Pin-Yu Chen (陳品諭)
Research Staff Member, IBM Research AI; MIT-IBM Watson AI Lab; RPI-IBM AIRC
IBM Thomas J. Watson Research Center, NY, USA
Link to my Twitter Google scholar profile CV Academic activities Bio
Contact: pinyuchen.tw at gmail.com (primary reviewer account) , pin-yu.chen at ibm.com
- I am a Research Staff Member of Trusted AI Group & PI of MIT-IBM Watson AI Lab, IBM Thomas J. Watson Research Center. I am also the Chief Scientist of RPI-IBM AI Research Collaboration program. My recent research focus has been on adversarial machine learning and robustness of neural networks, and more broadly, making machine learning trustworthy. Here is my <bio>. Check out my research vision and portfolio.
- My research contributes to IBM Adversarial Robustness Toolbox, AI Explainability 360, AI Factsheets 360, and Watson Openscale
- I am open to collaboration with highly motivated researchers!
- I received my Ph.D. degree in electrical engineering and computer science and M.A. degree in Statistics from the University of Michigan Ann Arbor in 2016, under the supervision of Prof. Alfred Hero.
- Workshop organizer (selected):
- Tutorial presenter (selected):
[CVPR'21] Practical Adversarial Robustness in Deep Learning: Problems and Solutions
[ECCV'20] Adversarial Robustness of Deep Learning Models: Attack, Defense, Verification, and Beyond
[CVPR'20] Zeroth Order Optimization: Theory and Applications to Deep Learning
[KDD'19] Recent Progress in Zeroth Order Optimization and Its Applications to Adversarial Robustness in Data Mining and Machine Learning
- Editorial board: PLOS ONE, IEEE J-IOT (Guest), KSII-TIIS (area editor)
- Area Chair/Senior PC: ICML, AAAI, IJCAI
Featured Talks
Featured Media Coverage
Adversarial Robustness & Trustworthy ML: <IBM_Blog_Certification> <EETimes_adversarialAI> <Portswigger_interview> <Techerati_interview> <TheNextWeb_BAR> <Analytics_India_Magazine_ZO_opt> <AItrends_interview> <TheNextWeb_sanitization> <TechTalks_Robust_AI> <Nature_News> <EE_TIMES_adv_robustness> <PHYS.ORG_AutoZOOM> <TechTalks_Paraphrasing> <IBM_Research_AI_Review_2019> <IBM Response to NIST RFI on AI> <SiliconANGLE> <Venturebeat_Adv_T-Shirt> <Quartz_Adv_T-Shirt> <WIRED_Adv_T-Shirt> <TechTalks_temporal_dependency> <VB_Paraphrasing> <Forbes_CEM>
Machine Learning for Scientific Discovery: <MIT_News_Covid-19> <IBM_blog_AI_Drug_Discovery>
Cyber Security: <IEEE COMSOC Technology News> <IEEE Xplore Spotlight> <PNNL research highlight>
Funded Research Projects
IBM PI of the Department of Energy project "A Robust Event Diagnostics Platform: Integrating Tensor Analytics and Machine Learning into Real-time Grid Monitoring" [2019 - present]
RPI-IBM AI Research Collaboration (AIRC): PI of ongoing RPI-IBM research projects [2019 - present]
MIT-IBM Watson AI Lab: PI of ongoing MIT-IBM research projects. Two of them are featured in <MIT Quest for Intelligence Research> and <MIT_News> [2018 - present]
UIUC-IBM Center for Cognitive Computing System Research (C3SR) [2019 - present]
Research Interests
Machine Learning: adversarial machine learning and robustness, online and distributed learning, unsupervised and semi-supervised learning
Cyber Security: AI for security, attack and defense models, action recommendations for network resilience, malware propagation models
Graph Learning and Network Data Analytics: spectral graph theory and algorithms, graph signal processing, community detection, graph clustering, event propagation and control in networks, complex network
Selected Awards and Honors
Special IBM Research Division Team Award for COVID-19 Research (2020)
IBM Master Inventor (2020)
Listed as “Top Subject Matter Experts in AI & ML” by onalytica (2020) <Link>
Two Outstanding Research Accomplishments on "adversarial robustness" and "trusted AI" at IBM Research (2019)
Research Accomplishment on "graph learning and analysis" at IBM Research (2019)
NeurIPS Best Reviewer Award (2017) <Link>
Best Paper Finalist, ACM Workshop on Artificial Intelligence and Security (2017)
Outstanding Performance Award at Pacific Northwest National Laboratory (2015)
Univ. Michigan Rackham International Student Fellowship (Chia-Lun Lo Fellowship) (2013-2014) <Link>
EE:Systems Fellowship, University of Michigan, Ann Arbor (2012-2013)
Best Master Thesis Award of Graduate Institute of Communications Engineering, National Taiwan Univ. (2011)
Second Best Master Thesis Award of Chinese Institute of Electrical Engineering (2011)
IEEE GLOBECOM GOLD Best Paper Award (2010) <Link>
Ranked 1st Place (Full Scores) in Taiwan National College Entrance Exam (2005)
Selected Publications
I. Adversarial Machine Learning and Robustness of Neural Networks
-Attack & Defense
“How Robust are Randomized Smoothing based Defenses to Data Poisoning?” CVPR 2021
Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, and Jihun Hamm
“On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning,” ICLR 2021
Ren Wang, Kaidi Xu, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Chuang Gan, and Meng Wang
“Self-Progressing Robust Training,” AAAI 2021
Minhao Cheng, Pin-Yu Chen, Sijia Liu, Shiyu Chang, Cho-Jui Hsieh, and Payel Das
“Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning,” AAAI 2021
Syed Zawad, Ahsan Ali, Pin-Yu Chen, Ali Anwar, Yi Zhou, Nathalie Baracaldo, Yuan Tian, Feng Yan
“Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Cases,” ECCV 2020
Ren Wang, Gaoyuan Zhang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong, and Meng Wang
“Adversarial T-shirt! Evading Person Detectors in A Physical World,” ECCV 2020
Kaidi Xu, Gaoyuan Zhang, Sijia Liu, Quanfu Fan, Mengshu Sun, Hongge Chen, Pin-Yu Chen, Yanzhi Wang, and Xue Lin
<Venturebeat_Adv_T-Shirt> <Import_AI_Adv_T-Shirt> <The_Register_Adv_T-Shirt> <NEU_News_Adv_T-Shirt> <Boston Globe_Adv_T-Shirt> <VICE_Adv_T-Shirt> <ODSC_Adv_T-Shirt> <Quartz_Adv_T-Shirt> <WIRED_Adv_T-Shirt> <Comm_ACM_Adv_T-Shirt> <機器之心_Adv_T-Shirt>
“Proper Network Interpretability Helps Adversarial Robustness in Classification,” ICML 2020
Akhilan Boopathy, Sijia Liu, Gaoyuan Zhang, Cynthia Liu, Pin-Yu Chen, Shiyu Chang, and Luca Daniel
“Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness,” ICLR 2020
Pu Zhao, Pin-Yu Chen, Payel Das, Karthikeyan Natesan Ramamurthy, and Xue Lin
<Model_Sanitization_code> <TechTalks_sanitization> <TheNextWeb_sanitization>
“DBA: Distributed Backdoor Attacks against Federated Learning,” ICLR 2020
Chulin Xie, Keli Huang, Pin-Yu Chen, and Bo Li
“Sign-OPT: A Query-Efficient Hard-label Adversarial Attack,” ICLR 2020
Minhao Cheng*, Simranjit Singh*, Patrick H. Chen, Pin-Yu Chen, Sijia Liu, and Cho-Jui Hsieh (*equal contribution)
“Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples,” AAAI 2020
Minhao Cheng, Jinfeng Yi, Pin-Yu Chen, Huan Zhang, and Cho-Jui Hsieh
“Towards Query-Efficient Black-Box Adversary with Zeroth-Order Natural Gradient Descent,” AAAI 2020
Pu Zhao, Pin-Yu Chen, Siyue Wang, and Xue Lin
Pu Zhao, Sijia Liu, Pin-Yu Chen, Nghia Hoang, Kaidi Xu, Bhavya Kailkhura, and Xue Lin
Xiao Wang*, Siyue Wang*, Pin-Yu Chen, Yanzhi Wang, Brian Kulis, Xue Lin, and Sang Chin (*equal contribution)
<HRS_code> <TechTalks_HRS> <Medium_HRS> <IBM_Research_Blog_GNN_HRS>
“Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective,” IJCAI 2019
Kaidi Xu*, Hongge Chen*, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Mingyi Hong, and Xue Lin (*equal contribution)
“Discrete Adversarial Attacks and Submodular Optimization with Applications to Text Classification,” SysML 2019
Qi Lei*, Lingfei Wu*, Pin-Yu Chen, Alexandros G. Dimakis, Inderjit S. Dhillon, and Michael Witbrock (*equal contribution)
<Paraphrasing_attack_code> <VB_Paraphrasing> <TechTalks_Paraphrasing> <Jiqizhixin_Paraphasing> <Nature_News>
“Query-Efficient Hard-label Black-box Attack: An Optimization-based Approach,” ICLR 2019
Minhao Cheng, Thong Le, Pin-Yu Chen, Jinfeng Yi, Huan Zhang, and Cho-Jui Hsieh
“Structured Adversarial Attack: Towards General Implementation and Better Interpretability,” ICLR 2019
Kaidi Xu* Sijia Liu*, Pu Zhao, Pin-Yu Chen, Huan Zhang, Quanfu Fan, Deniz Erdogmus, Yanzhi Wang, Xue Lin (*equal contribution)
“Characterizing Audio Adversarial Examples Using Temporal Dependency,” ICLR 2019
Zhuolin Yang, Bo Li, Pin-Yu Chen, Dawn Song
<TD_code> <poster> <TechTalks_temporal_dependency> <IBM_Research_Blog_Temporal_Dependency> <Nature_News>
“AutoZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural Networks,” AAAI 2019 (oral presentation)
Chun-Chen Tu*, Paishun Ting*, Pin-Yu Chen*, Sijia Liu, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh, and Shin-Ming Cheng (*equal contribution)
<AutoZOOM_code> <slides> <poster> <EE_TIMES> <TechTalks_1> <TechTalks_2> <IBM_Research_Blog_AutoZOOM> <PHYS.ORG_AutoZOOM> <IBM_Research_AI_Review_2019> <MC.AI_AutoZOOM>
“Is Ordered Weighted $\ell_1$ Regularized Regression Robust to Adversarial Perturbation? A Case Study on OSCAR,” IEEE GlobalSIP 2018
Pin-Yu Chen*, Bhanukiran Vinzamuri*, and Sijia Liu (*equal contribution)
<poster>
“Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning,” ACL 2018
Hongge Chen*, Huan Zhang*, Pin-Yu Chen, Jinfeng Yi, and Cho-Jui Hsieh (*equal contribution)
“EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples,” AAAI 2018
Pin-Yu Chen*, Yash Sharma*, Huan Zhang, Jinfeng Yi, and Cho-Jui Hsieh (*equal contribution)
<EAD_code> <cleverhans> <adversarial_robustness_toolbox> <Foolbox> <slides>
“ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models,” ACM CCS Workshop on AI-Security, 2017
Pin-Yu Chen*, Huan Zhang*, Yash Sharma, Jinfeng Yi, and Cho-Jui Hsieh (*equal contribution)
<ZOO_code> <adversarial_robustness_toolbox> <slides> (best paper award finalist)
-Robustness Evaluation & Verification
“Non-Singular Adversarial Robustness of Neural Networks,” ICASSP 2021
Yu-Lin Tsai, Chia-Yi Hsu, Chia-Mu Yu, and Pin-Yu Chen
“Hidden Cost of Randomized Smoothing,” AISTATS 2021
Jeet Mohapatra, Ching-Yun Ko, Tsui-Wei (Lily) Weng, Sijia Liu, Pin-Yu Chen, and Luca Daniel
“Fast Training of Provably Robust Neural Networks by SingleProp,” AAAI 2021
Akhilan Boopathy, Lily Weng, Sijia Liu, Pin-Yu Chen, Gaoyuan Zhang, and Luca Daniel
“Higher-Order Certification For Randomized Smoothing,” NeurIPS 2020 (spotlight)
Jeet Mohapatra, Ching-Yun Ko, Tsui-Wei (Lily) Weng, Pin-Yu Chen, Sijia Liu, and Luca Daniel
“Towards Verifying Robustness of Neural Networks Against A Family of Semantic Perturbations,” CVPR 2020 (oral presentation)
Jeet Mohapatra, Tsui-Wei (Lily) Weng, Pin-Yu Chen, Sijia Liu, and Luca Daniel
“Towards Certificated Model Robustness Against Weight Perturbations,” AAAI 2020
“PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach,” ICML 2019
Tsui-Wei Weng, Pin-Yu Chen, Lam M. Nguyen, Mark S. Squillante, Ivan Oseledets, Akhilan Boopathy, and Luca Daniel
<PROVEN_code> <slides>
“CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks,” AAAI 2019 (oral presentation)
Akhilan Boopathy, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, and Luca Daniel
<CNN-Cert_code> <slides> <poster> <EE_TIMES> <TechTalks_Robust_AI> <IBM_Research_Blog_CNN-Cert> <MIT_IBM_Medium_CNN-Cert> <IBM Response to NIST RFI on AI> <MC.AI_CNN-Cert>
“Efficient Neural Network Robustness Certification with General Activation Functions,” NeurIPS 2018
Huan Zhang*, Tsui-Wei Weng*, Pin-Yu Chen, Cho-Jui Hsieh, and Luca Daniel (*equal contribution)
“Is Robustness the Cost of Accuracy? A Comprehensive Study on the Robustness of 18 Deep Image Classification Models,” ECCV 2018
“Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach,” ICLR 2018
Tsui-Wei Weng*, Huan Zhang*, Pin-Yu Chen, Jinfeng Yi, Dong Su, Yupeng Guo, Cho-Jui Hsieh, and Luca Daniel (*equal contribution)
<CLEVER_code> <adversarial_robustness_toolbox> <IBM_Research_Blog> <SiliconANGLE> <MIT_IBM_Medium> <IBM Response to NIST RFI on AI> <Fool_the_Bank_demo>
-Applications to Other Machine Learning Tasks
“AID: Attesting the Integrity of Deep Neural Networks,” DAC 2021
Omid Aramoon, Pin-Yu Chen, and Gang Qu,
“Don't Forget to Sign the Gradients!,” MLSyS 2021
Omid Aramoon, Pin-Yu Chen, and Gang Gu
“Fake it Till You Make it: Self-Supervised Semantic Shifts for Monolingual Word Embedding Tasks,” AAAI 2021
Maurício Gruppi, Sibel Adali, and Pin-Yu Chen
Yun-Yun Tsai, Pin-Yu Chen, and Tsung-Yi Ho
<BAR_code>
II. Cyber Security & Network Resilience
“Traffic-aware Patching for Cyber Security in Mobile IoT,” IEEE Communications Magazine, 2017
S.-M. Cheng, Pin-Yu Chen, C.-C. Lin, and H.-C. Hsiao
“Decapitation via Digital Epidemics: A Bio-Inspired Transmissive Attack,” IEEE Communications Magazine, 2016
Pin-Yu Chen, C.-C. Lin, S.-M. Cheng, C.-Y. Huang, and H.-C. Hsiao
“Multi-Centrality Graph Spectral Decompositions and Their Application to Cyber Intrusion Detection,” IEEE ICASSP, 2016
“Action Recommendation for Cyber Resilience,” ACM CCS Workshop, 2015
S. Choudhury, Pin-Yu Chen, L. Rodriguez, D. Curtis, P. Nordquist, I. Ray, K. Oler, and P. Nordquist,
“Sequential Defense against Random and Intentional Attacks in Complex Networks”, Physical Review E, 2015
Pin-Yu Chen and S.-M. Cheng
“Assessing and Safeguarding Network Resilience to Centrality Attacks,” IEEE Communications Magazine, 2014
Pin-Yu Chen and A. O. Hero
“Information Fusion to Defend Intentional Attack in Internet of Things,” IEEE Internet of Things Journal, 2014
Pin-Yu Chen, S.-M. Cheng, and K.-C. Chen
“Smart Attacks in Smart Grid Communication Networks,” IEEE Communications Magazine, 2012
Pin-Yu Chen, S.-M. Cheng, and K.-C. Chen
III. Graph Learning and Network Data Analytics
“Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case,” ICML 2020
Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, and Jinjun Xiong,
“hpGAT: High-order Proximity Informed Graph Attention Network,” IEEE Access, 2019
Zhining Liu, Weiyi Liu, Pin-Yu Chen, Chenyi Zhuang, and Chengyun Song,
“Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications,” ICML 2019 (long oral presentation)
Pin-Yu Chen, Lingfei Wu, Sijia Liu, and Indika Rajapakse
<FINGER_code> <slides>
“Neural-Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding,” Data Science and Engineering & ASONAM, 2019
Vachik S. Dave, Baichuan Zhang, Pin-Yu Chen, Mohammad Al Hasan
“Learning Graph Topological Features via GAN,” IEEE Access, 2019
Weiyi Liu, Hal Cooper, Min-Hwan Oh, Pin-Yu Chen, Sailung Yeung, Fucai Yu, Toyotaro Suzumura, Guangmin Hu
“Scalable Spectral Clustering Using Random Binning Features,” ACM KDD, 2018 (oral presentation)
Lingfei Wu, Pin-Yu Chen, Ian En-Hsu Yen, Fangli Xu, Yinglong Xia, and Charu Aggarwal
<IBM_Research_Blog> <poster> <slides> <SC-RB_Code>
“Phase Transitions and a Model Order Selection Criterion for Spectral Graph Clustering,” IEEE Transactions on Signal Processing, 2018
“On the Supermodularity of Active Graph-based Semi-Supervised Learning with Stieltjes Matrix Regularization,” IEEE ICASSP, 2018
Pin-Yu Chen* and Dennis Wei* (*equal contribution)
<poster>
“Revisiting Spectral Graph Clustering with Generative Community Models,” IEEE ICDM, 2017
Pin-Yu Chen and L. Wu
<slides>
“Multilayer Spectral Graph Clustering via Convex Layer Aggregation: Theory and Algorithms,” IEEE Transactions on Signal and Information Processing over Networks, 2017
Pin-Yu Chen and A. O. Hero
(awarded IEEE GlobalSIP Student Travel Grant) <slides> <MIMOSA_code>
“Bias-Variance Tradeoff of Graph Laplacian Regularizer,” IEEE Signal Processing Letters, 2017
Pin-Yu Chen and S. Liu
“Incremental Eigenpair Computation for Graph Laplacian Matrices: Theory and Applications,” Social Network Analysis and Mining, 2018
“When Crowdsourcing Meets Mobile Sensing: A Social Network Perspective,” IEEE Communications Magazine, 2015
Pin-Yu Chen, S.-M. Cheng, P.-S. Ting, C.-W. Lien, and F.-J Chu
“Deep Community Detection,” IEEE Transactions on Signal Processing, 2015
Pin-Yu Chen and A. O. Hero
<DCD_code>
“Phase Transitions in Spectral Community Detection,” IEEE Transactions on Signal Processing, 2015
Pin-Yu Chen and A. O. Hero
“Universal Phase Transition in Community Detectability under a Stochastic Block Model,” Physical Review E, 2015
Pin-Yu Chen and A. O. Hero
“Local Fiedler Vector Centrality for Detection of Deep and Overlapping Communities in Networks,” IEEE ICASSP, 2014
IV. Event Propagation Models in Networks
“Identifying Influential Links for Event Propagation on Twitter: A Network of Networks Approach,” IEEE Transactions on Signal and Information Processing over Networks, 2018
Pin-Yu Chen, Chun-Chen Tu, Paishun Ting, Ya-Yun Luo, Danai Koutra, and Alfred Hero
“Analysis of Data Dissemination and Control in Social Internet of Vehicles,” IEEE Internet of Things Journal, 2018
Pin-Yu Chen, Shin-Ming Cheng and Meng-Hsuan Sung
“Analysis of Information Delivery Dynamics in Cognitive Sensor Networks Using Epidemic Models,” IEEE Internet of Things Journal, 2017
Pin-Yu Chen, S.-M. Cheng, and H.-Y. Hsu
“Optimal Control of Epidemic Information Dissemination over Networks,” IEEE Transactions on Cybernetics, 2014
Pin-Yu Chen, S.-M. Cheng, and K.-C. Chen
“On Modeling Malware Propagation in Generalized Social Networks,” IEEE Communications Letters, 2011
S.-M. Cheng, W. C. Ao, Pin-Yu Chen, and K.-C. Chen
“Information Epidemics in Complex Networks with Opportunistic Links and Dynamic Topology," IEEE GLOBECOM, 2010
Pin-Yu Chen, and K.-C. Chen
V. Optimization for Machine Learning and Signal Processing
“Rate-improved Inexact Augmented Lagrangian Method for Constrained Nonconvex Optimization,” AISTATS 2021
Zichong Li, Pin-Yu Chen*, Sijia Liu*, Songtao Lu*, and Yangyang Xu* (*alphabetical order)
“Optimizing Mode Connectivity via Neuron Alignment,” NeurIPS 2020
“ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training,” NeurIPS 2020
Chia-Yu Chen, Jiamin Ni, Songtao Lu, Xiaodong Cui, Pin-Yu Chen, Xiao Sun, Naigang Wang, Swagath Venkataramani, Vijayalakshmi (Viji) Srinivasan, Wei Zhang, and Kailash Gopalakrishnan
“A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning,” IEEE Signal Processing Magazine, 2020
Sijia Liu, Pin-Yu Chen, Bhavya Kailkhura, Gaoyuan Zhang, Alfred Hero, and Pramod K. Varshney
“SignSGD via Zeroth-Order Oracle,” ICLR 2019
Sijia Liu, Pin-Yu Chen, Xiangyi Chen, and Mingyi Hung
“Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization,” NeurIPS 2018
Sijia Liu, Bhavya Kailkhura, Pin-Yu Chen, Pai-Shun Ting, Shiyu Chang, and Lisa Amini
<poster>
“Accelerated Distributed Dual Averaging over Evolving Networks of Growing Connectivity,” IEEE Transactions on Signal Processing, 2018
Sijia Liu, Pin-Yu Chen, and Alfred Hero
“Zeroth-Order Online Alternating Direction Method of Multipliers: Convergence Analysis and Applications,” AISTATS 2018
Sijia Liu, Jie Chen, Pin-Yu Chen, and Alfred Hero
<poster>
VI. Interpretability, Explainability, Fairness and Causality for Machine Learning Systems
“AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models,” Journal of Machine Learning Research, 2020
Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilović, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, and Yunfeng Zhang (alphabetical order)
“An Information-Theoretic Perspective on the Relationship Between Fairness and Accuracy,” ICML 2020
Sanghamitra Dutta, Dennis Wei, Hazar Yueksel, Pin-Yu Chen, Sijia Liu, and Kush R. Varshney
“When Causal Intervention Meets Adversarial Perturbation and Image Masking for Deep Neural Networks,” IEEE ICIP 2019
Chao-Han Huck Yang*, Yi-Chieh Liu*, Pin-Yu Chen, Xiaoli Ma, Yi-Chang James Tsai (*equal contribution)
<Code>
“Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives,” NeurIPS 2018
Amit Dhurandhar*, Pin-Yu Chen*, Ronny Luss, Chun-Chen Tu, Paishun Ting, Karthikeyan Shanmugam, and Payel Das (*equal contribution)
Preprints
Yu-Lin Tsai, Chia-Yi Hsu, Chia-Mu Yu, and Pin-Yu Chen, “Formalizing Generalization and Robustness of Neural Networks to Weight Perturbations,”
Chia-Yi Hsu, Pin-Yu Chen, Songtao Lu, Sijia Lu, and Chia-Mu Yu, “Adversarial Examples for Unsupervised Machine Learning Models,”
Chao-Han Huck Yang, I-Te Danny Hung, Yi Ouyang, and Pin-Yu Chen, “Causal Inference Q-Network: Toward Resilient Reinforcement Learning,”
Omid Aramoon, Pin-Yu Chen, Gang Qu, and Yuan Tian, “Meta Federated Learning,”
Yiqin Yu, Pin-Yu Chen, Yuan Zhou, and Jing Mei, “Adversarial Sample Enhanced Domain Adaptation: A Case Study on Predictive Modeling with Electronic Health Records,”
Rulin Shao, Zhouxing Shi, Jinfeng Yi, Pin-Yu Chen, and Cho-Jui Hsieh, “Robust Text CAPTCHAs Using Adversarial Examples,”
Pranay Sharma, Kaidi Xu, Sijia Liu, Pin-Yu Chen, Xue Lin, and Pramod K. Varshney, “Zeroth-Order Hybrid Gradient Descent: Towards A Principled Black-Box Optimization Framework,”
Samuel Hoffman, Vijil Chenthamarakshan, Kahini Wadhawan, Pin-Yu Chen, and Payel Das, “Optimizing Molecules using Efficient Queries from Property Evaluations,”
Orlando Romero, Subhro Das, Pin-Yu Chen, and Sérgio Pequito, “A Dynamical Systems Approach for Convergence of the Bayesian EM Algorithm,”
Minhao Cheng, Qi Lei, Pin-Yu Chen, Inderjit Dhillon, and Cho-Jui Hsieh, “CAT: Customized Adversarial Training for Improved Robustness,”
Jingkang Wang*, Tianyun Zhang*, Sijia Liu, Pin-Yu Chen, Jiacen Xu, Makan Fardad, and Bo Li, “Towards A Unified Min-Max Framework for Adversarial Exploration and Robustness,” (*equal contribution)
Amit Dhurandhar*, Tejaswini Pedapati*, Avinash Balakrishnan*, Pin-Yu Chen*, Karthikeyan Shanmugam, and Ruchir Puri, “Model Agnostic Contrastive Explanations for Structured Data,” (*equal contribution)
Ronny Luss*, Pin-Yu Chen*, Amit Dhurandhar*, Prasanna Sattigeri*, Karthikeyan Shanmugam, and Chun-Chen Tu, “Generating Contrastive Explanations with Monotonic Attribute Functions,” (*equal contribution)
U.S. Patents
[PA1] System and Methods for Automated Detection, Reasoning, and Recommendations for Resilient Cyber Systems
[PA2] Graph Similarity Analytics
[PA3] Contrastive explanations for interpreting deep neural networks
[PA4] Model Agnostic Contrastive Explanations for Structured Data
[PA5] Adversarial Input Identification using Reduced Precision Deep Neural Networks
[PA6] Framework for Certifying a lower bound on a robustness level of convolutional neural networks
[PA7] Computational creativity based on a tunable creativity control function of a model
[PA8] Efficient and secure gradient-free black box optimization
Technical Reports
[T11] Vijay Arya, Rachel KE Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C Hoffman, Stephanie Houde, Q Vera Liao, Ronny Luss, Aleksandra Mojsilović, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R Varshney, Dennis Wei, and Yunfeng Zhang. “One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques,”
[T10] Rise Ooi, Chao-Han Huck Yang, Pin-Yu Chen, Vìctor Eguìluz, Narsis Kiani, Hector Zenil, David Gomez-Cabrero, Jesper Tegnèr, “Controllability, Multiplexing, and Transfer Learning in Networks using Evolutionary Learning”
[T9] Sijia Liu, Pin-Yu Chen, Alfred Hero, and Indika Rajapakse, “Dynamic Network Analysis of the 4D Nucleome”
[T8] Sheng-Chun Kao*, Chao-Han Huck Yang*, Pin-Yu Chen, Xiaoli Ma, and Tushar Krishna, “Reinforcement Learning based Interconnection Routing for Adaptive Traffic Optimization,” poster paper at IEEE/ACM International Symposium on Networks-on-Chip (NOCS), 2019 (*equal contribution)
[T7] Chia-Yi Hsu, Pin-Yu Chen, and Chia-Mu Yu, “Characterizing Adversarial Subspaces by Mutual Information,” poster paper at AsiaCCS, 2019
[T6] Pin-Yu Chen, Sutanay Choudhury, Luke Rodriguez, Alfred O. Hero, and Indrajit Ray, “Enterprise Cyber Resiliency Against Lateral Movement: A Graph Theoretic Approach,” technical report for a book chapter in “Industrial Control Systems Security and Resiliency: Practice and Theory,” Springer, 2019
[T5] Sijia Liu and Pin-Yu Chen, “Zeroth-Order Optimization and Its Application to Adversarial Machine Learning,” IEEE Intelligent Informatics BULLETIN (invited paper)
[T4] Hongge Chen, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, Cho-Jui Hsieh, “Show-and-Fool: Crafting Adversarial Examples for Neural Image Captioning”
[T3] Yash Sharma and Pin-Yu Chen, “Bypassing Feature Squeezing by Increasing Adversary Strength”
[T2] Zhuolin Yang, Bo Li, Pin-Yu Chen, Dawn Song, “Towards Mitigating Audio Adversarial Perturbations”
[T1] Pin-Yu Chen, Meng-Hsuan Sung, and Shin-Ming Cheng, “Buffer Occupancy and Delivery Reliability Tradeoffs for Epidemic Routing”
Conference/Workshop Organizer:
[IEEE GlOBECOM 2020] Industrial Forum Co-Chair
[KDD 2019, 2020] Adversarial Learning Methods for Machine Learning and Data Mining
[IEEE GlobalSIP 2018] Signal Processing for Adversarial Machine Learning
[IEEE ICME 2018] Machine Learning and Artificial Intelligence for Multimedia Creation
Tutorial Presenter:
[CVPR 2021] Practical Adversarial Robustness in Deep Learning: Problems and Solutions
[IEEE HOST 2020] Security Issues in AI and Their Impacts on Hardware Security
[ECCV 2020] Adversarial Robustness of Deep Learning Models: Attack, Defense, Verification, and Beyond
[CVPR 2020] Zeroth Order Optimization: Theory and Applications to Deep Learning
[ICASSP 2020] Adversarial Robustness of Deep Learning Models: Attack, Defense and Verification
[IEEE BigData 2018] Recent Progress in Zeroth Order Optimization and Its Applications to Adversarial Robustness in Deep Learning
Service
Editorial board
- PLOS ONE
- KSII-TIIS (area editor)
- IEEE J-IOT (Guest),
Area Chair/Senior PC
ICML, AAAI, IJCAI
Featured conference reviewers
NuerIPS, ICML (AC), AAAI (Senior PC), ICLR, IJCAI (Senior PC), CVPR, ICDM, WWW, INFOCOM, GLOBECOM, ICC, WCNC, ACC, ICASSP, ICME; ACMMM
Featured journal reviewers
Proc. IEEE, IEEE T-SP, T-IP, J-STSP, T-SIPN, T-KDE, T-PAMI, J-SAC, ToN, T-WC, T-VT, CL, SPL, T-PDS, T-IFS, T-NNLS, WCM, WCL, J-IoT, SPL, T-CNS, ACCESS, J-ETCAS; PLOS ONE
Internship
Pacific Northwest National Laboratory (PNNL) - Data Science PhD Intern
action recommendations for real-time service degradation attacks
user segmentation and host hardening against lateral movement attacks
Fun and Proud Fact: My Erdos number is 4 (through two distinct paths)!!
Me -> Alfred Hero -> Wayne Stark -> Robert McEliece -> Paul Erdos
Me -> Pai-Shun Ting -> John. P. Hayes -> Frank Harary -> Paul Erdos