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_
Timothy Castiglia, Anirban Das, Stacy Patterson. Multi-Level Local SGD: Distributed SGD for Heterogeneous Hierarchical Networks. ICLR 2021 https://openreview.net/forum?id=C70cp4Cn32
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
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.
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
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/
Maurício Gruppi, Sibel Adali, Pin-Yu Chen. Fake it Till You Make it: Self-Supervised Semantic Shifts for Monolingual Word Embedding Tasks. AAAI 2021
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
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
S. Lu, K. Zhang, T. Chen, T. Basar, L. Horesh, "Decentralized policy gradient descent ascent for safe multi-agent reinforcement learning," AAAI 2021
Arpan Mukherjee, Ali Tajer, Pin-Yu Chen, and Payel Das. Active Estimation from Multimodal Data ICASSP 2021
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
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
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
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
T. Chen, Z. Guo, Y. Sun, W. Yin, "CADA: Communication-adaptive distributed Adam,” NeurIPS workshop on Optimization for Machine Learning, 2020
- Zijun Cui, Tengfei Song, Yuru Wang, and Qiang Ji. Knowledge Augmented Deep Neural Network for Joint Facial Expression and Action Unit Recognition NeurIPS 2020
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
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.
Fan, F., Xiong, J., & Wang, G. (2020). Universal approximation with quadratic deep networks. Neural Networks, 124, 383-392. (JOURNAL)
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.
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.
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.
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
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
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
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)
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
Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong. Guaranteed Convergence of Training Convolutional Neural Networks via Accelerated Gradient Descent. CISS 2020
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.
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.
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
Matthew Klawonn, Eric Heim and James Hendler, Exploiting Class Learnability in Noisy Data, Proc. National Conference on Artificial Intelligence (AAAI 19), Honolulu, Hawaii, 2019.
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
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.
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.
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.
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.
Bassem Makni, James A. Hendler: Deep learning for noise-tolerant RDFS reasoning. Semantic Web Journal, 10(5): 823-862 (2019)
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.
Jeremy Stewart, Matthew Goodheart, Curtis Bahn, Mary Simoni, Jonas Braasch (2019). "Music Intelligence and Knowledge Agent (MIKA)." Proceedings of the International Computer Music Conference.
Yanlin Zhu, Lirong Xia, and Oshani Seneviratne. “A Proposal for Account Recovery in Decentralized Applications”; Proceedings of the IEEE Blockchain Conference 2019.
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.
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.
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.
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.
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.