Journal Papers and Magazines

[J54] Ruoqi Liu, Pin-Yu Chen, and Ping Zhang, “CURE: A Pre-training Framework on Large-scale Patient Data for Treatment Effect Estimation,” Cell Patterns, 2024 <OSU_CURE> <AZO_Robotics_CURE>

[J53] Hongkang Li, Shuai Zhang, Yihua Zhang, Meng Wang, Sijia Liu, and Pin-Yu Chen, “How Does Promoting the Minority Fraction Affect Generalization? A Theoretical Study of One-hidden-layer Neural Network on Group Imbalance,” IEEE Journal of Selected Topics in Signal Processing, 2024

[J52] Ryan L'Abbate, Anthony D'Onofrio, Samuel Stein, Samuel Yen-Chi Chen, Ang Li, Pin-Yu Chen, Juntao Chen, and Ying Mao, “A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity,” IEEE Transactions on Quantum Engineering, 2024

[J51] Vijay Sadashivaiah, Keerthiram Murugesan, Ronny Luss, Pin-Yu Chen, Chris Sims, James Hendler, and Amit Dhurandhar, “To Transfer or Not to Transfer: Suppressing Concepts from Source Representations,” Transactions on Machine Learning Research, 2024

[J50] Jun Qi, Chao-Han Huck Yang, and Pin-Yu Chen, “QTN-VQC: An End-to-End Learning framework for Quantum Neural Networks,” Physica Scripta, 2023

[J49] Zichong Li, Pin-Yu Chen, Sijia Liu, Songtao Lu, and Yangyang Xu, “Stochastic Inexact Augmented Lagrangian Method for Nonconvex Expectation Constrained Optimization,” Computational Optimization and Applications, 2023

[J48] Elvin Lo and Pin-Yu Chen, “Understanding and Improving Zeroth-order Optimization Methods on AI-driven Molecule Optimization,” Digital Discovery, 2023

[J47] Bikram Sahoo, Sarwan Ali, Pin-Yu Chen, Murray Patterson, and Alexander Zelikovsky, “Assessing the Resilience of Machine Learning Classification Algorithms on SARS-CoV-2 Genome Sequences Generated with Long-Read Specific Errors,” Biomolecules, 2023

[J46] Jokin Labaien, Tsuyoshi Idé, Pin-Yu Chen, Ekhi Zugasti, and Xabier De Carlos, “Diagnostic spatio-temporal transformer with faithful encoding,” Knowledge-Based Systems, 2023

[J45] Sarwan Ali, Bikram Sahoo, Alexander Zelikovskiy, Pin-Yu Chen, and Murray Patterson, “Benchmarking Machine Learning Robustness in Covid-19 Genome Sequence Classification,” Nature Scientific Reports, 2023

[J44] Pin-Yu Chen and Payel Das, “AI Maintenance: A Robustness Perspective,” IEEE Computer Magazine, 2023

[J43] Jun Qi*, Chao-Han Huck Yang, Pin-Yu Chen*, and Min-Hsiu Hsieh*, “Theoretical Error Performance Analysis for Variational Quantum Circuit Based Functional Regression,” npj Quantum Information, 2023 (*corresponding authors)

[J42] Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen, and Javier Tejedor, “Exploiting Low-Rank Tensor-Train Deep Neural Networks Based on Riemannian Gradient Descent With Illustrations of Speech Processing,” IEEE Transactions on Audio, Speech and Language Processing, 2023

[J41] Farhad Mohsin, Ao Liu, Pin-Yu Chen, Francesca Rossi, and Lirong Xia, “Learning to Design Fair and Private Voting Rules,” Journal of Artificial Intelligence Research, 2023

[J40] Rulin Shao, Zhouxing Shi, Jinfeng Yi, Pin-Yu Chen, and Cho-Jui Hsieh, “On the Adversarial Robustness of Vision Transformers,” Transactions on Machine Learning Research, 2022 <ViT_AdvRobustness_video>

[J39] Arpan Mukherjee, Ali Tajer, Pin-Yu Chen, and Payel Das, “Active Sampling of Multiple Sources for Sequential Estimation,” IEEE Transactions on Signal Processing, 2022

[J38] Yunchuan Liu, Lei Yang, Amir Ghasemkhani, Hanif Livani, Virgilio A. Centeno, Pin-Yu Chen, and Junshan Zhang, “Robust Event Classification Using Imperfect Real-world PMU Data,” IEEE Internet of Things Journal, 2022

[J37] Samuel Hoffman, Vijil Chenthamarakshan, Kahini Wadhawan, Pin-Yu Chen*, and Payel Das*, “Optimizing Molecules using Efficient Queries from Property Evaluations,” Nature Machine Intelligence, 2022 (*corresponding authors) <QMO_code> <IBM_QMO> <IBM Pat Goldberg Memorial Best Paper Award (2023)>

[J36] Payel Das, Tom Sercu, Kahini Wadhawan, Inkit Padhi, Sebastian Gehrmann, Flaviu Cipcigan, Vijil Chenthamarakshan, Hendrik Strobelt, Cicero dos Santos, Pin-Yu Chen, Yi Yan Yang, Jeremy Tan, James Hedrick, Jason Crain, and Aleksandra Mojsilovic, “Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics,” Nature Biomedical Engineering, 2021 <AMP_code> <VentureBeat_CLASS> <New_Atlas_CLASS> <IBM_CLASS> <Axios_CLASS> <WRAL_TechWire_CLASS> <Psychology_Today_CLASS> <ACS_CLASS> <Chemistry_World_CLASS> <Technicity_CLASS> <VOX_CLASS> <IBM Pat Goldberg Memorial Best Paper Award (2022)>

[J35] Sijia Liu, Pin-Yu Chen, Bhavya Kailkhura, Gaoyuan Zhang, Alfred Hero, and Pramod K. Varshney, “A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning,” IEEE Signal Processing Magazine, 2020

[J34] Shuai Zhang, Meng Wang, Jinjun Xiong, Sijia Liu, and Pin-Yu Chen, “Improved Linear Convergence of Training CNNs With Generalizability Guarantees: A One-Hidden-Layer Case,” IEEE Transactions on Neural Networks and Learning Systems, 2020

[J33] 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,  “AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models,” Journal of Machine Learning Research, 2020  (alphabetical order)

[J32] Samuel Yen-Chi Chen, Chao-Han Huck Yang, Jun Qi, Pin-Yu Chen, Xiaoli Ma, and Hsi-Sheng Goan, “Variational Quantum Circuits for Deep Reinforcement Learning,”  IEEE Access, 2020 <QuantumDRL_code>

[J31] Zhao Qin*, Lingfei Wu*, Hui Sun*, Siyu Huo, Tengfei Ma, Eugene Lim, Pin-Yu Chen, Benedetto Marelli, and Markus Buehler, “Artificial intelligence method to design and fold alpha-helix structural proteins from the primary amino acid sequence,” Extreme Mechanics Letters, 2020 (*equal contribution) <MNNN_code> <MIT_News_SARS-CoV-2> <Academic_Times_MNNN>

[J30] Zhining Liu, Weiyi Liu, Pin-Yu Chen, Chenyi Zhuang and Chengyun Song, “hpGAT: High-order Proximity Informed Graph Attention Network,” IEEE Access, 2019

[J29] Chun-Chen Tu, Pin-Yu Chen, and Naisyin Wang, “Improving Prediction Efficacy through Abnormality Detection and Data Preprocessing,” IEEE Access, 2019

[J28] Vachik S. Dave, Baichuan Zhang, Pin-Yu Chen, Mohammad Al Hasan, “Neural-Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding,” Data Science and Engineering, 2019 <Neural-Brane_code>

[J27] Weiyi Liu, Zhining Liu, Fucai Yu, Pin-Yu Chen, Toyotaro Suzumura, Guangmin Hu, “A Scalable Attribute-aware Network Embedding System,” Neurocomputing, 2019

[J26] Weiyi Liu, Hal Cooper, Min-Hwan Oh, Pin-Yu Chen, Sailung Yeung,  Fucai Yu, Toyotaro Suzumura, Guangmin Hu, “Learning Graph Topological Features via GAN,” IEEE Access, 2019

[J25] Sijia Liu, Haiming Chen, Scott Ronquist, Laura Seaman, Nicholas Ceglia, Walter Meixner, Pin-Yu Chen, Gerald Higgins, Pierre Baldi, Steve Smale, Alfred Hero, Lindsey Muir, Indika Rajapakse, “Genome Architecture Mediates Transcriptional Control of Human Myogenic Reprogramming,” iScience, 2018

[J24] Pin-Yu Chen, Chun-Chen Tu, Paishun Ting, Ya-Yun Luo, Danai Koutra, and Alfred Hero, “Identifying Influential Links for Event Propagation on Twitter: A Network of Networks Approach,” IEEE Transactions on Signal and Information Processing over Networks, 2018

[J23] Pin-Yu Chen, Meng-Hsuan Sung, and Shin-Ming Cheng, “Analysis of Data Dissemination and Control in Social Internet of Vehicles,” IEEE Internet of Things Journal, vol. 5, no. 4, pp. 2467-2477, Aug. 2018 

[J22] Pin-Yu Chen and Alfred Hero, “Phase Transitions and a Model Order Selection Algorithm for Spectral Graph Clustering,” IEEE Transactions on Signal Processing, vol. 66, no. 13, pp. 3407-3420, Jul. 2018 <AMOS code> <slides>

[J21] Sijia Liu, Pin-Yu Chen, and Alfred Hero, “Accelerated Distributed Dual Averaging over Evolving Networks of Growing Connectivity,”  IEEE Transactions on Signal Processing, vol. 66, no. 7, pp. 1845-1859, Apr. 2018

[J20] Pin-Yu Chen, Baichuan Zhang, and Mohammad Al Hasan, “Incremental Eigenpair Computation for Graph Laplacian Matrices: Theory and Applications,” Social Network Analysis and Mining, vol. 8, no. 1, Jan. 2018

[J19] P.-Y. Chen, S.-M. Cheng, and H.-Y. Hsu, “Analysis of Information Delivery Dynamics in Cognitive Sensor Networks Using Epidemic Models,”  IEEE Internet of Things Journal (special issue on Cognitive Internet of Things), vol. 5, no. 4, pp. 2333-2342, Aug. 2018

[J18] P.-Y. Chen and A. O. Hero, “Multilayer Spectral Graph Clustering via Convex Layer Aggregation: Theory and Algorithms,” IEEE Transactions on Signal and Information Processing over Networks (joint special issue on graph signal processing for J-STSP and T-SIPN), vol. 3, no. 3, pp. 553-567, Sep. 2017 <slides> <MIMOSA_code>

[J17] P.-Y. Chen and S. Liu, “Bias-Variance Tradeoff of Graph Laplacian Regularizer,” IEEE Signal Processing Letters,  vol. 24, no. 8, pp. 1118-1122, Aug. 2017

[J16] S.-M. Cheng, P.-Y. Chen, C.-C. Lin, and H.-C. Hsiao, “Traffic-aware Patching for Cyber Security in Mobile IoT,” IEEE Communications Magazine, vol. 55, no 7, pp. 29-35, Jul. 2017 (highlighted by IEEE Xplore Spotlight, Dec. 2017)

[J15] P.-Y. Chen, C.-C. Lin, S.-M. Cheng, C.-Y. Huang, and H.-C. Hsiao, “Decapitation via Digital Epidemics: A Bio-Inspired Transmissive Attack,” IEEE Communications Magazine, vol. 54, no. 6, pp. 75-81, Jun. 2016

[J14] P.-Y. Chen and A. O. Hero, “Deep Community Detection,” IEEE Transactions on Signal Processing, vol. 63, no. 21, pp. 5706-5719, Nov. 2015 <DCD_code>

[J13] P.-Y. Chen, S.-M. Cheng, P.-S. Ting, C.-W. Lien, and F.-J Chu, “When Crowdsourcing Meets Mobile Sensing: A Social Network Perspective,” IEEE Communications Magazine, vol. 53, no. 10, pp. 157-163, Oct. 2015

[J12] P.-Y. Chen and A. O. Hero, “Phase Transitions in Spectral Community Detection,” IEEE Transactions on Signal Processing, vol. 63, no. 16, pp. 4339-4347, Aug. 2015

[J11] P.-Y. Chen and A. O. Hero, “Universal Phase Transition in Community Detectability under a Stochastic Block Model,” Physical Review E, vol. 91, no. 3, pp. 032804, Mar. 2015

[J10] P.-Y. Chen and S.-M. Cheng, “Sequential Defense against Random and Intentional Attacks in Complex Networks,” Physical Review E, vol. 91, no. 2, pp. 022805, Feb. 2015

[J9] P.-Y. Chen and A. O. Hero, “Assessing and Safeguarding Network Resilience to Centrality Attacks,” IEEE Communications Magazine, vol. 52, no. 11, pp. 138-143, Nov. 2014 [Supplementary File] [Correction: For ego centrality, I means the matrix of ones]

[J8] P.-Y. Chen, S.-M. Cheng, and K.-C. Chen, “Information Fusion to Defend Intentional Attack in Internet of Things,” IEEE Internet of Things Journal, vol. 1, no. 4, pp. 337-348, Aug. 2014

[J7] P.-Y. Chen, S.-M. Cheng, and K.-C. Chen, “Optimal Control of Epidemic Information Dissemination over Networks,” IEEE Transactions on Cybernetics, vol. 44, no. 12, pp. 2316-2328, Dec. 2014

[J6] S.-M. Cheng, V. Karyotis, P.-Y. Chen, K.-C. Chen, and S. Papavassiliou, “Diffusion Models for Information Dissemination Dynamics in Wireless Complex Networks,” Journal of Complex Systems, 2013

[J5] P.-Y. Chen, S.-M. Cheng, and K.-C. Chen, “Smart Attacks in Smart Grid Communication Networks,” IEEE Communications Magazine, vol. 50, no. 8, pp. 24–29, Aug. 2012  (highlighted by IEEE COMSOC Technology News, Sep. 2012)

[J4] W. C. Ao, P.-Y. Chen, and K.-C. Chen, “Rate-Reliability-Delay Trade-off of Multipath Transmission Using Network Coding,” IEEE Transactions on Vehicular Technology, vol.61, no.5, pp.2336-2342, Jun 2012

[J3] P.-Y. Chen, W. C. Ao, and K.-C. Chen, “Rate-Delay Enhanced Multipath Transmission Schem via Network Coding in Multihop Networks,” IEEE Communications Letters, vol.16, no.3, pp.281-283, March 2012

[J2] S.-M. Cheng, P.-Y. Chen, and K.-C. Chen, “Ecology of Cognitive Radio Ad Hoc Networks,” IEEE Communications Letters, vol.15, no.7, pp.764-766, July 2011

[J1] S.-M. Cheng, W. C. Ao, P.-Y. Chen, and K.-C. Chen, “On Modeling Malware Propagation in Generalized Social Networks,” IEEE Communications Letters, vol.15, no.1, pp.25-27, Jan. 2011

 Conference Papers and Workshop Papers (with Proceedings)

[215] Megh Thakkar, Quentin Fournier, Matthew D Riemer, Pin-Yu Chen, Amal Zouaq, Payel Das, and Sarath Chandar, “A Deep Dive into the Trade-Offs of Parameter-Efficient Preference Alignment Techniques,” Annual Meeting of the Association for Computational Linguistics (ACL), 2024

[214] Chaoyi Zhu, Jeroen Galjaard, Pin-Yu Chen, and Lydia Y. Chen, “Duwak: Dual Watermarks in Large Language Models,” Annual Meeting of the Association for Computational Linguistics (ACL), 2024 (Findings) <Duwak_code>

[213] Changchang Yin, Pin-Yu Chen, Bingsheng Yao, Dakuo Wang, Jeffrey M Caterino, and Ping Zhang, “SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing,” ACM SIGKDD International Conference on Knowledge Discovery and Data Mining  (KDD), 2024 (Applied Data Science Track)

[212] Farhad Mohsin, Qishen Han, Sikai Ruan, Pin-Yu Chen, Francesca Rossi, and Lirong Xia, “Computational Complexity of Verifying the Group No-show Paradox,” International Joint Conference on Artificial Intelligence (IJCAI), 2024

[211] Zhi-Yi Chin, Chieh-Ming Jiang, Ching-Chun Huang, Pin-Yu Chen, and Wei-Chen Chiu, “Prompting4Debugging: Red-Teaming Text-to-Image Diffusion Models by Finding Problematic Prompts,” International Conference on Machine Learning (ICML), 2024 <P4D_Page> <P4D_code>

[210] Ching-Yun Ko, Pin-Yu Chen, Payel Das, Jeet Mohapatra, and Luca Daniel, “What Would Gauss Say About Representations? Probing Pretrained Image Models using Synthetic Gaussian Benchmarks,” International Conference on Machine Learning (ICML), 2024

[209] Zhiyuan He, Yijun Yang, Pin-Yu Chen, Qiang Xu, and Tsung-Yi Ho,  “Be Your Own Neighborhood: Detecting Adversarial Examples by the Neighborhood Relations Built on Self-Supervised Learning,” International Conference on Machine Learning (ICML), 2024

[208] Asterios Tsiourvas, Wei Sun, Georgia Perakis, Pin-Yu Chen, and Yada Zhu, “Learning Optimal Projection for Forecast Reconciliation of Hierarchical Time Series,” International Conference on Machine Learning (ICML), 2024

[207] Hongkang Li, Meng Wang, Songtao Lu, Xiaodong Cui, and Pin-Yu Chen, “How Do Nonlinear Transformers Learn and Generalize in In-Context Learning?,” International Conference on Machine Learning (ICML), 2024

[206] Shuai Zhang, Heshan Devaka Fernando, Miao Liu, Keerthiram Murugesan, Songtao Lu, Pin-Yu Chen, Tianyi Chen, and Meng Wang, “SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning,” International Conference on Machine Learning (ICML), 2024

[205] Mohammed Nowaz Rabbani Chowdhury, Meng Wang, Kaoutar El Maghraoui, Naigang Wang, Pin-Yu Chen, and Christopher Carothers, “A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts,” International Conference on Machine Learning (ICML), 2024

[204] Yihua Zhang, Pingzhi Li, Junyuan Hong, Jiaxiang Li, Yimeng Zhang, Wenqing Zheng, Pin-Yu Chen, Jason D. Lee, Wotao Yin, Mingyi Hong, Zhangyang Wang, Sijia Liu, and Tianlong Chen, “Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark,” International Conference on Machine Learning (ICML), 2024 <ZO-LLM_code>

[203] Hongkang Li, Meng Wang, Tengfei Ma, Sijia Liu, Zaixi Zhang, and Pin-Yu Chen, “What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding,” International Conference on Machine Learning (ICML), 2024

[202] Payel Das, Subhajit Chaudhury, Elliot Nelson, Igor Melnyk, Sarath Swaminathan, Sihui Dai, Aurélie Lozano, Georgios Kollias, Vijil Chenthamarakshan, Jiří, Navrátil, Soham Dan, and Pin-Yu Chen,  “Larimar: Large Language Models with Episodic Memory Control,” International Conference on Machine Learning (ICML), 2024 <MARKTECHPOST_Larimar> <Larimar_code>

[201] Lichao Sun, Yue Huang, Haoran Wang, Siyuan Wu, Qihui Zhang, Chujie Gao, Yixin Huang, Wenhan Lyu, Yixuan Zhang, Xiner Li, Zhengliang Liu, Yixin Liu, Yijue Wang, Zhikun Zhang, Bhavya Kailkhura, Caiming Xiong, Chao Zhang, Chaowei Xiao, Chunyuan Li, Eric Xing, Furong Huang, Hao Liu, Heng Ji, Hongyi Wang, Huan Zhang, Huaxiu Yao, Manolis Kellis, Marinka Zitnik, Meng Jiang, Mohit Bansal, James Zou, Jian Pei, Jian Liu, Jianfeng Gao, Jiawei Han, Jieyu Zhao, Jiliang Tang, Jindong Wang, John Mitchell, Kai Shu, Kaidi Xu, Kai-Wei Chang, Lifang He, Lifu Huang, Michael Backes, Neil Zhenqiang Gong, Philip S. Yu, Pin-Yu Chen, Quanquan Gu, Ran Xu, Rex Ying, Shuiwang Ji, Suman Jana, Tianlong Chen, Tianming Liu, Tianyi Zhou, Willian Wang, Xiang Li, Xiangliang Zhang, Xiao Wang, Xing Xie, Xun Chen, Xuyu Wang, Yan Liu, Yanfang Ye, Yinzhi Cao, and Yue Zhao, “TrustLLM: Trustworthiness in Large Language Models,” International Conference on Machine Learning (ICML), 2024 <TrustLLM_project_page>

[200] Diganta Misra*, Muawiz Chaudhary*, Agam Goyal*, Bharat Runwal*, and Pin Yu Chen, “Uncovering the Hidden Cost of Model Compression,” Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024 (*equal contribution) <Reprog_LTH_code>

[199] Erh-Chung Chen, Pin-Yu Chen, I-Hsin Chung, and Che-rung Lee, “Overload: Latency Attacks on Object Detection for Edge Devices,” Conference on Computer Vision and Pattern Recognition (CVPR), 2024

[198] Saiteja Utpala, Alex Gu, and Pin Yu Chen, “Language Agnostic Code Embeddings,” Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2024

[197] Chi Hong, Robert Birke, Pin-Yu Chen, and Lydia Y. Chen, “On Dark Knowledge for Distilling Generators,” Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2024

[196] Xiangyu Qi, Yi Zeng, Tinghao Xie, Pin-Yu Chen, Ruoxi Jia, Prateek Mittal, and Peter Henderson, “Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!,” International Conference on Learning Representations (ICLR), 2024 (oral presentation; top 1.2%) <LLM-Tuning-Safety_Page> <Stanford_HAI_policy_brief> <New York Times_finetune_safety> <VentureBeat_finetune_safety> <WinBuzzer_finetune_safety> <TS2_finetune_safety> <TheRegister_finetune_safety> <Info_Lopare_finetune_safety> <PCMag_safety> <Silicon_Sonnets_safety> <Stanford_HAI_finetune_safety> <NewScientist_finetune_safety>

[C195] Yu-Lin Tsai, Chia-Yi Hsu, Chulin Xie, Chih-Hsun Lin, Jia-You Chen, Bo Li, Pin-Yu Chen, Chia-Mu Yu, and Chun-Ying Huang, “Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?” International Conference on Learning Representations (ICLR), 2024 <Ring-A-Bell_code>

[C194] Hsi-Ai Tsao, Lei Hsiung, Pin-Yu Chen, Sijia Liu, and Tsung-Yi Ho, “AutoVP: An Automated Visual Prompting Framework and Benchmark, International Conference on Learning Representations (ICLR), 2024 <AutoVP_code>

[C193] Yan Liu, Yu Liu, Xiaokang Chen, Pin-Yu Chen, Daoguang Zan, Min-Yen Kan, and Tsung-Yi Ho, “The Devil is in the Neurons: Interpreting and Mitigating Social Biases in Language Models,” International Conference on Learning Representations (ICLR), 2024

[C192] Ming-Yu Chung, Sheng-Yen Chou, Chia-Mu Yu, Pin-Yu Chen, Sy-Yen Kuo, and Tsung-Yi Ho, “Rethinking Backdoor Attacks on Dataset Distillation: A Kernel Method Perspective,” International Conference on Learning Representations (ICLR), 2024

[C191] Chen Chen, Ruizhe Li, Yuchen Hu, Sabato Marco Siniscalchi, Pin-Yu Chen, Ensiong Chng, and Chao-Han Huck Yang, “It's Never Too Late: Fusing Acoustic Information into Large Language Models for Automatic Speech Recognition,” International Conference on Learning Representations (ICLR), 2024

[C190] Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y. Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, and Qingsong Wen, “Time-LLM: Time Series Forecasting by Reprogramming Large Language Models,” International Conference on Learning Representations (ICLR), 2024 <Time-LLM_code> <ABCP_Highlight>

[C189] Yuchen Hu, Chen Chen, Chao-Han Huck Yang, Ruizhe Li, Chao Zhang, Pin-Yu Chen, and EnSiong Chng, “Large Language Models are Efficient Learners of Noise-Robust Speech Recognition,” International Conference on Learning Representations (ICLR), 2024 (spotlight presentation, top 5%) <RobstGER_code>

[C188] Zishen Wan, Nandhini Chandramoorthy, Karthik Swaminathan, Pin-Yu Chen, Kshitij Bhardwaj, Vijay Janapa Reddi, and Arijit Raychowdhury, “MulBERRY: Enabling Bit-Error Robustness for Energy-Efficient Multi-Agent Autonomous Systems,” International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2024

[C187] Heshan Fernando, Lisha Chen, Songtao Lu, Pin-Yu Chen, Miao Liu, Subhajit Chaudhury, Keerthiram Murugesan, Gaowen Liu, Meng Wang, and Tianyi Chen, “Variance Reduction Can Improve Trade-Off in Multi-Objective Learning,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024

[C186] Ming-Chang Chiu, Yingfei Wang, Yen-Ju Kuo, and Pin-Yu Chen, “DDI-CoCo: A Dataset For Understanding the Effect of Color Contrast In Machine-Assisted Skin Disease Detection,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024

[C185] Andrew Geng and Pin-Yu Chen, “Model Reprogramming Outperforms Fine-tuning on Out-of-distribution Data in Text-Image Encoders,” IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 2024 <Reprogrammer_code>

[C184] Chulin Xie, Pin-Yu Chen, Qinbin Li, Arash Nourian, Ce Zhang, and Bo Li, “Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM,” IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 2024

[C183] Pin-Yu Chen, “Model Reprogramming: Resource-Efficient Cross-Domain Machine Learning,” AAAI Conference on Artificial Intelligence (AAAI), 2024 (senior member presentation track) <Model_Reprogramming_Repo>

[C182] Shengwei An, Sheng-Yen Chou, Kaiyuan Zhang, Qiuling Xu, Guanhong Tao, Guangyu Shen, Siyuan Cheng, Shiqing Ma, Pin-Yu Chen, Tsung-Yi Ho, and Xiangyu Zhang, “Elijah: Eliminating Backdoors Injected in Diffusion Models via Distribution Shift,” AAAI Conference on Artificial Intelligence (AAAI), 2024

[C181] Zhi-Yi Chin, Chieh-Ming Jiang, Ching-Chun Huang, Pin-Yu Chen, and Wei-Chen Chiu, “Masking Improves Contrastive Self-Supervised Learning for ConvNets, and Saliency Tells You Where,” IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024

[C180] Xiaomeng Hu, Pin-Yu Chen, and Tsung-Yi Ho, “RADAR: Robust AI-Text Detection via Adversarial Learning,” Neural Information Processing Systems (NeurIPS), 2023 <RADAR_page_demo> <IBM_Blog_AI_Forensics> <RADAR_NIST_report>

[C179] Sheng-Yen Chou, Pin-Yu Chen, and Tsung-Yi Ho, “VillanDiffusion: A Unified Backdoor Attack Framework for Diffusion Models,” Neural Information Processing Systems (NeurIPS), 2023 <VillanDiffusion_code>

[C178] Shuai Zhang, Hongkang Li, Meng Wang, Miao Liu, Pin-Yu Chen, Songtao Lu, Sijia Liu, Keerthiram Murugesan, and Subhajit Chaudhury, “On the Convergence and Sample Complexity Analysis of Deep Q-Networks with $\epsilon$-Greedy Exploration,” Neural Information Processing Systems (NeurIPS), 2023

[C177] Yan Liu, Xiaokang Chen, Yan Gao, Zhe Su, Fengji Zhang, Daoguang Zan, Jian-Guang Lou, Pin-Yu Chen, and Tsung-Yi Ho, “Uncovering and Quantifying Social Biases in Code Generation,” Neural Information Processing Systems (NeurIPS), 2023 <Code-Bias_code>

[C176] Chen Chen, Yuchen Hu, Chao-Han Huck Yang, Sabato Marco Siniscalchi, Pin-Yu Chen, and Ensiong Chng, “HyPoradise: An Open Baseline for Generative Speech Recognition with Large Language Models,” Neural Information Processing Systems (NeurIPS), 2023 (Datasets and Benchmarks Track) <HyPoradise_code>

[C175] Saiteja Utpala, Sara Hooker, and Pin-Yu Chen, “Locally Differentially Private Document Generation Using Zero Shot Prompting,” Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023 (Findings) <DP_prompt_code>

[C174] Sarwan Ali, Pin-Yu Chen, and Murray Patterson, “Unveiling the Robustness of Machine Learning Models in Classifying COVID-19 Spike Sequences,” International Symposium on Bioinformatics Research and Applications (ISBRA), 2023 

[C173] Chulin Xie, Yunhui Long, Pin-Yu Chen, Qinbin Li, Sanmi Koyejo, and Bo Li, “Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks,” ACM Conference on Computer and Communications Security (CCS), 2023

[C172] Jiajin Zhang, Hanqing Chao, Amit Dhurandhar, Pin-Yu Chen, Ali Tajer, Yangyang Xu, and Pingkun Yan, “Spectral Adversarial MixUp for Few-Shot Unsupervised Domain Adaptation,”  International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2023

[C171] Yizhe Li, Yu-Lin Tsai, Chia-Mu Yu, Pin-Yu Chen, and Xuebin Ren, “Exploring the Benefits of Visual Prompting in Differential Privacy,” International Conference on Computer Vision (ICCV), 2023

[C170] Yihua Zhang, Ruisi Cai, Tianlong Chen, Guanhua Zhang, Huan Zhang, Pin-Yu Chen, Shiyu Chang, Zhangyang Wang, and Sijia Liu, “Robust Mixture-of-Expert Training for Convolutional Neural Networks,” International Conference on Computer Vision (ICCV), 2023 (oral presentation)

[C169] Ming-Chang Chiu, Pin-Yu Chen, and Xuezhe Ma, “Better May Not Be Fairer: A Study on Subgroup Discrepancy in Image Classification” International Conference on Computer Vision (ICCV), 2023

[C168] Tsun-An Hsieh, Chao-Han Huck Yang, Pin-Yu Chen, Sabato Marco Siniscalchi, and Yu Tsao, “Inference and Denoise: Causal Inference-based Neural Speech Enhancement,” IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2023 (oral presentation) 

[C167] Hsin-Ju Lin, Tsu-Chun Chung, Ching-chun Hsiao, Pin-Yu Chen, Wei-Chen Chiu, and Ching-Chun Huang, “MENTOR: Multilingual Text Detection Toward Learning by Analogy,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023

[C166] Hao Yen, Pin-Jui Ku, Chao-Han Huck Yang, Hu Hu, Sabato Marco Siniscalchi, Pin-Yu Chen, and Yu Tsao, “Neural Model Reprogramming with Similarity Based Mapping for Low-Resource Spoken Command Classification,” INTERSPEECH Conference (INTERSPEECH), 2023 <INTERSPEECH_2023_Best_Student_Paper_Award_Finalist>

[C165] Chao-Han Huck Yang, Zhengling Qi, Yifan Cui, and Pin-Yu Chen, “Pessimistic Model Selection for Offline Deep Reinforcement Learning,” The Conference on Uncertainty in Artificial Intelligence (UAI), 2023

[C164] Yihao Xue, Siddharth Joshi, Eric Gan, Pin-Yu Chen, and Baharan Mirzasoleiman, “Which Features are Learnt by Contrastive Learning? On the Role of Simplicity Bias in Class Collapse and Feature Suppression,” International Conference on Machine Learning (ICML), 2023 (oral presentation) 

[C163] Igor Melnyk, Vijil Chenthamarakshan, Pin-Yu Chen, Payel Das, Amit Dhurandhar, Inkit Padhi, and Devleena Das, “Reprogramming Pretrained Language Models for Antibody Sequence Infilling,” International Conference on Machine Learning (ICML), 2023

[C162] Minhao Cheng, Rui Min, Haochen Sun, and Pin-Yu Chen, “Identification of the Adversary from a Single Adversarial Example,” International Conference on Machine Learning (ICML), 2023

[C161] Yonggui Yan, Jie Chen, Pin-Yu Chen, Xiaodong Cui, Songtao Lu, and Yangyang Xu, “Compressed Decentralized Proximal Stochastic Gradient Method for Nonconvex Composite Problems with Heterogeneous Data,” International Conference on Machine Learning (ICML), 2023

[C160] Sihui Dai, Saeed Mahloujifar, Chong Xiang, Vikash Sehwag, Pin-Yu Chen, and Prateek Mittal, “MultiRobustBench: Benchmarking Robustness Against Multiple Attacks,” International Conference on Machine Learning (ICML), 2023

[C159] Mohammed Nowaz Rabbani Chowdhury, Shuai Zhang, Meng Wang, Sijia Liu, and Pin-Yu Chen, “Patch-level Routing in Mixture-of-Experts is Provably Sample-efficient for Convolutional Neural Networks,” International Conference on Machine Learning (ICML), 2023 (oral presentation) 

[C158] Sarwan Ali, Babatunde Bello, Prakash Chourasia, Ria Thazhe Punathil, Pin-Yu Chen, Imdad Ullah Khan, and Murray Patterson, “Virus2Vec: Viral Sequence Classification Using Machine Learning,” Conference on Health, Inference, and Learning (CHIL), 2023 (oral presentation)

[C157] Jia-Hong Huang, Chao-Han Huck Yang, Pin-Yu Chen, Min-Hung Chen, and Marcel Worring, “Causalainer: Causal Explainer for Automatic Video Summarization,” CVPR Workshop on New Frontiers in Visual Language Reasoning: Compositionality, Prompts, and Causality, 2023

[C156] Sheng-Yen Chou, Pin-Yu Chen, and Tsung-Yi Ho, “How to Backdoor Diffusion Models?,” Conference on Computer Vision and Pattern Recognition (CVPR), 2023 <Best Paper Award at ICLR 2023 BANDS Workshop> <BadDiffusion_code> <VentureBeat_BadDiffusion> <IBM_blog_BadDiffusion> <TEXAL_BadDiffusion> <Threat_Prompt_BadDiffusion>

[C155] Lei Hsiung, Yun-Yun Tsai, Pin-Yu Chen, and Tsung-Yi Ho, “Towards Compositional Adversarial Robustness: Generalizing Adversarial Training to Composite Semantic Perturbations,” Conference on Computer Vision and Pattern Recognition (CVPR), 2023 <Composite_Adv_code>

[C154] Aochuan Chen, Yuguang Yao, Pin-Yu Chen, Yihua Zhang, and Sijia Liu, “Understanding and Improving Visual Prompting: A Label-Mapping Perspective,” Conference on Computer Vision and Pattern Recognition (CVPR), 2023 <ILMVP_page>

[C153] Zishen Wan, Nandhini Chandramoorthy, Karthik Swaminathan, Pin-Yu Chen, Vijay Janapa Reddi, and Arijit Raychowdhury, “BERRY: Bit Error Robustness for Energy-Efficient Reinforcement Learning-Based Autonomous Systems,” The Design Automation Conference (DAC), 2023

[C152] Neel Bhandari and Pin-Yu Chen, “Lost In Translation: Generating Adversarial Examples Robust to Round-Trip Translation,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023 <NMT_attack_code>

[C151] Aochuan Chen*, Peter Lorenz*, Yuguang Yao, Pin-Yu Chen, and Sijia Liu, “Visual Prompting for Adversarial Robustness,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023 (*equal contribution) <Top 3% Paper Recognition>

[C150] Jhih-Cing Huang, Yu-Lin Tsai, Chao-Han Huck Yang, Cheng-Fang Su, Chia-Mu Yu, Pin-Yu Chen, and Sy-Yen Kuo, “Certified Robustness of Quantum Classifiers against Adversarial Examples through Quantum Noise,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023

[C149] Yun-Ning Hung, Chao-Han Huck Yang, Pin-Yu Chen, and Alexander Lerch, “Low-Resource Music Genre Classification with Cross-Modal Neural Model Reprogramming,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023

[C148] Dennis Wei, Haoze Wu, Min Wu, Pin-Yu Chen, Clark Barrett, and Eitan Farchi, “Convex Bounds on the Softmax Function with Applications to Robustness Verification,” International Conference on Artificial Intelligence and Statistics (AISTATS), 2023 <softmax_bound_video>

[C147] Farhad Mohsin, Qishen Han, Sikai Ruan, Pin-Yu Chen, Francesca Rossi, and Lirong Xia, “Computational Complexity of Verifying the Group No-show Paradox,” International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2023 (Extended Abstracts)

[C146] Hongkang Li, Meng Wang, Sijia Liu, and Pin-Yu Chen, “A Theoretical Understanding of Shallow Vision Transformers: Learning, Generalization, and Sample Complexity,” International Conference on Learning Representations (ICLR), 2023

[C145] Shuai Zhang, Meng Wang, Pin-Yu Chen, Sijia Liu, Songtao Lu, and Miao Liu, “Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks,” International Conference on Learning Representations (ICLR), 2023

[C144] Kaiyuan Zhang, Guanhong Tao, Qiuling Xu, Siyuan Cheng, Shengwei An, Yingqi Liu, Shiwei Feng, Guangyu Shen, Pin-Yu Chen, Shiqing Ma, and Xiangyu Zhang, “FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning,” International Conference on Learning Representations (ICLR), 2023 <Best Paper Award at ECCV 2022 AROW Workshop> <Purdue_News>

[C143] Pin-Yu Chen and Sijia Liu, “Holistic Adversarial Robustness of Deep Learning Models,” AAAI Conference on Artificial Intelligence (AAAI), 2023 (senior member presentation track)

[C142] Jiajin Zhang, Hanqing Chao, Amit Dhurandhar, Pin-Yu Chen, Ali Tajer, Yangyang Xu, and Pingkun Yan, “When Neural Networks Fail to Generalize? A Model Sensitivity Perspective,” AAAI Conference on Artificial Intelligence (AAAI), 2023 <SADA_code>

[C141] Huzaifa Arif, Alex Gittens, and Pin-Yu Chen, “Reprogrammable-FL: Improving Utility-Privacy Tradeoff in Federated Learning via Model Reprogramming,” IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 2023 <Reprogrammable-FL_code>

[C140] Washington Garcia, Pin-Yu Chen, Hamilton Scott Clouse, Somesh Jha, and Kevin R. B. Butler, “Less is More: Dimension Reduction Finds On-Manifold Adversarial Examples in Hard-Label Attacks,” IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 2023

[C139] Chao-Han Huck Yang, I-Te Danny Hung, Yi-Chieh Liu, and Pin-Yu Chen, “Treatment Learning Causal Transformer for Noisy Image Classification,” IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023 <TLT_code>

[C138] Zhixu Du, Jingwei Sun, Ang Li, Pin-Yu Chen, Jianyi Zhang, Hai "Helen" Li, and Yiran Chen, “Rethinking Normalization Methods in Federated Learning,” Proceedings of the 3rd International Workshop on Distributed Machine Learning (DistributedML), 2022

[C137] Rulin Shao, Zhouxing Shi, Jinfeng Yi, Pin-Yu Chen, and Cho-Jui Hsieh, “Robust Text CAPTCHAs Using Adversarial Examples,” IEEE International Conference on Big Data (Big Data), 2022

[C136] Yu-Hsuan Li, Tzu-Yin Chao, Ching-Chun Huang, Pin-Yu Chen, and Wei-Chen Chiu, “Make an Omelette with Breaking Eggs: Zero-Shot Learning for Novel Attribute Synthesis,” Neural Information Processing Systems (NeurIPS), 2022

[C135] Bikram Sahoo, Sarwan Ali, Pin-Yu Chen, Murray Patterson, and Alex Zelikovsky, “Evaluating the Robustness of ML Models in SARS-CoV-2 Genome Sequences Generated Using TGS Technology,” International Symposium on Bioinformatics Research and Applications (ISBRA), 2022

[C134] Zishen Wan, Karthik Swaminathan, Pin-Yu Chen, Nandhini Chandramoorthy, and Arijit Raychowdhury, “Analyzing and Improving Resilience and Robustness of Autonomous Systems,” IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2022  

[C133] Wenrui Mu, Ying Mao, Long Cheng, Qingle Wang, Weiwen Jiang, and Pin-Yu Chen, “Iterative Qubits Management for Quantum Index Searching in a Hybrid System,” IEEE International Performance, Computing, and Communications Conference (IPCCC), 2022

[C132] Shibal Ibrahim, Wenyu Chen, Yada Zhu, Pin-Yu Chen, Yang Zhang, and Rahul Mazumder, “Knowledge Graph Guided Simultaneous Forecasting and Network Learning for Multivariate Financial Time Series,” ACM International Conference on AI in Finance (ICAF), 2022

[C131] Jiachen Sun, Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Dan Hendrycks, Jihun Hamm, and Z. Morley Mao, “A Spectral View of Randomized Smoothing under Common Corruptions: Benchmarking and Improving Certified Robustness,” European Conference on Computer Vision (ECCV), 2022 <Fourier_Mix_code>

[C130] Jia-Hong Huang, Chao-Han Huck Yang, Pin-Yu Chen, Andrew Brown, and Marcel Worring, “Causal Video Summarizer for Video Exploration,” IEEE International Conference on Multimedia and Expo (ICME), 2022

[C129] Gaoyuan Zhang*, Songtao Lu*, Yihua Zhang, Xiangyi Chen, Pin-Yu Chen, Quanfu Fan, Lee Martie, Lior Horesh, Mingyi Hong, and Sijia Liu, “Distributed Adversarial Training to Robustify Deep Neural Networks at Scale,” The Conference on Uncertainty in Artificial Intelligence (UAI), 2022 (*equal contribution) <DAT_code> <Best paper runner-up award at UAI 2022>

[C128] Minhao Cheng, Qi Lei, Pin-Yu Chen, Inderjit Dhillon, and Cho-Jui Hsieh, “CAT: Customized Adversarial Training for Improved Robustness,” International Joint Conference on Artificial Intelligence (IJCAI), 2022

[C127] Celia Cintas, Payel Das, Brian Quanz, Girmaw Abebe Tadesse, Skyler Speakman, and Pin-Yu Chen, “Towards Creativity Characterization of Generative Models via Group-based Subset Scanning,” International Joint Conference on Artificial Intelligence (IJCAI), 2022 (Special Track on AI, the Arts and Creativity)

[C126] Hongkang Li, Meng Wang, Sijia Liu, Pin-Yu Chen, and Jinjun Xiong, “Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling,” International Conference on Machine Learning (ICML), 2022

[C125] Tianlong Chen*, Huan Zhang*, Zhenyu Zhang, Shiyu Chang, Sijia Liu, Pin-Yu Chen, and Zhangyang Wang, “Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness,” International Conference on Machine Learning (ICML), 2022 <Linearity_Grafting_code>  (*equal contribution)

[C124] Momin Abbas, Quan Xiao, Lisha Chen, Pin-Yu Chen, and Tianyi Chen, “Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning,”  International Conference on Machine Learning (ICML), 2022 <Sharp-MAML_code>

[C123] Ching-Yun Ko, Jeet Mohapatra, Sijia Liu, Pin-Yu Chen, Luca Daniel, and Lily Weng, “Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework,”  International Conference on Machine Learning (ICML), 2022

[C122] Yong Xie, Dakuo Wang, Pin-Yu Chen, Jinjun Xiong, Sijia Liu, and Sanmi Koyejo, “A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction,” Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2022 <AdvTweet_code> <TheRegister_Adv_Tweet> <IBM_Blog_Adv_Tweet>

[C121] Chao-Han Huck Yang, Jun Qi, Samuel Yen-Chi Chen, Yu Tsao, and Pin-Yu Chen, “When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022

[C120] Vardaan Taneja, Pin-Yu Chen, Yuguang Yao, and Sijia Liu, “When Does Backdoor Attack succeed in Image Reconstruction? A Study of Heuristics vs Bi-level Solution,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022

[C119] Chang-Sheng Lin, Chia-Yi Hsu, Pin-Yu Chen, and Chia-Mu Yu,  “Real-World Adversarial Examples involving Makeup Application,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022

[C118] Chia Hsiang Kao, Wei-Chen Chiu, and Pin-Yu Chen, “MAML is a Noisy Contrastive Learner in Classification,” International Conference on Learning Representations (ICLR), 2022

[C117] Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, and Jinjun Xiong, “How Unlabeled Data Improve Generalization in Self-training? A One-hidden-layer Theoretical Analysis,” International Conference on Learning Representations (ICLR), 2022

[C116] Keerthiram Murugesan*, Vijay Sadashivaiah*, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen, and Amit Dhurandhar, “Auto-Transfer: Learning to Route Transferable Representations,” International Conference on Learning Representations (ICLR), 2022 (*equal contribution) <AutoTransfer_code>

[C115] Sayak Paul* and Pin-Yu Chen*, “Vision Transformers are Robust Learners,” AAAI Conference on Artificial Intelligence (AAAI), 2022 (*equal contribution) <VitRobustness_code>

[C114] Chao-Han Huck Yang, I-Te Danny Hung, Yi Ouyang, and Pin-Yu Chen, “Training a Resilient Q-Network against Observational Interference,” AAAI Conference on Artificial Intelligence (AAAI), 2022 (*equal contribution) <CIQ_code>

[C113] Chia-Yi Hsu, Pin-Yu Chen, Songtao Lu, Sijia Lu, and Chia-Mu Yu, “Adversarial Examples can be Effective Data Augmentation for Unsupervised Machine Learning,” AAAI Conference on Artificial Intelligence (AAAI), 2022 <UAE_code>

[C112] Zichong Li, Pin-Yu Chen*, Sijia Liu*, Songtao Lu*, and Yangyang Xu*, “Zeroth-order Optimization for Composite Problems with Functional Constraints,” AAAI Conference on Artificial Intelligence (AAAI), 2022 (*alphabetical order) (oral presentation) 

[C111] Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilovic, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang, “AI Explainability 360: Impact and Design,” Annual Conference on Innovative Applications of Artificial Intelligence (IAAI), 2022

[C110] Xiao Jin, Pin-Yu Chen, Chia-Yi Hsu, Chia-Mu Yu, and Tianyi Chen, “CAFE: Catastrophic Data Leakage in Vertical Federated Learning,” Neural Information Processing Systems (NeurIPS), 2021 <CAFE_code>

[C109] Yair Schiff, Brian Quanz, Payel Das, and Pin-Yu Chen, “Predicting Deep Neural Network Generalization with Perturbation Response Curves,” Neural Information Processing Systems (NeurIPS), 2021

[C108] Yu-Lin Tsai, Chia-Yi Hsu, Chia-Mu Yu, and Pin-Yu Chen, “Formalizing Generalization and Adversarial Robustness of Neural Networks to Weight Perturbations,” Neural Information Processing Systems (NeurIPS), 2021

[C107] Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, and Jihun Hamm, “Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning,” Neural Information Processing Systems (NeurIPS), 2021 <UDA_limit_code>

[C106] Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, and Jinjun Xiong, “Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Sparse Neural Networks,” Neural Information Processing Systems (NeurIPS), 2021

[C105] Jingkang Wang*, Tianyun Zhang*, Sijia Liu, Pin-Yu Chen, Jiacen Xu, Makan Fardad, and Bo Li, “Adversarial Attack Generation Empowered by Min-Max Optimization,” Neural Information Processing Systems (NeurIPS), 2021 (*equal contribution)

[C104] Arpan Mukherjee, Ali Tajer, Pin-Yu Chen, and Payel Das, “Mean-based Best Arm Identification in Stochastic Bandits under Reward Contamination,” Neural Information Processing Systems (NeurIPS), 2021

[C103] Lijie Fan, Sijia Liu, Pin-Yu Chen, Gaoyuan Zhang, and Chuang Gan, “When does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?,” Neural Information Processing Systems (NeurIPS), 2021 <AdcCL_code> <TechTalks_AdvCL>

[C102] Yue Cao, Payel Das, Vijil Chenthamarakshan, Pin-Yu Chen, Igor Melnyk, and Yang Shen, “Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design,” International Conference on Machine Learning (ICML), 2021 <Fold2Seq_code>

[C101] Chao-Han Huck Yang, Yun-Yun Tsai, and Pin-Yu Chen, “Voice2Series: Reprogramming Acoustic Models for Time Series Classification,” International Conference on Machine Learning (ICML), 2021 <V2S_code>

[C100] Chulin Xie, Minghao Chen, Pin-Yu Chen, and Bo Li, “CRFL: Certifiably Robust Federated Learning against Backdoor Attacks,” International Conference on Machine Learning (ICML), 2021 <CRFL_code>

[C99] Ronny Luss*, Pin-Yu Chen*, Amit Dhurandhar*, Prasanna Sattigeri*, Yunfeng Zhang*, Karthikeyan Shanmugam, and Chun-Chen Tu, “Leveraging Latent Features for Local Explanations,” ACM SIGKDD International Conference on Knowledge Discovery and Data Mining  (KDD) 2021 (*equal contribution)

[C98] Siyue Wang*, Xiao Wang*, Pin-Yu Chen, Pu Zhao, and Xue Lin, “Characteristic Examples: High-Robustness, Low-Transferability Fingerprinting of Neural Networks,” International Joint Conference on Artificial Intelligence (IJCAI), 2021 (*equal contribution)

[C97] Arpan Mukherjee, Ali Tajer, Pin-Yu Chen, and Payel Das, “Active Binary Classification of Random Fields,” IEEE International Symposium on Information Theory (ISIT), 2021

[C96] Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, and Jihun Hamm, “How Robust are Randomized Smoothing based Defenses to Data Poisoning?” Conference on Computer Vision and Pattern Recognition (CVPR), 2021 <PACD_code> <TeckTalks_PACD> 

[C95] Omid Aramoon, Pin-Yu Chen, and Gang Qu, “AID: Attesting the Integrity of Deep Neural Networks,” Design Automation Conference (DAC), 2021

[C94] Yu-Lin Tsai, Chia-Yi Hsu, Chia-Mu Yu, and Pin-Yu Chen, “Non-Singular Adversarial Robustness of Neural Networks,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021

[C93] Arpan Mukherjee, Ali Tajer, Pin-Yu Chen, and Payel Das, “Active Estimation from Multimodal Data,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021

[C92] Chun-Chieh Teng, Pin-Yu Chen, and Wei-Chen Chiu, “Domain Adaptation for Learning Generator from Paired Few-Shot Data,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021

[C91] Chao-Han Huck Yang, Jun Qi, Samuel Yen-Chi Chen, Pin-Yu Chen, Sabato Marco Siniscalchi, Xiaoli Ma, and Chin-Hui Lee, “Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021 <QVFL_code> <QVFL_video> <QVFL_slides>

[C90] Zichong Li, Pin-Yu Chen*, Sijia Liu*, Songtao Lu*, and Yangyang Xu*, “Rate-improved Inexact Augmented Lagrangian Method for Constrained Nonconvex Optimization,” Artificial Intelligence and Statistics (AISTATS), 2021 (*alphabetical order)

[C89] Jeet Mohapatra, Ching-Yun Ko, Tsui-Wei (Lily) Weng, Sijia Liu, Pin-Yu Chen, and Luca Daniel, “Hidden Cost of Randomized Smoothing,” Artificial Intelligence and Statistics (AISTATS), 2021

[C88] Ren Wang, Kaidi Xu, Sijia Liu, Pin-Yu Chen, Tsui-Wei (Lily) Weng, Chuang Gan, and Meng Wang, “On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning,” International Conference on Learning Representations (ICLR), 2021 <Robust_MAML_video>

[C87] Omid Aramoon, Pin-Yu Chen, and Gang Gu, “Don't Forget to Sign the Gradients!,” Fourth Conference on Machine Learning and Systems (MLSyS), 2021 <SignGradient_video>

[C86] Minhao Cheng, Pin-Yu Chen, Sijia Liu, Shiyu Chang, Cho-Jui Hsieh, and Payel Das, “Self-Progressing Robust Training,” AAAI Conference on Artificial Intelligence (AAAI), 2021 <SPROUT_code>

[C85] Maurício Gruppi, Sibel Adali, and Pin-Yu Chen, “Fake it Till You Make it: Self-Supervised Semantic Shifts for Monolingual Word Embedding Tasks,” AAAI Conference on Artificial Intelligence (AAAI), 2021 <S4_code> <Sense_demo>

[C84] Syed Zawad, Ahsan Ali, Pin-Yu Chen, Ali Anwar, Yi Zhou, Nathalie Baracaldo, Yuan Tian, and Feng Yan, “Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning,” AAAI Conference on Artificial Intelligence (AAAI), 2021 

[C83] Akhilan Boopathy, Lily Weng, Sijia Liu, Pin-Yu Chen, Gaoyuan Zhang, and Luca Daniel, “Fast Training of Provably Robust Neural Networks by SingleProp,” AAAI Conference on Artificial Intelligence (AAAI), 2021 

[C82] N. Joseph Tatro, Pin-Yu Chen, Payel Das, Igor Melnyk, Prasanna Sattigeri, and Rongjie Lai, “Optimizing Mode Connectivity via Neuron Alignment,” Neural Information Processing Systems (NeurIPS), 2020 <Neuron_Alignment_code> <IBM_blog_TND_Align>

[C81] Jeet Mohapatra, Ching-Yun Ko, Tsui-Wei (Lily) Weng, Pin-Yu Chen, Sijia Liu, and Luca Daniel, “Higher-Order Certification For Randomized Smoothing,” Neural Information Processing Systems (NeurIPS), 2020 (selected for spotlight presentation, top 3% submission) <IBM_Blog_Certification>

[C80] 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, “ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training,” Neural Information Processing Systems (NeurIPS), 2020 <IBM_blog_ScaleCom>

[C79] Ren Wang, Gaoyuan Zhang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong, and Meng Wang, “Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Cases,” European Conference on Computer Vision (ECCV), 2020 <TND_code> <IBM_blog_TND_Align>

[C78] Kaidi Xu, Gaoyuan Zhang, Sijia Liu, Quanfu Fan, Mengshu Sun, Hongge Chen, Pin-Yu Chen, Yanzhi Wang, and Xue Lin, “Adversarial T-shirt! Evading Person Detectors in A Physical World,” European Conference on Computer Vision (ECCV), 2020 <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>

[C77] Yun-Yun Tsai, Pin-Yu Chen, Tsung-Yi Ho, “Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources,” International Conference on Machine Learning (ICML), 2020 <BAR_code> <TheNextWeb_BAR> <Techtalks_BAR>

[C76] Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, and Jinjun Xiong, “Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case,” International Conference on Machine Learning (ICML), 2020

[C75] Akhilan Boopathy, Sijia Liu, Gaoyuan Zhang, Cynthia Liu, Pin-Yu Chen, Shiyu Chang, and Luca Daniel, “Proper Network Interpretability Helps Adversarial Robustness in Classification,” International Conference on Machine Learning (ICML), 2020

[C74] Sanghamitra Dutta, Dennis Wei, Hazar Yueksel, Pin-Yu Chen, Sijia Liu, and Kush R. Varshney, “An Information-Theoretic Perspective on the Relationship Between Fairness and Accuracy,” International Conference on Machine Learning (ICML), 2020

[C73] Payel Das*, Brian Quanz*, Pin-Yu Chen, Jaw-wook Ahn, and Dhruv Shah “Toward A Neuro-inspired Creative Decoder,” International Joint Conference on Artificial Intelligence (IJCAI), 2020 (*equal contribution)

[C72] Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, and Jinjun Xiong, “Guaranteed Convergence of Training Convolutional Neural Networks via Accelerated Gradient Descent,” Annual Conference on Information Sciences and Systems (CISS), 2020

[C71] Jeet Mohapatra, Tsui-Wei (Lily) Weng, Pin-Yu Chen, Sijia Liu, and Luca Daniel, “Towards Verifying Robustness of Neural Networks Against A Family of Semantic Perturbations,” Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (oral presentation) <Semantify-NN_code> <Semantify-NN_video>

[C70] Chao-Han Huck Yang, Jun Qi, Pin-Yu Chen, Xiaoli Ma, and Chin-Hui Lee, “Characterizing Adversarial Speech Examples Using Self-Attention U-Net Enhancement,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020

[C69] Chao-Han Huck Yang, Jun Qi, Pin-Yu Chen, Yi Ouyang, Chin-Hui Lee, and Xiaoli Ma, “Enhanced Adversarial Strategically-Timed Attacks against Deep Reinforcement Learning,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020

[C68] Kaidi Xu, Sijia Liu, Pin-Yu Chen, Mengshu Sun, Caiwen Ding, Bhavya Kailkhura, and Xue Lin, “Towards an Efficient and General Framework of Robust Training for Graph Neural Networks,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020

[C67] Xiao Wang, Siyue Wang, Pin-Yu Chen, Xue Lin, and Peter Chin, “ADVMS: A Multi-source Multi-cost Defense against Adversarial Attacks,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020

[C66] Pu Zhao, Pin-Yu Chen, Payel Das, Karthikeyan Natesan Ramamurthy, and Xue Lin, “Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness,” International Conference on Learning Representations (ICLR), 2020 <TechTalks_sanitization> <TheNextWeb_sanitization> <Model_Sanitization_code>

[C65] Chulin Xie, Keli Huang, Pin-Yu Chen, and Bo Li, “DBA: Distributed Backdoor Attacks against Federated Learning,” International Conference on Learning Representations (ICLR), 2020 <DBA_video>

[C64] Minhao Cheng*, Simranjit Singh*, Patrick H. Chen, Pin-Yu Chen, Sijia Liu, and Cho-Jui Hsieh, “Sign-OPT: A Query-Efficient Hard-label Adversarial Attack,” International Conference on Learning Representations (ICLR), 2020 (*equal contribution) <Sign-OPT_IBM>

[C63] Minhao Cheng, Jinfeng Yi, Pin-Yu Chen, Huan Zhang, and Cho-Jui Hsieh, “Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples,” AAAI Conference on Artificial Intelligence (AAAI), 2020 <Seq2Sick_code> <Towards_Data_Science_Seq2Sick>

[C62] Pu Zhao, Pin-Yu Chen, Siyue Wang, and Xue Lin, “Towards Query-Efficient Black-Box Adversary with Zeroth-Order Natural Gradient Descent,” AAAI Conference on Artificial Intelligence (AAAI), 2020 <ZO_NGD_code>

[C61] Tsui-Wei Weng*, Pu Zhao*, Sijia Liu, Pin-Yu Chen, Xue Lin, and Luca Daniel, “Towards Certificated Model Robustness Against Weight Perturbations,” AAAI Conference on Artificial Intelligence (AAAI), 2020 (*equal contribution) <code> <poster>

[C60] Yunan Ye, Hengzhi Pei, Boxin Wang, Pin-Yu Chen, Yada Zhu, Jun Xiao, and Bo Li, “Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States,” AAAI Conference on Artificial Intelligence (AAAI), 2020 <poster>

[C59] Pu Zhao, Sijia Liu, Pin-Yu Chen, Nghia Hoang, Kaidi Xu, Bhavya Kailkhura, and Xue Lin, “On the Design of Black-box Adversarial Examples by Leveraging Gradient-free Optimization and Operator Splitting Method,” International Conference on Computer Vision (ICCV), 2019 <ZO_ADMM_code>

[C58] Vachik S. Dave, Baichuan Zhang, Pin-Yu Chen, Mohammad Al Hasan, “Neural-Brane: An Inductive Approach for Attributed Network Embedding,” IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2019 <Neural-Brane_code>

[C57] Xiao Wang*, Siyue Wang*, Pin-Yu Chen, Yanzhi Wang, Brian Kulis, Xue Lin, Sang Chin, “Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses,” International Joint Conference on Artificial Intelligence (IJCAI), 2019 (*equal contribution) <IBM_Research_Blog_GNN_HRS> <TechTalks_HRS> <Medium_HRS> <HRS_code>

[C56] Kaidi Xu*, Hongge Chen*, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Mingyi Hong, and Xue Lin, “Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective,” International Joint Conference on Artificial Intelligence (IJCAI), 2019 (*equal contribution) <IBM_Research_Blog_GNN_HRS>

[C55] Chao-Han Huck Yang*, Yi-Chieh Liu*, Pin-Yu Chen, Xiaoli Ma, Yi-Chang James Tsai, “When Causal Intervention Meets Adversarial Perturbation and Image Masking for Deep Neural Networks,” IEEE International Conference on Image Processing (ICIP), 2019 (*equal contribution)

[C54] Pin-Yu Chen, Lingfei Wu, Sijia Liu, and Indika Rajapakse, “Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications,” International Conference on Machine Learning (ICML), 2019 (oral presentation) <FINGER_code> <slides>

[C53] Tsui-Wei Weng, Pin-Yu Chen, Lam M. Nguyen, Mark S. Squillante, Ivan Oseledets, Akhilan Boopathy, and Luca Daniel, “PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach,” International Conference on Machine Learning (ICML), 2019 (oral presentation) <PROVEN_code> <slides>

[C52] Qi Lei*, Lingfei Wu*, Pin-Yu Chen, Alexandros G. Dimakis, Inderjit S. Dhillon, and Michael Witbrock, “Discrete Adversarial Attacks and Submodular Optimization with Applications to Text Classification,” The Conference on Systems and Machine Learning (SysML) 2019 (*equal contribution) <Paraphrasing_attack_code> <VB_Paraphrasing> <TechTalks_Paraphrasing> <Jiqizhixin_Paraphasing> <Nature_News>

[C51] Sijia Liu, Pin-Yu Chen, Xiangyi Chen, and Mingyi Hong, “SignSGD via Zeroth-Order Oracle,” International Conference on Learning Representations (ICLR), 2019

[C50] Minhao Cheng, Thong Le, Pin-Yu Chen, Jinfeng Yi, Huan Zhang, and Cho-Jui Hsieh, “Query-Efficient Hard-label Black-box Attack: An Optimization-based Approach,” International Conference on Learning Representations (ICLR), 2019 <OptAttack_code>

[C49] Kaidi Xu* Sijia Liu*, Pu Zhao, Pin-Yu Chen, Huan Zhang, Quanfu Fan, Deniz Erdogmus, Yanzhi Wang, Xue Lin, “Structured Adversarial Attack: Towards General Implementation and Better Interpretability,” International Conference on Learning Representations (ICLR), 2019 (*equal contribution) <StrAttack_code>

[C48] Zhuolin Yang, Bo Li, Pin-Yu Chen, Dawn Song, “Characterizing Audio Adversarial Examples Using Temporal Dependency,” International Conference on Learning Representations (ICLR), 2019 <TD_code> <poster> <TechTalks_temporal_dependency> <IBM_Research_Blog_Temporal_Dependency> <Nature_News>

[C47] Akhilan Boopathy, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, and Luca Daniel “CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks,” AAAI Conference on Artificial Intelligence (AAAI), 2019 (oral presentation) <CNN-Cert_code> <slides> <poster> <EE_TIMES> <TechTalks> <IBM_Research_Blog_CNN-Cert> <MIT_IBM_Medium_CNN-Cert> <IBM Response to NIST RFI on AI> <MC.AI_AutoZOOM>

[C46] Chun-Chen Tu*, Paishun Ting*, Pin-Yu Chen*, Sijia Liu, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh, and Shin-Ming Cheng, “AutoZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural Networks,” AAAI Conference on Artificial Intelligence (AAAI), 2019 (oral presentation) (*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>

[C45] Amit Dhurandhar*, Pin-Yu Chen*, Ronny Luss, Chun-Chen Tu, Paishun Ting, Karthikeyan Shanmugam, and Payel Das, “Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives,” Neural Information Processing Systems (NeurIPS), 2018 (*equal contribution) <CEM_code> <Forbes_coverage> <PC_Magazine> <IBM_Research_Blog> 

[C44] Huan Zhang*, Tsui-Wei Weng*, Pin-Yu Chen, Cho-Jui Hsieh, and Luca Daniel, “Efficient Neural Network Robustness Certification with General Activation Functions,” Neural Information Processing Systems (NeurIPS), 2018 (*equal contribution) <CROWN_code>

[C43] Sijia Liu, Bhavya Kailkhura, Pin-Yu Chen, Pai-Shun Ting, Shiyu Chang, and Lisa Amini, “Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization,” Neural Information Processing Systems (NeurIPS), 2018 <poster>

[C42] Pin-Yu Chen*, Bhanukiran Vinzamuri*, and Sijia Liu, “Is Ordered Weighted $\ell_1$ Regularized Regression Robust to Adversarial Perturbation? A Case Study on OSCAR,” IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2018 (*equal contribution) <slides> 

[C41] Tsui-Wei Weng*, Huan Zhang*, Pin-Yu Chen, Aurelie Lozano, Cho-Jui Hsieh, and Luca Daniel, “On Extensions of CLEVER: a Neural Network Robustness Evaluation Algorithm,” IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2018 (*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>

[C40] Sijia Liu, Xingguo Li, Pin-Yu Chen, Jarvis Haupt, and Lisa Amini, “Zeroth-Order Stochastic Projected Gradient Descent for Nonconvex Optimization,” IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2018

[C39] Chia-Yi Hsu, Pei-Hsuan Lu, Pin-Yu Chen, and Chia-Mu Yu, “On The Utility of Conditional Generation Based Mutual Information for Characterizing Adversarial Subspaces,” IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2018 <poster>

[C38] Lingfei Wu, Ian E.H. Yen, Kun Xu, Fangli Xu, Avinash Balakrishnan, Pin-Yu Chen, Pradeep Ravikumar, and Michael J. Witbrock, “Word Mover's Embedding: From Word2Vec to Document Embedding,” Conference on Empirical Methods in Natural Language Processing (EMNLP), 2018 <IBM_Research_Blog>

[C37] Dong Su*, Huan Zhang*, Hongge Chen, Jinfeng Yi, Pin-Yu Chen, and Yupeng Gao, “Is Robustness the Cost of Accuracy? A Comprehensive Study on the Robustness of 18 Deep Image Classification Models,” European Conference on Computer Vision (ECCV), 2018 (*equal contribution) <Code> <slides>

[C36] Lingfei Wu, Pin-Yu Chen, Ian En-Hsu Yen, Fangli Xu, Yinglong Xia, and Charu Aggarwal, “Scalable Spectral Clustering Using Random Binning Features,” ACM SIGKDD International Conference on Knowledge Discovery and Data Mining  (KDD) 2018 (oral presentation) <IBM_Research_Blog> <poster> <slides> <SC-RB_Code>

[C35] Hongge Chen*, Huan Zhang*, Pin-Yu Chen, Jinfeng Yi, and Cho-Jui Hsieh, “Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning,” Annual Meeting of the Association for Computational Linguistics (ACL), 2018  (*equal contribution) <ShowAndFool_code> <poster>

[C34] Pei-Hsuan Lu, Pin-Yu Chen, Kang-Cheng Chen, and Chia-Mu Yu, “On the Limitation of MagNet Defense against $L_1$ based Adversarial Examples,” IEEE/IFIP International Conference on Dependable and Systems and Networks (DSN) 2018, Workshop on Dependable and Secure Machine Learning

[C33] Pei-Hsuan Lu, Pin-Yu Chen, and Chia-Mu Yu, “On the Limitation of Local Intrinsic Dimensionality for Characterizing the Subspaces of Adversarial Examples,” International Conference on Learning Representations (ICLR) 2018 Workshop <poster>

[C32] Yash Sharma and Pin-Yu Chen, “Attacking the Madry Defense Model with $L_1$-based Adversarial Examples,” International Conference on Learning Representations (ICLR) 2018 Workshop <poster>

[C31] Tsui-Wei Weng*, Huan Zhang*, Pin-Yu Chen, Jinfeng Yi, Dong Su, Yupeng Guo, Cho-Jui Hsieh, and Luca Daniel,  “Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach,” International Conference on Learning Representations (ICLR), 2018 (*equal contribution) <CLEVER_code> <IBM_Research_Blog> <SiliconANGLE_news> <MIT_IBM_Medium> <IBM_Research_AI_2018_Review> <IBM Response to NIST RFI on AI> <Fool_the_Bank_demo>

[C30] Pin-Yu Chen* and Dennis Wei*, “On the Supermodularity of Active Graph-based Semi-supervised Learning with Stieltjes Matrix Regularization,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018 (*equal contribution) <poster>

[C29] Sijia Liu, Pin-Yu Chen, Indika Rajapakse, and Alfred Hero, “First-order Bifurcation Detection for Dynamic Complex Networks,”  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018

[C28] Jie Chen, Sijia Liu, and Pin-Yu Chen, “Zeroth-order Diffusion Adaptation over Networks,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018

[C27] Sijia Liu, Jie Chen, Pin-Yu Chen, and Alfred Hero, “Zeroth-Order Online Alternating Direction Method of Multipliers: Convergence Analysis and Applications,” International Conference on Artificial Intelligence and Statistics (AISTATS), 2018  - Also presented at NeurIPS 2017 Optimization for Machine Learning Workshop <poster>

[C26] Pin-Yu Chen*, Yash Sharma*, Huan Zhang, Jinfeng Yi, and Cho-Jui Hsieh, “EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples,” AAAI Conference on Artificial Intelligence (AAAI), 2018 (*equal contribution) <EAD_code> <cleverhans> <adversarial_robustness_toolbox> <Foolbox> <slides>

[C25] Pin-Yu Chen*, Huan Zhang*, Yash Sharma, Jinfeng Yi, and Cho-Jui Hsieh, “ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models,” ACM Conference on Computer and Communications Security (CCS) Workshop on Artificial Intelligence and Security (AISec), 2017 (*equal contribution) <ZOO_code> <adversarial_robustness_toolbox> <slides> (best paper award finalist) - Also presented at NeurIPS 2017 Machine Learning and Computer Security Workshop

[C24] W. Liu, P.-Y. Chen, S. Yeung, T. Suzumura and L. Chen, “Principled Multilayer Network Embedding,” IEEE Conference on Data Mining (ICDM) Workshop on Data Mining in Networks, pp. 134-141, Nov., 2017

[C23] P.-Y. Chen and L. Wu, “Revisiting Spectral Graph Clustering with Generative Community Models,” IEEE Conference on Data Mining (ICDM), pp. 51-60, Nov., 2017 <slides> (9.25 % regular paper acceptance rate)

[C22] W. Liu, P.-Y. Chen, H. Cooper, M.-H. Oh, S. Yeung, and T. Suzumura, “Can GAN Learn Topological Features of a Graph?” International Conference on Machine Learning (ICML) Workshop on Implicit Generative Models, Aug., 2017

[C21] P.-S. Ting, C.-C. Tu, P.-Y. Chen, Y-.Y. Luo, and S.-M. Cheng, “FEAST: An Automated Feature Selection Framework for Compilation Tasks,” 31-st IEEE International Conference on Advanced Information Networking and Applications (AINA), pp. 1138-1145, Mar. 2017 <slides><slides+audio>

[C20] P.-Y. Chen, T. Gensollen, and A. O. Hero, “AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6448--6452, Mar. 2017  (oral presentation) <AMOS code> <slides>

[C19] S. Liu, P.-Y. Chen, and A. O. Hero, “Distributed Optimization for Evolving Networks of Growing Connectivity,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4079--4083, Mar. 2017 (oral presentation)

[C18] P.-Y. Chen and A. O. Hero, “Multilayer Spectral Graph Clustering via Convex Layer Aggregation,” IEEE Global Conference on Signal and Information Processing (GlobalSIP),  pp. 317-321, Dec. 2016 <slides> (oral presentation; awarded IEEE GlobalSIP Student Travel Grant)

[C17] P.-Y. Chen, B. Zhang, M. Hasan, and A. O. Hero, “Incremental Method for Spectral Clustering of Increasing Orders,” ACM SIGKDD International Conference on Knowledge Discovery and Data Mining  (KDD) workshop on Mining and Learning with Graphs (MLG), 2016 <slides> <poster> <video> (contributed talk; awarded ACM KDD Student Travel Award)

[C16] S.-M. Cheng and P.-Y. Chen, “Ecology-based DoS Attack in Cognitive Radio Networks,” IEEE Symposium on Security and Privacy (S&P) workshop on Bio-inspired Security, Trust, Assurance, and Resilience (BioSTAR), pp. 104-110, May 2016 (awarded IEEE S&P Student Travel Grant)

[C15] P.-Y. Chen, S. Choudhury, and A. O. Hero, “Multi-Centrality Graph Spectral Decompositions and Their Application to Cyber Intrusion Detection,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4553-4556, Mar. 2016 <slides><poster> <video>

[C14] P.-Y. Chen, C.-W. Lien, F.-J. Chu, P.-S. Ting, and S.-M. Cheng, “Supervised Collective Classification for Crowdsourcing,” IEEE Global Communications Conference (GLOBECOM) Workshop on Networking and Collaboration Issues for the Internet of Everything, pp. 1-6, Dec. 2015

[C13] S. Choudhury, P.-Y. Chen, L. Rodriguez, D. Curtis, P. Nordquist, I. Ray, K. Oler and and P. Nordquist,  “Action Recommendation for Cyber Resilience,” ACM Conference on Computer and Communications Security (CCS) Workshop on Automated Decision Making for Active Cyber Defense, pp. 3-8, Oct. 2015 (acceptance rate 8/27 (Covered by PNNL research highlight <Link>)

[Demo Video: Defending Real-Time Attacks on Amazon Cloud]

[C12] P.-Y. Chen, Z. Qi, Y. Pan, and S.-M. Cheng, “Multivariate and Categorical Analysis of Gaming Statistics,” 18th International Conference on Network-Based Information Systems (NBiS), pp. 286-293, Sep. 2015

[C11] P.-Y. Chen, and A. O. Hero, “Phase Transitions in Spectral Community Detection of Large Noisy Networks,“ IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3402-3406, Apr. 2015 (Oral Presentation <Link>; awarded SPS Travel Grant)

[C10] P.-Y. Chen, and A. O. Hero, “Local Fiedler Vector Centrality for Detection of Deep and Overlapping Communities in Networks,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1120-1124, May 2014 (awarded NSF Travel Grant)  <LFVC_code> <poster>

[C9] P.-Y. Chen, H.-F. Lin,K.-H. Hsu, and S.-M. Cheng, “Modeling Dynamics of Malware with Incubation Period from the View of Individual,” IEEE Vehicular Technology Conference (VTC), pp. 1-5, May 2014 

[C8] P.-Y. Chen, and A. O. Hero, “Node Removal Vulnerability of the Largest Component of a Network”, IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp. 587-590. Dec. 2013 <poster>

[C7] S.-Y. Lien, H.-H. Lee, S.-Y. Shih, P.-Y. Chen, and K.-C. Chen, “Network Synchronization among Femtocells,” IEEE Global Communications Conference (GLOBECOM) Workshops, pp.248-252, Dec. 2011

[C6] P.-Y. Chen, W. C. Ao, S.-C. Lin, and K.-C. Chen, “Reciprocal Spectrum Sharing Game and Mechanism in Cellular Systems with Cognitive Radio Users,” IEEE Global Communications Conference (GLOBECOM) Workshops, pp.981-985, Dec. 2011 

[C5] P.-Y. Chen, and K.-C. Chen, “Optimal Control of Epidemic Information Dissemination in Mobile Ad Hoc Networks," IEEE Global Communications Conference (GLOBECOM), pp. 1–5. Dec. 2011

[C4] P.-Y. Chen, and K.-C. Chen, “Intentional Attack and Fusion-based Defense Strategy in Complex Networks," IEEE Global Communications Conference (GLOBECOM), pp. 1–5. Dec. 2011

[C3] P.-Y. Chen, V. Karyotis, S. Papavassiliou, and K.-C. Chen, “Topology Control in Multi-channel Cognitive Radio Networks with Non-uniform Node Arrangements," IEEE Symposium on Computers and Communications (ISCC), pp.1033-1037, June 2011

[C2] P.-Y. Chen, S.-M. Cheng, W. C. Ao, and K.-C. Chen, “Multi-path Routing with End-to-end Statistical QoS Provisioning in Underlay Cognitive Radio Networks,” IEEE International Conference on Computer Communications (INFOCOM) Workshops, pp.7-12, April 2011

[C1] P.-Y. Chen, and K.-C. Chen, “Information Epidemics in Complex Networks with Opportunistic Links and Dynamic Topology," IEEE Global Communications Conference (GLOBECOM), pp.1-6, Dec. 2010 (Received GOLD Best Paper Award <Link>)

Arxiv Preprints

• Tsuyoshi Idé, Jokin Labaien, and Pin-Yu Chen, “Improving Transformers using Faithful Positional Encoding,”

• N. Joseph Tatro, Payel Das, Pin-Yu Chen, Vijil Chenthamarakshan, and Rongjie Lai, “Learning Geometrically Disentangled Representations of Protein Folding Simulations,”

• Chia-Hung Yuan, Pin-Yu Chen, and Chia-Mu Yu, “Meta Adversarial Perturbations,” AAAI 2022 Workshop

• Rulin Shao, Jinfeng Yi, Pin-Yu Chen, and Cho-Jui Hsieh, “How and When Adversarial Robustness Transfers in Knowledge Distillation?”

• 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,”

• 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,”

• Alex Gu, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, and Luca Daniel, “Certified Interpretability Robustness for Class Activation Mapping,” NeurIPS 2020 Workshop on Machine Learning for Autonomous Driving

• Orlando Romero, Subhro Das, Pin-Yu Chen, and Sérgio Pequito, “A Dynamical Systems Approach for Convergence of the Bayesian EM Algorithm,”

• Amit Dhurandhar*, Tejaswini Pedapati*, Avinash Balakrishnan*, Pin-Yu Chen*, Karthikeyan Shanmugam, and Ruchir Puri, “Model Agnostic Contrastive Explanations for Structured Data,” (*equal contribution)

• Zhenhan Huang, Chunheng Jiang, Pin-Yu Chen, and Jianxi Gao, “Network Graph Based Neural Architecture Search,”