Publications

2024

  1. A F M Saif, Lisha Chen, Xiaodong Cui, Songtao Lu, Brian Kingsbury, and Tianyi Chen. "M$^{2}$ASR: Multilingual Multi-Task Automatic Speech Recognition via Multi-Objective Optimization." Annual Conference of the International Speech Communication Association (Interspeech 2024)

  2. A. F. M. Saif, Xiaodong Cui, Han Shen, Songtao Lu, Brian Kingsbury, and Tianyi Chen. Joint Unsupervised and Supervised Training for Automatic Speech Recognition via Bilevel Optimization." IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024.

  3. Emre Acartürk, Burak Varıcı, Karthikeyan Shanmugam, and Ali Tajer. Sample Complexity of Interven- tional Causal Representation Learning. In Proc. Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 2024 

  4. Anmol Dwivedi and Ali Tajer. GRNN-based real-time fault chain prediction. IEEE Transactions on Power Systems, 39(1):934–946, January 2024.

  5. Brandon Rozek, Junkyu Lee, Harsha Kokel, Michael Katz and Shirin Sohrabi, Partially Observable Hierarchical Reinforcement Learning with AI Planning (student abstract), AAAI 2024

  6. Brandon Rozek, Junkyu Lee, Harsha Kokel, Michael Katz, Shirin Sohrabi, Guiding Hierarchical Reinforcement Learning in Partially Observable Environments with AI Planning. PRL, ICAPS 2024

  7. Burak Varıcı, Emre Acartürk, Karthikeyan Shanmugam, and Ali Tajer. General Identifiability and Achievability for Causal Representation Learning. In Proc. International Conference on Artificial Intelligence and Statistics (AISTATS), Valencia, Spain, May 2024

  8. Burak Varıcı, Dmitriy Katz-Rogozhnikov, Ali Tajer, Dennis Wei, and Prasanna Sattigeri. Separability Analysis for Causal Discovery in Mixture of DAGs. Transactions on Machine Learning Research, January 2024.

  9. Anamitra Chaudhuri, Georgios Fellouris, and Ali Tajer. Round Robin Active Sequential Change Detection for Dependent Multi-Channel Data. IEEE Transactions on Information Theory (accepted for publication, September 2024)

  10. Chunheng Jiang, Zhenhan Huang, Tejaswini Pedapati, Pin-Yu Chen, Yizhou Sun, and Jianxi Gao, “Network properties determine neural network performance,” Nature Communications, 2024

  11. Zijun Cui, Hanjing Wang, Tian Gao, Kartik Talamadupula, Qiang Ji. Theory-guided Message Passing Neural Network for Probabilistic Inference. AISTATS 2024 

  12. Anmol Dwivedi, Santiago Paternain, and Ali Tajer. Blackout Mitigation via Physics-guided RL. IEEE Transactions on Power Systems (accepted for publication, September 2024)

  13. Anmol Dwivedi, Nurali Virani, Santiago Paternain, and Ali Tajer. RL for Mitigating Cascading Failures: Targeted Exploration via Sensitivity Factors. In Proc. Conference on Neural Information Processing Systems (NeurIPS) – Tackling Climate Change with Machine Learning Workshop, Vancouver, Canada, December 2024

  14. 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

  15. 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

  16. 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 

  17. 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 

  18. 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

  19. James Oswald, Kavitha Srinivas, Harsha Kokel, Junkyu Lee, Michael Katz and Shirin Sohrabi, Large Language Models as Planning Domain Generators (student abstract), AAAI 2024

  20. James Oswald, Kavitha Srinivas, Harsha Kokel, Junkyu Lee, Michael Katz and Shirin Sohrabi, Large Language Models as Planning Domain Generators, ICAPS 2024

  21. Inwon Kang, Parikshit Ram, Yi Zhou, Horst Samulowitz, and Oshani Seneviratne. "Effective Data Distillation for Tabular Datasets (Student Abstract)." In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 21, pp. 23533-23534. 2024

  22. P. N. Karthik, Vincent Y. F. Tan, Arpan Mukherjee, and Ali Tajer. Optimal Best Restless Markov Arm Identification with Fixed Confidence. IEEE Transactions on Information Theory, June 2024

  23. P. N. Karthik, Vincent Y. F. Tan, Arpan Mukherjee, and Ali Tajer. Optimal Best Restless Markov Arm Identification with Fixed Confidence. IEEE Transactions on Information Theory, 70(10):7349–7384, October 2024

  24. Md Shamim Hussain, Mohammed J. Zaki, Dharmashankar Subramanian. Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers. International Conference on Machine Learning (ICML), 2024

  25. 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 

  26. Momin Abbas, Yi Zhou, Parikshit Ram, Nathalie Baracaldo, Horst Samulowitz, Theodoros Salonidis, Tianyi Chen. "Enhancing In-context Learning via Linear Probe Calibration". AISTATS 2024

  27. Arpan Mukherjee and Ali Tajer. Efficient Best Arm Identification in Stochastic Bandits: Beyond β−optimality. IEEE Transactions on Information Theory (accepted for publication, September 2024)

  28. Arpan Mukherjee and Ali Tajer. BAI in Exponential Family: Efficiency and Optimality. In Proc. IEEE International Symposium on Information Theory (ISIT), Athens, Greece, July 2024

  29. Arpan Mukherjee and Ali Tajer. BAI in Exponential Family: Efficiency and Optimality. In Proc. IEEE International Symposium on Information Theory (ISIT), Athens, Greece, July 2024

  30. Lilian Ngweta, Mayank Agarwal, Subha Maity, Alex Gittens, Yuekai Sun, & Mikhail Yurochkin. “Aligners: Decoupling LLMs and Alignment”. In The Second Tiny Papers Track. ICLR 2024

  31. Qitong Wang, Georgios Kollias, Vasileios Kalantzis, Naoki Abe, Mohammed J Zaki. Directed Graph Transformers. Transaction on Machine Learning Research

  32. 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 

  33. V. Sadashivaiah, K. Murugesan, R. Luss, P.-Y. Chen, C. Sims, J. Hendler, & A. Dhurandhar. To Transfer or Not to Transfer: Suppressing Concepts from Source Representations. Transactions on Machine Learning Research 2024

  34. Burak Varıcı, Dmitriy Katz-Rogozhnikov, Dennis Wei, Prasanna Sattigeri, and Ali Tajer. Interventional Causal Discovery in a Mixture of DAGs. In Proc. Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 2024

  35. Burak Varıcı, Emre Acartürk, Karthikeyan Shanmugam, and Ali Tajer. Linear Causal Representation Learning from Unknown Multi-node Interventions. In Proc. Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 2024

  36. Burak Varıcı, Emre Acartürk, Karthikeyan Shanmugam, and Ali Tajer. General Identifiability and Achievability for Causal Representation Learning. In International Conference on Artificial Intelligence and Statistics (AISTATS), Valencia, Spain, May 2024 

  37. Weiqin Chen, Darmashankar Subramanian and Santiago Paternain, “Policy Gradients for Probabilistic Constrained Reinforcement Learning,” 57th Annual Conference on Information Sciences and Systems (CISS), pp. 1-6, Baltimore, MD, USA, March 22-24, 2023.

  38. Weiqin Chen, Dharmashankar Subramanian, Santiago Paternain. “Probabilistic Constraint for Safety-Critical Reinforcement Learning”, IEEE Transactions on Automatic Control, vol: 69, issue: 10, October 2024

  39. Weiqin Chen, James Onyejizu, Long Vu, Lan Hoang, Dharmashankar Subramanian, Koushik Kar, Sandipan Mishra and Santiago Paternain, “Adaptive Primal-Dual Method for Safe Reinforcement Learning”, In The 23rd International Conference on Autonomous Agents and Multi-Agent Systems, Auckland, New Zeland, May 6-10, 2024.

  40. Weiqin Chen and Santiago Paternain, “Generalized Safe Reinforcement Learning”, In Proc. 6th Annual Learning for Dynamics and Control Conference, Oxford, UK July 15-17.

  41. Zhaoxian Wu, Tayfun Gokmen, Malte J. Rasch, and Tianyi Chen. "Towards Exact Gradient-based Training on Analog In-memory Computing." Advances in neural information processing systems, 2024.

  42. Zirui Yan, Dennis Wei, Dmitriy Katz-Rogozhnikov, Prasanna Sattigeri, and Ali Tajer. General Causal Bandits: General Causal Models and Interventions. In International Conference on Artificial Intelligence and Statistics (AISTATS), Valencia, Spain, May 2024

  43. Zirui Yan, Arpan Mukherjee, Burak Varıcı, and Ali Tajer. Robust Causal Bandits for Linear Time-varying Models. IEEE Journal of Selected Areas in Information Theory, 5:78 – 93, March 2024

  44. Zirui Yan, Arpan Mukherjee, Burak Varıcı, and Ali Tajer. Improved Bound for Robust Causal Bandits with Linear Models. In Proc. IEEE International Symposium on Information Theory (ISIT), Athens, Greece, July 2024

  45. Zirui Yan, Arpan Mukherjee, Burak Varıcı, and Ali Tajer. Improved Bound for Robust Causal Bandits with Linear Models. In Proc. IEEE International Symposium on Information Theory (ISIT), Athens, Greece, July 2024

  46. Zirui Yan and Ali Tajer. Linear Causal Bandits: Unknown Graph and Soft Interventions. In Proc. Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 2024 

  47. Naiyu Yin, Hanjing Wang, Tian Gao, Yue Yu, Qiang Ji. Effective Causal Discovery under Identifiable Heteroscedastic Noise Model. AAAI 2024 

  48. Yunshi Wen, Tengfei Ma, Tsui-Wei Weng, Lam M. Nguyen, Anak Agung Julius, "Abstracted Shapes as Tokens - A Generalizable and Interpretable Model for Time-series Classification". NeurIPS 2024

  49. Xuan Zhang, Gabriel Mancino-Ball, Necdet Serhat Aybat, Yangyang Xu. Jointly Improving the Sample and Communication Complexities in Decentralized Stochastic Minimax Optimization. AAAI, 2024

  50. Yating Zhou and Meng Wang, "Unifying Load Disaggregation and Prediction for Buildings with Behind-the-Meter Solar," in IEEE Transactions on Power Systems, 2024

  51. Zirui Yan, Ali Tajer, and Dennis Wei. General Causal Bandits: General Causal Models and Interventions. In Proc. International Conference on Artificial Intelligence and Statistics (AISTATS), Valencia, Spain, May 2024

  52. Zirui Yan, Burak Varıcı, Arpan Mukherjee, and Ali Tajer. Robust Causal Bandits for Linear Time-varying Models. Journal of Selected Areas in Information Theory, January 2024

     

2023

  1. Alam, Md Ibrahim Ibne, Koushik Kar, Theodoros Salonidis, and Horst Samulowitz. "FLASH: Automating Federated Learning using CASH." In Uncertainty in Artificial Intelligence, 2023

  2. Ankita Bhaumik, Praveen Venkateswaran, Yara Rizk, Vatche Isahagian. “TaskDiff: A Similarity Metric for Task-Oriented Conversations”. EMNLP 2023 

  3. Huzaifa Arif, Alex Gittens, Pin-Yu Chen. Reprogrammable-FL: Improving Utility-Privacy Tradeoff in Federated Learning via Model Reprogramming. SaTML 2023

  4. Benjamin Hoover, Yuchen Liang, Bao Pham, Rameswar Panda, Hendrik Strobelt, Duen Horng Chau, Mohammed J. Zaki, Dmitry Krotov. Energy Transformer. NeurIPS 2023 

  5. Bhanushee Sharma, Vijil Chenthamarakshan, Amit Dhurandhar, Shiranee Pereira, James A. Hendler, Jonathan S. Dordick and Payel Das. Accurate Clinical Toxicity Prediction using Multi-task Deep Neural Nets and Contrastive Molecular Explanations. Nature Scientific Reports

  6. Bingsheng Yao, Ishan Jindal, Lucian Popa, Yannis Katsis, Sayan Ghosh, Lihong He, Yuxuan Lu, Shashank Srivastava, Yunyao Li, James Hendler, Dakuo Wang, Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture, Empirical Methods in Natural Language Processing (EMNLP23), December 2023, Singapore.   (preprint: https://arxiv.org/abs/2305.12710)

  7. Bingsheng Yao, Prithviraj Sen, Lucian Popa, James Hendler and Dakuo Wang. Are Human Explanations Always Helpful? Towards Objective Evaluation of Human Natural Language Explanations. ACL 2023 

  8. Bishwajit Saha, Dmitry Krotov, Mohammed Zaki, Parikshit Ram. End-to-end Differentiable Clustering with Associative Memories. ICML 2023. 

  9. Burak Varıcı, Emre Acartürk, Karthikeyan Shanmugam, and Ali Tajer. Score-based Causal Representation Learning from Interventions: Nonparametric Identifiability. In Proc. Conference on Neural In- formation Processing Systems (NeurIPS) – Workshop on Causal Representation Learning, New Orleans, LA, December 2023.

  10. Lisha Chen*, Heshan Fernando*, Yiming Ying, Tianyi Chen, “Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance”, 2023.

  11. Lisha Chen, Momin Abbas, and Tianyi Chen. "A Nested Ensemble Method to Bilevel Machine Learning." IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023.

  12. Weiqin Chen, Dharmashankar Subramanian, Santiago Paternain; "Policy Gradients for Probabilistic Constrained Reinforcement Learning" 57th Annual Conference on Information Sciences and Systems (CISS)

  13. Zijun Cui , Tian Gao, Kartik Talamadupula, and Qiang Ji. Knowledge-augmented Deep Learning and Its Applications: A Survey. IEEE Transactions on Neural Networks and Learning Systems.

  14. Zijun Cui , Tian Gao, Kartik Talamadupula, and Qiang Ji. Knowledge-augmented Deep Learning and Its Applications: A Survey. IEEE Transactions on Neural Networks and Learning Systems. 2023

  15. Debarun Bhattacharjya, Tian Gao ( IBM Research ), Dharmashankar Subramanian, Xiao Shou. Score-Based Learning of Graphical Event Models with Background Knowledge Augmentation. AAAI 2023

  16. Machado Reyes D, Bose A, Karavani E, Parida L. FairPRS: adjusting for admixed populations in polygenic risk scores using invariant risk minimization. Pac Symp Biocomput. 2023;28:198-208. PMID: 36540977. FairPRS: adjusting for admixed populations in polygenic risk scores using invariant risk minimization | Biocomputing 2023 (worldscientific.com)

  17. G. Mancino-Ball, Y. Xu, J. Chen. A Decentralized Primal-Dual Framework for Non-convex Smooth Consensus Optimization. IEEE Transactions on Signal Processing, 71, 525–538, 2023.

  18. Hanjing Wang, Dhiraj Joshi, Shiqiang Wang, Qiang Ji. Gradient-based Uncertainty Attribution for Explainable Bayesian Deep Learning. CVPR 2023

  19. Heshan Fernando, Han Shen, Miao Liu, Subhajit Chaudhury, Keerthiram Murugesan, Tianyi Chen. Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Stochastic Approach. ICLR 2023 

  20. Hongkang Li, Meng Wang, Sijia Liu, and Pin-Yu Chen. A Theoretical Understanding of Vision Transformers: Learning, Generalization, and Sample Complexity. ICLR 2023 

  21. H. Arif, A. Gittens and P. -Y. Chen, "Reprogrammable-FL: Improving Utility-Privacy Tradeoff in Federated Learning via Model Reprogramming," 2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), Raleigh, NC, USA, 2023, pp. 197-209, doi: 10.1109/SaTML54575.2023.00022.

  22. Karan Bhanot, Ioana Baldini, Dennis Wei, Jiaming Zheng, Kristin Bennett. Stress-testing Bias Mitigation Algorithms to Understand Fairness Vulnerabilities, AIES 2023

  23. 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

  24. Lilian Ngweta*, Subha Maity*, Alex Gittens, Yuekai Sun, Mikhail Yurochkin. Simple Disentanglement of Style and Content in Visual Representations. ICML 2023

  25. Gabriel Mancino-Ball, Shengnan Miao, Yangyang Xu, Jie Chen. Proximal Stochastic Recursive Momentum Methods for Nonconvex Composite Decentralized Optimization. AAAI 2023

  26. Md Shamim Hussain, Mohammed J. Zaki, and Dharmashankar Subramanian. The Information Pathways Hypothesis: Transformers are Dynamic Self-Ensembles. KDD 2023

  27. Mohammed Nowaz Rabbani Chowdhury, Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen. Patch-level Routing in Mixture-of-Experts is Provably Sample-efficient for Convolutional Neural Networks. ICML 2023.

  28. Farhad Mohsin, Ao Liu, Pin-Yu Chen, Francesca Rossi, Lirong Xia. Learning to Design Fair and Private Voting Rules. Journal of Artificial Intelligence Research 2023

  29. A. Mukherjee and A. Tajer, "SPRT-based efficient best arm identification in stochastic bandits”, IEEE Journal on Selected Areas in Information Theory (JSAIT), 2023

  30. Quan Xiao, Songtao Lu and Tianyi Chen, "A Generalized Alternating Method for Bilevel Optimization under the Polyak-Lojasiewicz Condition," Neural Information Processing Systems (NeurIPS), 2023 

  31. Han Shen and Tianyi Chen, “On Penalty-based Bilevel Gradient Descent Method” Proc. of Intl. Conf. on Machine Learning (ICML), Honolulu, Hawai'i, July 23 - July 29, 2023.

  32. Han Shen, Songtao Lu, Xiaodong Cui, and Tianyi Chen, “Distributed Offline Policy Optimization Over Batch Data,” Proc. of Intl. Conf. on Artificial Intelligence and Statistics (AISTATS), Valencia, Spain, April 25 April 27, 2023 

  33. Xiao Shou, Tian Gao, Dharmashankar Subramanian, Debarun Bhattacharjya, Kristin Bennett. Concurrent Multi-Label Prediction in Event Streams. AAAI 2023

  34. 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. NeurIPS 2023 

  35. 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. ICLR 2023

  36. Sola Shirai, Debarun Bhattacharjya, Oktie Hassanzadeh. Event Prediction using Case-Based Reasoning over Knowledge Graphs. The Web Conference (WWW) 2023 

  37. Timothy Castiglia, Shiqiang Wang, Stacy Patterson. Flexible Vertical Federated Learning with Heterogeneous Parties. IEEE Transactions on Neural Networks and Learning Systems 2023.

  38. Timothy Castiglia, Yi Zhou, Shiqiang Wang, Swanand Kadhe, Nathalie Baracaldo, Stacy Patterson. LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning. ICML 2023

  39. Burak Varıcı, Emre Acartürk, Karthikeyan Shanmugam, and Ali Tajer. Score-based Causal Represen- tation Learning from Interventions: Nonparametric Identifiability. In Proc. Conference on Neural In- formation Processing Systems (NeurIPS) – Workshop on Causal Representation Learning, New Orleans, LA, December 2023

  40. B. Varici, K. Shanmugam, P. Sattigeri, and  A. Tajer, "Causal bandits for linear structural equation models", Journal of Machine Learning Research (JMLR), 2023

  41. Ruixuan Yan, Yunshi Wen, Debarun Bhattacharjya, Ronny Luss, Tengfei Ma, Achille Fokoue, and Agung Julius; Weighted Clock Logic Point Process, Published conference paper at ICLR 2023

  42. Xiao Shou, Debarun Bhattacharjya, Tian Gao, Dharmashankar Subramanian, Oktie Hassanzadeh, Kristin Bennett. Pairwise Causality Guided Transformers for Event Sequences. NeurIPS 2023

  43. Xiao Shou, Tian Gao, Dharmashankar Subramanian, Debarun Bhattacharjya, Kristin Bennett. Influence-Aware Attention for Multivariate Temporal Point Processes. Causal Learning and Reasoning 2023 conference (CLeaR)

  44. Xiao Shou, Debarun Bhattacharjya, Tian Gao, Dharmashankar Subramanian, Oktie Hassanzadeh, Kristin Bennett. Probabilistic Attention-to-Influence Neural Models for Event Sequences. ICML 2023.

  45. Yonggui Yan, Jie Chen, Pin-Yu Chen, Xiaodong Cui, Songtao Lu, Yangyang Xu. Compressed Decentralized Proximal Stochastic Gradient Method for Nonconvex Composite Problems with Heterogeneous Data. ICML 2023.

  46. Jiajin Zhang, Hanqing Chao, Amit Dhurandhar, Pin-Yu Chen, Ali Tajer, Yangyang Xu, Pingkun Yan. When Neural Networks Fail to Generalize? A Model Sensitivity Perspective. AAAI 2023

  47. Zijun Cui, Chenyi Kuang, Tian Gao, Kartik Talamadupula, Qiang Ji. Biomechanics-guided Facial Action Unit Detection through Force Modeling. CVPR 2023

2022

  1. 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

  2. Ying Xu, Dakuo Wang, Mo Yu, Daniel Ritchie, Bingsheng Yao, Tongshuang Wu, Zheng Zhang, Toby Jia-Jun Li, Nora Bradford, Branda Sun, Tran Bao Hoang, Yisi Sang, Yufang Hou, Xiaojuan Ma, Diyi Yang, Nanyun Peng, Zhou Yu, Mark Warschauer, Fantastic Questions and Where to Find Them: FairytaleQA--An Authentic Dataset for Narrative Comprehension. ACL 2022

  3. Bingsheng Yao, Dakuo Wang, Tongshuang Wu, Zheng Zhang, Toby Jia-Jun Li, Mo Yu, Ying Xu. It is AI’s Turn to Ask Human a Question: Question and Answer Pair Generation for Children Storybooks in FairytaleQA Dataset. ACL 2022

  4. Anirban Das, Timothy Castiglia, Shiqiang Wang, Stacy Patterson. Cross-Silo Federated Learning for Multi-Tier Networks with Vertical and Horizontal Data Partitioning. ACM Transactions on Intelligent Systems and Technology (TIST), accepted for publication, May 2022.

  5. Karan Bhanot, Ioana Baldini, Dennis Wei, Jiaming Zeng and Kristin Bennett .Downstream Fairness Caveats with Synthetic Healthcare Data. Conference on Health, Inference, and Learning (CHIL) 2022.

  6. Lisha Chen, Songtao Lu, Tianyi Chen, "Understanding Benign Overfitting in Nested Meta Learning," Neural Information Processing Systems (NeurIPS), 2022 [Tier-1 AI Conf]

  7. Han Shen and Tianyi Chen, "A Single-Timescale Analysis For Stochastic Approximation With Multiple Coupled Sequences," Neural Information Processing Systems (NeurIPS), 2022 

  8. Anmol Dwivedi, Sihui Wang, and Ali Tajer. Discriminant Analysis under f -divergence Measures. Entropy, 24(2), 2022.

  9. Oktie Hassanzadeh, Parul Awasthy, Ken Barker, Onkar Bhardwaj, Debarun Bhattacharjya, Mark Feblowitz, Aamod Khatiwada, Lee Martie, Steve Fonin Mbouadeu, Jian Ni, Anik Saha, Sola Shirai, Kavitha Srinivas, Lucy Yip. Knowledge-Based News Event Analysis Toolkit. ISWC 2022 (industrial track)

  10. Ibrahim Abdelaziz, Julian Dolby, Jamie McCusker, Kavitha Srinivas. Can Machines Read Coding Manuals Yet? – A Benchmark for Building Better Language Models for Code Understanding. AAAI 2022 

  11. Timothy Castiglia, Anirban Das, Shiqiang Wang, Stacy Patterson.  Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data.  ICML 2022

  12. Zijun Cui, Naiyu Yin, Yuru Wang, and Qiang Ji. "Empirical Bayesian Approaches for Robust Constraint-based Causal Discovery under Insufficient Data" IJCAI 2022

  13. Md Shamim Hussain, Mohammed J. Zaki, and Dharmashankar Subramanian. Global Self-Attention as a Replacement for Graph Convolution. KDD 2022

  14. Hongkang Li, Meng Weng, 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

  15. Zichong Li, Yangyang Xu, Pin-Yu Chen, Sijia Liu, Songtao Lu. Zeroth-order Optimization for Composite Problems with Functional Constraints. AAAI 2022  

  16. Yuchen Liang, Dmitry Krotov, and Mohammed J. Zaki. Modern Hopfield Networks for graph embedding. Frontiers in Big Data 2022

  17. Lisha Chen and Tianyi Chen, "Is Bayesian Model-Agnostic Meta Learning Better than Model-Agnostic Meta Learning, Provably?,” Proc. Intl. Conf. on Artificial Intelligence and Statistics (AISTATS), Virtual, March 28 - 30, 2022.

  18. Ao Liu, Xiaoyu Chen, SijiaLiu, Lirong Xia, Chuang Gan. Certifiably robust interpretation via Rényi differential privacy. Artificial Intelligence. 2022

  19. Mauricio Gruppi, Sibel Adali, Pin-Yu Chen.  SenSE:  A Toolkit for Semantic Change Exploration via Word Embedding Alignment. AAAI 2022

  20. Arpan Mukherjee, Ali Tajer, Pin-Yu Chen, and Payel Das. Active Sampling of Multiple Sources for Sequential Estimation. IEEE Transactions on Signal Processing, 2022

  21. Keerthiram Murugesan, Vijay Sadashivaiah, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen, and Amit Dhurandhar. Auto-Transfer: Learning to Route Transferable Representations. ICLR 2022

  22. Saurabh Sihag and Ali Tajer. Estimating Structurally Similar Graphical Models. IEEE Transactions on Information Theory, accepted for publication (2022).

  23. Saurabh Sihag, Ali Tajer, and Urbashi Mitra. Adaptive Graph-constrained Group Testing. IEEE Transactions on Signal Processing, 70:381 – 396, 2022.

  24. Zijun Cui, Hanjing Wang, Tian Gao, Kartik Talamadupula, Qiang Ji. "Variational Message Passing Neural Network for Maximum-A-Posteriori Inference" UAI 2022

  25. Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer, “Intervention Target Estimation in the Presence of Latent Variables”, UAI 2022

  26. Yangyang Xu, Yibo Xu, Yonggui Yan, and Jie Chen. Distributed stochastic inertial-accelerated methods with delayed derivatives for nonconvex problems. SIAM Journal on Imaging Sciences, 15(2):550–590, 2022.

  27. Ruixuan Yan, Tengfei Ma, Achille Fokoue, Maria Chang, Agung Julius. Neuro-symbolic Models for Interpretable Time Series Classification using Temporal Logic Description. ICDM 2022

  28. Zirui Yan, Quan Xiao, Tianyi Chen, and Ali Tajer. Federated Multi-armed Bandit via Uncoordinated Exploration. In Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Singapore, May 2022.

  29. 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. ICLR 2022 

  30. Zheng Zhang, Ying Xu, Yanhao Wang, Bingsheng Yao, Daniel Ritchie, Tongshuang Wu, Mo Yu, Dakuo Wang, Toby Jia-Jun Li. StoryBuddy: A Human-AI Collaborative Agent for Parent-Child Interactive Storytelling with Flexible Parent Involvement. CHI 2022

2021

  1. Nkechinyere Agu, Joy T. Wu, Hanqing Chao, Ismini Lourentzou, Arjun Sharma, Mehdi Moradi, Pingkun Yan, and James Hendler. AnaXNet: Anatomy Aware Multi-label Finding Classification in Chest X-ray Nkechinyere. MICCAI 2021

  2. Kartik Ahuja, Jun Wang, Amit Dhurandhar, Karthikeyan Shanmugam, Kush R. Varshney. Empirical or Invariant Risk Minimization? A Sample Complexity Perspective. ICLR 2021  https://openreview.net/forum?id=jrA5GAccy_

  3. Timothy Castiglia, Anirban Das, Stacy Patterson. Multi-Level Local SGD: Distributed SGD for Heterogeneous Hierarchical Networks. ICLR 2021  https://openreview.net/forum?id=C70cp4Cn32

  4. Maurício Gruppi, Pin-Yu Chen, and Sibel Adali, “Fake it Till You Make it: Self-Supervised Semantic Shifts for Monolingual Word Embedding Tasks,” AAAI Conference on Artificial Intelligence (AAAI), 2021

  5. Y. Sun, T. Chen and W. Yin, "An Optimal Stochastic Compositional Optimization Method with Applications to Meta Learning," ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021, pp. 3665-3669, doi: 10.1109/ICASSP39728.2021.9414369.

  6. Zijun Cui, Pavan Kapanipathi, Kartik Talamadupula, Tian Gao, and Qiang  Ji, Type-augmented Relation Prediction in Knowledge Graphs,  in Proceedings of the National Conference on Artificial Intelligence (AAAI), virtual conference, 2021.

  7. Zijun Cui, Pavan Kapanipathi, Kartik Talamadupula, Tian Gao, and Qiang Ji. Type-augmented Relation Prediction in Knowledge Graphs. AAAI 2021 https://arxiv.org/abs/2009.07938

  8. Anirban Das, Stacy Patterson.  Multi-Tier Federated Learning for Vertically Partitioned Data.  ICASSP 2021

  9. Exploring the Efficacy of Generic Drugs in Treating Cancer. Ioana Baldini, Mariana Bernagozzi, Sulbha Aggarwal, Mihaela Bornea, Saksham Chawla, Joppe Geluykens, Dmitriy A. Katz-Rogozhnikov, Pratik Mukherjee, Smruthi Ramesh, Sara Rosenthal, Jagrati Sharma, Kush R. Varshney, Catherine Del Vecchio Fitz, Pradeep Mangalath, and Laura B. Kleiman. AAAI Conference on Artificial Intelligence, February 2021.  https://aaai.org/Conferences/AAAI-21/aaai21demoscall/

  10. Tian Gao, Debarun Bhattacharjya, Shankar Subramaniam, Xiao Shou, Nicholas Mattei, Kristin Bennett. Causal Inference for Event Pairs in Multivariate Point Processes. NeurIPS 2021 

  11. Maurício Gruppi, Sibel Adali, Pin-Yu Chen. Fake it Till You Make it: Self-Supervised Semantic Shifts for Monolingual Word Embedding Tasks. AAAI 2021

  12. Dong Hu, Shashanka Ubaru, Alex Gittens, Lior Horesh, Kenneth L. Clarkson, and Vassillis Kalantzis.  Sparse Graph Based Sketching for Numerical Linear Algebra.  ICASSP 2021

  13. Xiao Jin, Pin-Yu Chen, Chia-Yi Hsu, Chia-Mu Yu, Tianyi Chen. Catastrophic Data Leakage in Vertical Federated Learning. NeurIPS 2021 

  14. Joy T. Wu, Nkechinyere Nneka Agu, Ismini Lourentzou, Arjun Sharma, Joseph Alexander Paguio, Jasper Seth Yao, Edward Christopher Dee, William G. Mitchell, Satyananda Kashyap, Andrea Giovannini, Leo Anthony Celi, Mehdi Moradi.  Chest ImaGenome Dataset for Clinical Reasoning.  NeurIPS 2021

  15. Zichong Li, Pin-Yu Chen*, Sijia Liu*, and Yangyang Xu.  Rate-improved Inexact Augmented Lagrangian Method for Constrained Nonconvex Optimization.  Artificial Intelligence and Statistics (AISTATS) 2021 (*alphabetical order) https://arxiv.org/abs/2007.01284

  16. Yuchen Liang, Chaitanya Ryali, Benjamin Hoover, Leopold Grinberg, Saket Navlakha, Mohammed Zaki, Dmitry Krotov. Can a Fruit Fly Learn Word Embeddings? ICLR 2021 https://openreview.net/forum?id=xfmSoxdxFCG

  17. S. Lu, K. Zhang, T. Chen, T. Basar, L. Horesh, "Decentralized policy gradient descent ascent for safe multi-agent reinforcement learning," AAAI 2021

  18. Mauricio Gruppi, Sibel Adali, Pin-Yu Chen.  SenSE:  A Toolkit for Semantic Change Exploration via Word Embedding Alignment. NeurIPS 2021 

  19. Arpan Mukherjee, Ali Tajer, Pin-Yu Chen, and Payel Das.  Active Estimation from Multimodal Data ICASSP 2021

  20. Arpan Mukherjee, Ali Tajer, Pin-Yu Chen, Payel Das. Best Arm Identification in Contaminated Stochastic Bandits. NeurIPS 2021 

  21. Arpan Mukherjee, Ali Tajer, Pin-Yu Chen, Payel Das. Active Binary Classification of Random Fields. IEEE International Symposium on Information Theory 2021

  22. Xiao Shou, Tian Gao, Dharmashankar Subramanian, Kristin P. Bennett. Match2: hybrid self-organizing map and deep learning strategies for treatment effect estimation. 12th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. 

  23. Burak Varici, Saurabh Sihag, Ali Tajer. Learning of Shared Subgraphs in Ising Model Pairs. AISTATS2021

  24. Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer. Scalable Intervention Target Estimation in Linear Models. NeurIPS 2021

  25. Ren Wang, Kaidi Xu, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Chuang Gan, Meng Wang. On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning. ICLR 2021.  https://openreview.net/forum?id=o81ZyBCojoA

  26. Xiangyang Mou, Chenghao Yang, Mo Yu, Bingsheng Yao, Xiaoxiao Guo, Saloni Potdar, Hui Su; Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study. Transactions of the Association for Computational Linguistics 2021; 9 1032–1046. doi: https://doi.org/10.1162/tacl_a_00411

  27. Yue Yu, Tian Gao, Naiyu Yin, Qiang Ji. DAG with No Curl: an efficiency DAG structure learning algorithm. ICML 2021

  28. Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong. Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Sparse Neural Networks. NeurIPS 2021 

2020

  1. Z. Wu, Q. Ling, T. Chen, and G. B. Giannakis, "Federated Variance-Reduced Stochastic Gradient Descent with Robustness to Byzantine Attacks," IEEE Transactions on Signal Processing (TSP), vol. 68, to appear December 2020

  2. T. Chen, Z. Guo, Y. Sun, W. Yin, "CADA: Communication-adaptive distributed Adam,” NeurIPS workshop on Optimization for Machine Learning, 2020

  3. Z. Wu, Q. Ling, T. Chen, and G. B. Giannakis, "Resilient to Byzantine Attacks Finite-Sum Optimization over Networks," Proc. of Intl. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Barcelona, Spain, May 4-9, 2020

  4. T. Chen, X. Jin, Y. Sun, and W. Yin, “VAFL: a Method of Vertical Asynchronous Federated Learning,” Proc. ICML Workshop on Federated Learning for User Privacy and Data Confidentiality, July 2020

    1. Zijun Cui, Tengfei Song, Yuru Wang, and Qiang Ji. Knowledge Augmented Deep Neural Network for Joint Facial Expression and Action Unit Recognition NeurIPS 2020 

  6. Bonnie J. Dorr, Archna Bhatia, Adam Dalton, Brodie Mather, Bryanna Hebenstreit, Sashank Santhanam, Zhuo Cheng, Samira Shaikh, Alan Zemel, Tomek Strzalkowski (2020) Detecting Asks in Social Engineering Attacks: Impact of Linguistic and Structural Knowledge. AAAI-20

  7. Fan, F., Xiong, J., & Wang, G. (2020). Universal approximation with quadratic deep networks. Neural Networks, 124, 383-392. (JOURNAL)

  8. Fenglei Fan, Mengzhou Li, Yueyang Teng, Ge. Wang Soft-Autoencoder and Its Wavelet Shrinkage Interpretation. IEEE Transactions on Computational Imaging. arXiv preprint arXiv:1812.11675.

  9. N. Joseph Tatro, Pin-Yu Chen, Payel Das, Igor Melnyk, Prasanna Sattigeri, Rongjie Lai.  Optimizing Mode Connectivity via Neuron Alignment, NeuRIPS 2020.  https://arxiv.org/abs/2009.02439

  10. Ingkarat Rak-amnouykit, Daniel McCrevan, Ana Milanova, Martin Hirzel, Julian Dolby. Python 3 Types in the Wild: A Tale of Two Type Systems. Dynamic Languages Symposium (DLS), pages 57-70, November 2020. https://doi.org/10.1145/3426422.3426981 https://conf.researchr.org/details/dls-2020/dls-2020-papers/5/Python-3-Types-in-the-Wild-A-Tale-of-Two-Type-Systems 

  11. Nidhi Rastogi, Sharmishtha Dutta, Mohammed J Zaki, Alex Gittens, Charu Aggarwal, “MALOnt: An Ontology for Malware Threat Intelligence”, Proceedings of the The First MLHat Workshop at (SIGKDD’20). Virtual, August 2020.

  12. Nidhi Rastogi and Qicheng Ma, “DANTE: Odd ones out - Deep learning using system logs to detect Insider Threat”, Proceedings of the 19th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom 2020), December 2020, Guangzhou, China.

  13. Sergio Dorado Rojas, Bhanukiran Vinzamuri and Luigi Vanfretti. Orthogonal Recurrent Neural Networks. NeurIPS 2020 Machine Learning and the Physical Sciences Workshop.

  14. Tomek Strzalkowski, Anna Newheiser, Nathan Kemper, Ning Sa, Bharvee Acharya and Gregorios Katsios (2020) Generating Ethnographic Models from Communities’ Online Data. Proceedings of ACL workshop on Figurative Language, ACL-2020 conference.

  15. Ren Wang, Gaoyuan Zhang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong, Meng Wang.  Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Cases. The European Conference on Computer Vision, (ECCV) 2020 

  16. Ren Wang, Meng Wang, Jinjun Xiong, “Achieve Data Privacy and Clustering Accuracy Simultaneously Through Quantized Data Recovery,” EURASIP Journal on Advances in Signal Processing, May 2020

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

  18. Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong. Guaranteed Convergence of Training Convolutional Neural Networks via Accelerated Gradient Descent. CISS 2020

  19. Shuai Zhang, Meng Wang, Jinjun Xiong, Sijia Liu, Pin-Yu Chen. Learning One-hidden-layer Convolutional Neural Networks via Accelerated Gradient Descent with Generalizability Guarantees. IEEE Transactions on Neural Networks and Learning Systems

2019

  1. J. Sun, T. Chen, G. B. Giannakis, and Z. Yang, "Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients," Proc. of Neural Information Processing (NeurIPS), Vancouver, Canada, December 8-14, 2019.

  2. David Dahlbom and Jonas Braasch (2019): "Multiple f0 pitch estimation for musical applications using dynamic Bayesian networks and learned priors." The Journal of the Acoustical Society of America 145, 1814.

  3. David De Roure, James A. Hendler, Diccon James, Terhi Nurmikko-Fuller, Max Van Kleek, Pip Willcox, Towards a Cyberphysical Web Science: A Social Machines Perspective on Pokémon GO! WebSci 2019: 65-69

  4. Gregorios Katsios, Ning Sa, and Tomek Strzalkowski (2019) Social Convos: A New Approach to Modeling Information Diffusion in Social Media. Proceedings of 10th AHFE Conference, Washington, DC. Springer Nature

  5. Nathan Keil, David Dahlbom, Jeremy Stewart, Matthew Goodheart, Curtis Bahn, Mary Simoni, Michael Perrone, Jonas Braasch (2019). "Polyphonic pitch perception in rooms using deep learning networks with data rendered in auditory virtual environments." Proceedings of the Acoustical Society of America.

  6. Matthew Klawonn, Eric Heim and James Hendler, Exploiting Class Learnability in Noisy Data, Proc. National Conference on Artificial Intelligence (AAAI 19), Honolulu, Hawaii, 2019.

  7. Lerch, R. A. & Sims, C. R. (2019a). "Rate-Distortion Theory and Computationally Rational Reinforcement Learning." Proceedings of Reinforcement Learning and Decision Making (RLDM) 2019, Montreal, Canada.

  8. Mengyi Li, Lirong Xia and Oshani Seneviratne. “Leveraging Standards Based Ontological Concepts in Distributed Ledgers: A Healthcare Smart Contract Example”; Proceedings of the IEEE International Conference on Decentralized Applications and Infrastructures 2019.

  9. Shuze Liu, Farhad Mohsin, Lirong Xia and Oshani Seneviratne. “Strengthening Smart Contracts to Handle Unexpected Situations”; Proceedings of the IEEE International Conference on Decentralized Applications and Infrastructures 2019.

  10. Bassem Makni, James A. Hendler: Deep learning for noise-tolerant RDFS reasoning. Semantic Web Journal, 10(5): 823-862 (2019)

  11. Malloy, T. J., Lerch, R. A., Fang, Z. & Sims, C. R. (2019). "Predicting Human Choice in a Multi-Dimensional N-Armed Bandit Task Using Actor-Critic Feature Reinforcement Learning." Proceedings of Reinforcement Learning and Decision Making (RLDM) 2019, Montreal, Canada.

  12. Jeremy Stewart, Matthew Goodheart, Curtis Bahn, Mary Simoni, Jonas Braasch (2019). "Music Intelligence and Knowledge Agent (MIKA)." Proceedings of the International Computer Music Conference.

  13. Yanlin Zhu, Lirong Xia, and Oshani Seneviratne. “A Proposal for Account Recovery in Decentralized Applications”; Proceedings of the IEEE Blockchain Conference 2019.

2018

  1. Bringsjord, S., G., Naveen S., Sen, A., Peveler, M., Srivastava, B., Talamadupula, K. (2018). "Tentacular Artificial Intelligence, and the Architecture Thereof, Introduced." In the Proceedings of the 1st International FAIM Workshop on Architectures and Evaluation for Generality, Autonomy & Progress in AI (AEGAP 2018), Stockholm, Sweden, 2018, held in conjunction with IJCAI-ECAI 2018, AAMAS 2018 and ICML 2018.

  2. Lerch, R. A. & Sims, C. R. (2018). "Exploration and policy generalization in capacity-limited reinforcement learning." Proceedings of the International Conference on Machine Learning (ICML) Workshop on Exploration in RL, Stockholm, Sweden.

  3. Peveler, M., G., Naveen S., Bringsjord, S., Sen, A. et al. "Toward Cognitive-and-Immersive Systems: Experiments in a Cognitive Microworld." Forthcoming in the Proceedings of the 6th Annual Conference on Advances in Cognitive Systems, Stanford, CA, USA, 2018.

  4. Sen, A., G., Naveen S., Bringsjord, S., Ghosh, R., Mayol, P., Srivastava, B., Talamadupula, K. (2018). "Toward a Smart City using Tentacular AI." Proceedings of the 2018 European Conference on Ambient Intelligence, Larnaca, Cyprus, 2018.

  5. Ren Wang, Meng Wang, and Jinjun Xiong. "Data Recovery and Subspace Clustering from Quantized and Corrupted Measurements." IEEE Journal of Selected Topics in Signal Processing, Special Issue on Robust Subspace Learning and Tracking: Theory, Algorithms, and Applications, 2018, 12(6): 1547-1560.


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