Publications

 

  • Menglu Yu, Ye Tian, Bo Ji, Chuan Wu, Hridesh Rajan, and Jia Liu, "GADGET: Online Resource Optimization for Scheduling Ring-All-Reduce Learning Jobs," in Proc. IEEE INFOCOM, Virtual Event, May 2022
  • Mohit Jangid, and Zhiqiang Lin. "Towards A TEE-based V2V Protocol For Connected And Autonomous Vehicles". In Proceedings of the Automotive and Autonomous Vehicle Security (AutoSec) Workshop 2022, San Diego, CA, April 2022. 
  • C. Katsis, F. Cicala, D. Thomsen, N. Ringo, “NEUTRON: A Graph-based Pipeline for Zero-Trust Network Architectures,” CODASPY '22: 2022 ACM Conference on Data and Application Security and Privacy, April 24--26, 2022.
  • Honghao Wei, Xin Liu, and Lei Ying. "A Provably-Efficient Model-Free Algorithm for Infinite-Horizon Average-Reward Constrained Markov Decision Processes." AAAI, 2022.
  • Honghao Wei, Xin Liu, and Lei Ying. “Triple-Q: A Model-Free Algorithm for Constrained Reinforcement Learning with Sublinear Regret and Zero Constraint Violation.” 25th International Conference on Artificial Intelligence and Statistics (AISTATS), March 2022. 
  • V. Chen, J. Li, J. Kim, G. Plumb, A. Talwalkar. Interpretable Machine Learning: Moving From Mythos to Diagnostics .Communications of the ACM (CACM), 2022.
  • Gurukar, Saket, Boettner, Bethany, Browning, Christopher, Calder, Catherine and Parthasarathy, Srinivasan. “Leveraging Network Representation Learning and Community Detection for Analyzing the Activity Profiles of Adolescents”. In Applied Network Science 2022 (To Appear).
  • L. Bonati, M. Polese, S. D’Oro, S. Basagni, T. Melodia, “OpenRAN Gym: An Open Toolbox for Data Collection and Experimentation with AI in O-RAN,” submitted to IEEE Workshop on Open RAN Architecture for 5G Evolution and 6G, colocated with IEEE WCNC 2022.
  • D. Roy, Y. Li, T. Jian, P. Tian, K. R. Chowdhury, and S. Ioannidis, "Multi-modality Sensing and Data Fusion for Multi-vehicle Detection". In IEEE Transactions on Multimedia, accepted, 2022.
  • Ziwei Guan, Tengyu Xu, Yingbin Liang. “PER-ETD: A Polynomially Efficient Emphatic Temporal Difference Learning Method”, International Conference on Learning Representations (ICLR), 2022.
  • M. Polese, L. Bonati, S. D'Oro, S. Basagni, T. Melodia, "ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platforms", submitted to IEEE Transactions on Mobile Computing (preprint available as arXiv:2112.09559 [cs.NI]), 2022.
  • J. Huang and N. Jiang. On the Convergence Rate of Density Ratio Learning Based Off-Policy Policy Gradient. AISTATS 2022. 
  • D. Vial, A. Parulekar, S. Shakkottai and R. Srikant. Improved Algorithms for Misspecified Linear Markov Decision Processes. AISTATS 2022.
  • K. Thekumparampil, N. He, S. Oh, “Lifted Primal-Dual Method for Bilinearly Coupled Smooth Minimax Optimization”, AISTATS 2022
  • Yuntian Deng, Xingyu Zhou, and Ness B. Shroff, “Weighted Gaussian Process Bandits for Non-stationary Environments,” to appear in the 25th International Conference on Artificial Intelligence and Statistics (AISTATS), March 2022.
  • B. Salehihikouei, J. Gu, D. Roy, and K. R. Chowdhury, “FLASH: Federated Learning for Automated Selection of High-band mmWave Sectors,” to appear in IEEE INFOCOM 2022. 
  • Y. Liu, Y. Li, L. Su, E. Yeh, and S. Ioannidis, “Experimental Design Networks:A Paradigm for Serving Heterogeneous Learners under Networking Constraints”, to appear in IEEE INFOCOM 2022. [Preprint][code]
  • J. Pan, A. Bedewy, Y. Sun, and N. B. Shroff, “Optimizing Sampling for Data Freshness: Unreliable Transmissions with Random Two-way Delay,” to appear in IEEE INFOCOM 2022. 
  • S. Kang, A. Eryilmaz, and N. B. Shroff, “Remote Tracking of Distributed Dynamic Sources over A Random Access Channel with One-bit Updates,” WIOPT 2022 (selected for fast track publication in IEEE Transactions on Network Science and Engineering).
  • Y. Li, T. Si-Salem, G. Neglia, and S. Ioannidis, “Online Caching Networks with Adversarial Guarantees”,  to appear in the International Conference on Measurements and Modeling of Computer Systems (SIGMETRICS), Mumbai, India, 2022. [Preprint][code]
  • J. Yun, S. Srivastava, D. Roy, N. Stohs, C. Myldarz, M. Salman, B. Steers, J.P. Bello, and A. Arora, “Infrastructure-free, Deep Learned Urban Noise Monitoring at ∼100mW”, to appear in ACM/IEEE 13th International Conference in Cyber Physical Systems (ICCPS), 2022.
  • H. Zhang, Y. Guan, A. Kamal, D. Qiao, M. Zheng, A. Arora, O. Boyraz, et al, “ARA: A Wireless Living Lab Vision for Smart and Connected Rural Communities”,  Proceedings of the 15th ACM Workshop on Wireless Network Testbeds, Experimental Evaluation & Characterization (WiNTECH'21), 9–16.
  • He,Yuntian, Gurukar, Saket, Kousha, Pouya, Subramoni, Hari, Panda, Dhabaleswar and Parthasarathy, Srinivasan. “DistMILE: A Distributed Multi-Level Framework for Scalable Graph Embedding”. In 2021 IEEE 28th International Conference on High Performance Computing, Data, and Analytics (HiPC) (pp. 282-291). IEEE.
  • Saket Gurukar, Srinivasan Parthasarathy, Rajiv Ramnath, Catherine A. Calder, Sobhan Moosavi: LocationTrails: a federated approach to learning location embeddings. IEEE ASONAM 2021: 377-384.  
  • Bajaj, G., Current, S., Schmidt, D., Bandyopadhyay, B., Myers, C. W., & Parthasarathy, S. (2021). Knowledge Gaps: A Challenge for Agent‐Based Automatic Task Completion. Topics in Cognitive Science
  • E. Bertino, I. Karim, “AI-powered Network Security: Approaches and Research Directions,” invited paper, 8th NSysS 2021: 8th International Conference on Networking, Systems and Security, December 21 - 23, 2021.
  • S. R. Hussain, I. Karim, A. Al Ishtiaq, O. Chowdhury, E. Bertino, “Noncompliance as Deviant Behavior: An Automated Black-box Noncompliance Checker for 4G LTE Cellular Devices,” CCS '21: 2021 ACM SIGSAC Conference on Computer and Communications Security, November 15 - 19, 2021. 
  • Junjie Yang, Kaiyi Ji, Yingbin Liang. “Provably faster algorithms for bilevel optimization”, Proc. Advances in Neural Information Processing Systems (NeurIPS), Spotlight, 2021.
  • X. Liu, W. Kong, S. Kakade, S. Oh, “Robust and differentially private mean estimation”, NeurIPS 2021
  • K. Thekumparampil, P. Jain, P. Netrapalli, S. Oh, “Statistically and Computationally Efficient Linear Meta-representation Learning”, NeurIPS 2021
  • S. Zhang and N. Jiang. Towards Hyperparameter-free Policy Selection for Offline Reinforcement Learning. NeurIPS 2021.
  • T. Xie, C. Cheng, N. Jiang, P. Mineiro, and A. Agarwal. Bellman-consistent Pessimism for Offline Reinforcement Learning. NeurIPS 2021 (selected for oral presentation).
  • T. Xie, N. Jiang, H. Wang, C. Xiong, and Y. Bai. Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning. NeurIPS 2021.
  • M. Khodak, R. Tu, T. Li, L. Li, M.F. Balcan, V. Smith, A. Talwalkar, “Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing,”in Proceedings of Neural Information Processing Systems (NeurIPS), 2021.
  • W. Ren, J. Liu, and N. B. Shroff, ''Sample Complexity Bounds for Active Ranking from Multi-wise Comparisons,” to appear in NeurIPS, 2021
  • Prashant Khanduri, Pranay Sharma, Haibo Yang, Mingyi Hong, Jia Liu, Ketan Rajawat, and Pramod Varshney, "STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning," in Proc. NeurIPS, Virtual Event, Dec. 2021.
  • Xin Zhang, Zhuqing Liu, Zhengyuan Zhu, and Songtao Lu, "Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning," in Proc. NeurIPS, Virtual Event, Dec. 2021
  • S. Leng and A. Yener, Learning to Transmit Fresh Information in Energy Harvesting Networks Using Supervised Learning, in Proceedings of the 55th Asilomar Conference on Signals, Systems and Computers, virtual, Nov 2021.
  • B. Guler and A. Yener, A Framework for Sustainable Federated Learning, in Proceedings of the IEEE International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt’21, virtual, Oct. 2021.
  • Q. Li, C. Peng, “Reconfiguring Cell Selection in 4G/5G Networks”, ICNP 2021.
  • Z. Shi, A. Eryilmaz, ‘‘Communication-efficient Subspace Methods for High-dimensional Federated Learning’’, in Proceedings of The 17th International Conference on Mobility, Sensing and Networking (MSN), 2021.