigation: What Can We Learn from Noise? CoRR abs/2207.00052 (2022) - [i13]Ching-Yun Ko, Pin-Yu Chen, Jeet Mohapatra, Payel Das, Luca Daniel:
SynBench: Task-Agnostic Benchmarking of Pretrained Representations using Synthetic Data. CoRR abs/2210.02989 (2022) - 2021
- [c8]Jeet Mohapatra, Ching-Yun Ko, Lily Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel:
Hidden Cost of Randomized Smoothing. AISTATS 2021: 4033-4041 - [i12]Ching-Yun Ko, Jeet Mohapatra, Sijia Liu, Pin-Yu Chen, Luca Daniel, Lily Weng:
Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework. CoRR abs/2112.04468 (2021) - 2020
- [j3]Ching-Yun Ko
, Kim Batselier
Fast and Accurate Tensor Completion With Total Variation Regularized Tensor Trains. IEEE Trans. Image Process. 29: 6918-6931 (2020) - [j2]Ching-Yun Ko
Deep Model Compression and Inference Speedup of Sum-Product Networks on Tensor Trains. IEEE Trans. Neural Networks Learn. Syst. 31(7): 2665-2671 (2020) - [c7]Zhaoyang Lyu, Ching-Yun Ko, Zhifeng Kong, Ngai Wong, Dahua Lin, Luca Daniel:
Fastened CROWN: Tightened Neural Network Robustness Certificates. AAAI 2020: 5037-5044 - [c6]Jeet Mohapatra, Ching-Yun Ko, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel:
Higher-Order Certification For Randomized Smoothing. NeurIPS 2020 - [i11]Rui Lin, Ching-Yun Ko, Zhuolun He, Cong Chen, Yuan Cheng, Hao Yu, Graziano Chesi, Ngai Wong:
HOTCAKE: Higher Order Tucker Articulated Kernels for Deeper CNN Compression. CoRR abs/2002.12663 (2020) - [i10]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) - [i9]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
- [c5]Kim Batselier, Ching-Yun Ko, Ngai Wong:
Extended Kalman Filtering with Low-Rank Tensor Networks for MIMO Volterra System Identification. CDC 2019: 7148-7153 - [c4]Ching-Yun Ko, Zhaoyang Lyu, Lily Weng, Luca Daniel, Ngai Wong, Dahua Lin:
POPQORN: Quantifying Robustness of Recurrent Neural Networks. ICML 2019: 3468-3477 - [c3]Ching Yun Ko, Rui Lin, Shu Li, Ngai Wong:
MiSC: Mixed Strategies Crowdsourcing. IJCAI 2019: 1394-1400 - [c2]Cong Chen, Kim Batselier, Ching-Yun Ko, Ngai Wong:
Matrix Product Operator Restricted Boltzmann Machines. IJCNN 2019: 1-8 - [c1]Cong Chen, Kim Batselier, Ching-Yun Ko, Ngai Wong:
A Support Tensor Train Machine. IJCNN 2019: 1-8 - [i8]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) - [i7]Ching-Yun Ko, Rui Lin, Shu Li, Ngai Wong:
MiSC: Mixed Strategies Crowdsourcing. CoRR abs/1905.07394 (2019) - [i6]Zhaoyang Lyu, Ching-Yun Ko, Zhifeng Kong, Ngai Wong, Dahua Lin, Luca Daniel:
Fastened CROWN: Tightened Neural Network Robustness Certificates. CoRR abs/1912.00574 (2019) - 2018
- [j1]Kim Batselier
Tensor network subspace identification of polynomial state space models. Autom. 95: 187-196 (2018) - [i5]Cong Chen, Kim Batselier, Ching Yun Ko, Ngai Wong:
A Support Tensor Train Machine. CoRR abs/1804.06114 (2018) - [i4]Ching Yun Ko, Kim Batselier, Wenjian Yu, Ngai Wong:
Fast and Accurate Tensor Completion with Tensor Trains: A System Identification Approach. CoRR abs/1804.06128 (2018) - [i3]Ching Yun Ko, Cong Chen, Yuke Zhang, Kim Batselier, Ngai Wong:
Deep Compression of Sum-Product Networks on Tensor Networks. CoRR abs/1811.03963 (2018) - [i2]Cong Chen, Kim Batselier, Ching-Yun Ko, Ngai Wong:
Matrix Product Operator Restricted Boltzmann Machines. CoRR abs/1811.04608 (2018) - 2017
- [i1]Kim Batselier, Ching Yun Ko, Ngai Wong:
Tensor network subspace identification of polynomial state space models. CoRR abs/1709.08773 (2017)
Coauthor Index
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