Research Products
Research Products
- I.Karim, Abdullah Al Ishtiaq, Syed Rafiul Hussain, Elisa Bertino (Purdue), “BLEDiff: Scalable and Property-Agnostic Noncompliance Checking for BLE Implementations,” Accepted at the 23rd IEEE Symposium on Security and Privacy, to be held on May 22-24, 2023. [full paper]
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L. Bonati, M. Polese, S. D’Oro, S. Basagni, and T. Melodia, "NeutRAN: An Open RAN Neutral Host Architecture for Zero-Touch RAN and Spectrum Sharing," arXiv:2301.07653 [cs.NI], pp. 1-13, January 2023. [full paper]
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Honghao Wei, Arnob Ghosh, Xingyu Zhou, Lei Ying, Ness Shroff, “Provably Efficient Model-Free Algorithms for Non-stationary CMDPs,” AISTATS 2023.
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Zixian Yang, R. Srikant and Lei Ying, “Learning While Scheduling in Multi-Server Systems With Unknown Statistics: MaxWeight with Discounted UCB,” AISTATS 2023. [full paper]
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Peizhong Ju, Yingbin Liang, Ness Shroff, “Theoretical characterization of the generalization performance of overfitted meta-learning,” International Conference on Learning Representations (ICLR), 2023.
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Ming Shi, Yingbin Liang, Ness Shroff. “Near-optimal adversarial reinforcement learning with switching costs,” International Conference on Learning Representations (ICLR), Spotlight, 2023.
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J. Gu, L. Collins, D. Roy, A. Mokhtari, S. Shakkottai, and K. R. Chowdhury, “Meta-learning for Image-guided Millimeter-wave Beam Selection in Unseen Environments,” submitted to ICASSP 2023.
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J. Zhang, J.Katz-Samuels, and R. Nowak, “GALAXY: Graph-based Active Learning at the Extreme,” ICML 2023.
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Yinbing Liu, Chunyi Peng, “A Close Look at 5G in the Wild: Unrealized Potentials and Implications,” To appear at INFOCOM’23.
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J. Katz-Samuels, J. Nakhleh, R. Nowak, and Y. Li, “Training OOD Detectors in their Natural Habitats,” Proceedings of the 39th International Conference on Machine Learning, PMLR, 162:10848-10865, 2022.
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D. Jhunjhunwala, P. Sharma, A. Nagarkatti, G. Joshi, “FedVARP: Tackling the Variance Due to Partial Client Participation in Federated Learning,” in the Proceedings of the Uncertainty in Artificial Intelligence Conference, Aug 2022. [full paper]
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P. Sharma, R. Panda, G. Joshi, P. Varshney, “Federated Minmax Optimization: Improved Convergence Analyses and Algorithms,”a in the Proceedings of the International Conference on Machine Learning (ICML), July 2022. [full paper]
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J. Pan, A. Bedewy, Y. Sun, and N. B. Shroff, “Optimizing Sampling for Data Freshness: Unreliable Transmissions with Random Two-way Delay,” IEEE INFOCOM 2022. [full paper]
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S. Cayci, Y. Zheng, A. Eryilmaz, “A Lyapunov-Based Methodology for Constrained Optimization with Bandit Feedback,” in Proceedings of Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22), 2022.
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Z. Shi and A. Eryilmaz, “A Bayesian Approach for Stochastic Continuum-armed Bandit with Long-term Constraints,” Conference on Artificial Intelligence and Statistics (AISTATS), 2022.
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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,” ACM/IEEE 13th International Conference in Cyber Physical Systems (ICCPS), 2022.
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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.
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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.
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Yuntian Deng, Xingyu Zhou, and Ness B. Shroff, “Weighted Gaussian Process Bandits for Non-stationary Environments,” 25th International Conference on Artificial Intelligence and Statistics (AISTATS), March 2022.
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Ziwei Guan, Tengyu Xu, Yingbin Liang. “PER-ETD: A Polynomially Efficient Emphatic Temporal Difference Learning Method,” International Conference on Learning Representations (ICLR), 2022.
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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.
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B. Salehihikouei, J. Gu, D. Roy, and K. R. Chowdhury, “FLASH: Federated Learning for Automated Selection of High-band mmWave Sectors,” IEEE INFOCOM 2022.
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Y. Liu, Y. Li, L. Su, E. Yeh, and S. Ioannidis, “Experimental Design Networks: A Paradigm for Serving Heterogeneous Learners under Networking Constraints,” IEEE INFOCOM 2022. [full paper]
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Y. Li, T. Si-Salem, G. Neglia, and S. Ioannidis, “Online Caching Networks with Adversarial Guarantees,” International Conference on Measurements and Modeling of Computer Systems (SIGMETRICS), Mumbai, India, 2022. [full paper]
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Alexia Atsidakou, Orestis Papadigenopoulos, Constantine Caramanis, Sujay Sanghavi, Sanjay Shakkottai, “Asymptotically Optimal Gaussian Bandits with Side Observations,” Proceedings of the International Conference on Machine Learning, Baltimore 2022. [full paper]
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J. Huang and N. Jiang, “On the Convergence Rate of Density Ratio Learning Based Off-Policy Policy Gradient,” AISTATS 2022.
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D. Vial, A. Parulekar, S. Shakkottai and R. Srikant, “Improved Algorithms for Mis-specified Linear Markov Decision Processes,” AISTATS 2022.
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K. Thekumparampil, N. He, S. Oh, “Lifted Primal-Dual Method for Bilinearly Coupled Smooth Minimax Optimization,” AISTATS 2022.
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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,” IEEE Transactions on Mobile Computing (preprint available as arXiv:2112.09559 [cs.NI]), January 2022.
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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 IEEE Workshop on Open RAN Architecture for 5G Evolution and 6G, collocated with IEEE WCNC 2022.
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T. Li, H. Seferoglu, E. Koyuncu, “REDIT: Resilient Distributed Text-to-Speech at Edge Networks,” submitted to IEEE Globecom, April 2022.
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J. Chen, N. Jiang, “Offline Reinforcement Learning Under Value and Density-Ratio Realizability: The Power of Gaps,” UAI 2022. [full paper]
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W. Zhan, B. Huang, A. Huang, N. Jiang, J. D. Lee, “Offline Reinforcement Learning with Realizability and Single-policy Concentrability,” COLT 2022. [full paper]
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C. Cheng, T. Xie, N. Jiang, A. Agarwal, “Adversarially Trained Actor Critic for Offline Reinforcement Learning,” ICML 2022. [full paper]
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C. Shi, M. Uehara, J. Huang, N. Jiang, “A Minimax Learning Approach to Off-Policy Evaluation in Partially Observable Markov Decision Processes,” ICML 2022. [full paper]
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H. Lee, I. Karim, N. Li, E. Bertino. “VWAnalyzer: A Systematic Security Analysis Framework for the Voice over WiFi Protocol,” ASIA CCS 2022: the 2022 on Asia Conference on Computer and Communications Security p. 182-195. [full paper]
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H. Wei, X. Liu, L. Ying, “Triple-Q: A Model-Free Algorithm for Constrained Reinforcement Learning with Sublinear Regret and Zero Constraint Violation,” In International Conference on Artificial Intelligence and Statistics (pp. 3274-3307). PMLR, 2022. [full paper]
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H. Wei, X. Liu, L. Ying, “A Provably-Efficient Model-Free Algorithm for Infinite-Horizon Average-Reward Constrained Markov Decision Processes,” AAAI 2022. [full paper]
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Xiyang Liu, Weihao Kong, Sewoong Oh, “Differential privacy and robust statistics in high dimensions,” COLT, 2022. [full paper]
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Charlie Hou, Kiran K. Thekumparampil, Giulia Fanti, Sewoong Oh, “FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated Learning,” ICLR, 2022. [full paper]
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Kiran Koshy Thekumparampil, Niao He, Sewoong Oh, “Lifted Primal-Dual Method for Bilinearly Coupled Smooth Minimax Optimization,” AISTATS, 2022. [full paper]
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Haibo Yang, Peiwen Qiu, Jia Liu and Aylin Yener, “Over-the-Air Federated Learning With Joint Adaptive Computation and Power Control,” ISIT 2022. [full paper]
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Jiayu Mao*, Haibo Yang*, Peiwen Qiu, Jia Liu, and Aylin Yener, “CHARLES: Channel-Quality-Adaptive Over-the-Air Federated Learning overWireless Networks,” SPAWC 2022. [full paper]
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Milionis, Jason; Kalavasis, Alkis; Fotakis, Dimitris; & Ioannidis, Stratis (2022), “Differentially Private Regression with Unbounded Covariates,” Proceedings of The 25th International Conference on Artificial Intelligence and Statistics (AISTATS), in Proceedings of Machine Learning Research, 151:3242-3273. [full paper]
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Samarth Gupta, Jinhang Zuo, Carlee Joe-Wong, Gauri Joshi, Osman Yagan, “Correlated Combinatorial Bandits for Online Resource Allocation,” ACM MobiHoc, International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, Oct 2022, Best Poster Award for a short version presented at SIGMETRICS 2022. [full paper]
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Tuhinangshu Choudhury, Weina Wang, Gauri Joshi, “Tackling Heterogeneous Traffic in Multi-access Systems via Erasure Coded Servers,” ACM MobiHoc, International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, Oct 2022, Best Paper Award. [full paper]
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Divyansh Jhunjhunwala, Pranay Sharma, Aushim Nagarkatti, Gauri Joshi, “FedVARP: Tackling the Variance Due to Partial Client Participation in Federated Learning,” Conference on Uncertainty in Artificial Intelligence (UAI), Aug 2022. [full paper]
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V. Chen, N. Johnson, N. Topin, G. Plumb, A. Talwalkar, “Use-Case-Grounded Simulations for Explanation Evaluation,” Neural Information Processing Systems (NeurIPS), 2022. [full paper]
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J. Shen, M. Khodak, A. Talwalkar, “Efficient Architecture Search for Diverse Tasks,” Neural Information Processing Systems (NeurIPS), 2022. [full paper]
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R. Tu, N. Roberts, M. Khodak, J. Shen, F. Sala, A. Talwalkar, “NAS-Bench-360: Benchmarking Diverse Tasks for Neural Architecture Search,” Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track, 2022. [full paper]
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A.Ghosh, X. Zhou, N. Shroff, “Provably Efficient Model-Free Constrained RL with Linear Function Approximation,” 36th Advanced Neural Information Processing Systems (NeurIPS), 2022. [full paper]
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Y. Deng, X. Zhou, A. Ghosh, A. Gupta and N. B. Shroff, "Interference Constrained Beam Alignment for Time-Varying Channels via Kernelized Bandits," 2022 20th International Symposium on Modeling and Optimization in Mobile, Ad hoc, and Wireless Networks (WiOpt), 2022, pp. 25-32, (Runner-up for the best student paper award). [full paper]
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J. Huang, J. Chen, L. Zhao, T. Qin, N. Jiang, T. Liu, “Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality,” (ICLR), 2022. [full paper]
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C. Cheng, T. Xie, N. Jiang, A. Agarwal, “Adversarially Trained Actor Critic for Offline Reinforcement Learning,” International Conference on Machine Learning (ICML), 2022. Outstanding Paper Runner-Up. [full paper]
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A. Huang, N. Jiang, “Beyond the Return: Off-policy Function Estimation under User-specified Error-measuring Distributions,” Neural Information Processing Systems (NeurIPS), 2022. [full paper]
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Canal, G., Mason, B., Vinayak, R. K., & Nowak, R. D., “One for All: Simultaneous Metric and Preference Learning over Multiple Users,” In Advances in Neural Information Processing Systems. [full paper]
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Zhu, Y. and Nowak, R.D., “Active Learning with Neural Networks: Insights from Nonparametric Statistics,” In Advances in Neural Information Processing Systems, 2022. [full paper]
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A. Singla, E.Bertino (Purdue), “DP-ADA: Differentially Private Adversarial Domain Adaptation for Training Deep Learning based Network Intrusion Detection Systems,” The 8th IEEE International Conference on Collaboration and Internet Computing, Dec.14-16, 2022.
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H. Lee, A. Mudgerikar, A. Kundu, N. Li, E. Bertino (Purdue), “An Infection-Identifying and Self-Evolving System for IoT Early Defense from Multi-Step Attacks,” ESORICS 2022 - 27th European Symposium on Research in Computer Security, Copenhagen, Denmark, September 26-30, 2022, Proceedings, Part II. Lecture Notes in Computer Science 13555, Springer 2022. [full paper]
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L. Bonati, M. Polese, S. D’Oro, S. Basagni, and T. Melodia, “OpenRAN Gym: An Open Toolbox for Data Collection and Experi- mentation with AI in O-RAN,” in Proc. of IEEE WCNC Workshop on Open RAN Architecture for 5G Evolution and 6G, Austin, TX, USA, April 2022. [full paper]
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L. Bonati, M. Polese, S. D’Oro, S. Basagni, and T. Melodia, “Intelligent Closed-loop RAN Control with xApps in OpenRAN Gym,” in Proceedings of European Wireless, Dresden, Germany, September 2022. [full paper]
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Kaiyi Ji, Mingrui Liu, Yingbin Liang, Lei Ying, "Will bilevel optimizers benefit from loops," Advances in Neural Information Processing Systems (NeurIPS), Spotlight, 2022. [full paper]
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Daouda Sow, Kaiyi Ji, Yingbin Liang, "On the convergence theory for Hessian-free bilevel algorithms," Advances in Neural Information Processing Systems (NeurIPS), 2022. [full paper]
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J. Regatti, A. Gupta, S. Lu, and N. B. Shroff, “Conditional Moment Alignment for Improved Generalization in Federated Learning,” FL-NeurIPS 2022 (best paper award).
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Srivastava, Sangeeta, Ho-Hsiang Wu, Joao Rulff, Magdalena Fuentes, Mark Cartwright, Claudio Silva, Anish Arora, and Juan Pablo Bello, "A study on robustness to perturbations for representations of environmental sound," In 2022 30th European Signal Processing Conference (EUSIPCO), pp. 125-129. IEEE, 2022. [full paper]
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Raef Bassily and Xinyu Zhou, “Task-level Differentially Private Meta Learning,” Advances in Neural Information Processing Systems (NeurIPS), 2022.
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Xiyang Liu, Weihao Kong, Prateek Jain, Sewoong Oh, “DP-PCA: Statistically Optimal and Differentially Private PCA,” Advances in Neural Information Processing Systems (NeurIPS), 2022. [full paper]
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B. Salehi, J. Gu, D. Roy, and K. Chowdhury, “FLASH: Federated Learning for Automated Selection of High-band mmWave Sectors,” IEEE INFOCOM 2022, London, United Kingdom, May 2022.
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L. Collins, A. Mokhtari, S. Oh and S.Shakkottai, “MAML and ANIL Provably Learn Representations,” Proceedings of the 39th International Conference on Machine Learning (ICML), Baltimore, MD, July 2022.
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D. Vial, A. Parulekar, S. Shakkottai and R. Srikant, “Improved Algorithms for Mis-specified Linear Markov Decision Processes,” To appear in Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS), Virtual Conference, April 2022.
- 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.
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W. Ren, J. Liu, and N. B. Shroff, “Sample Complexity Bounds for Active Ranking from Multi-wise Comparisons,” NeurIPS, 2021, Dec. 2021.
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D. Jhunjhunwala, A. Mallick, A. Gadhikar, S. Kadhe, G. Joshi, “Leveraging Spatial and Temporal Correlations in Sparsified Mean Estimation,” in the Proceedings of Neural Information Processing Systems (NeurIPS), Dec 2021. [full paper]
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Mallick, S. Smith, G. Joshi, “Rateless Codes for Distributed Non-linear Computations,” in the Proceedings of the International Special Topics in Coding (ISTC) Conference, July 2021. [full paper]
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S. Kang, A. Eryilmaz, and N. B. Shroff, “Remote Tracking of Distributed Dynamic Sources over A Random Access Channel with One-bit Updates,” WiOpt 2021 (selected for fast track publication in IEEE Transactions on Network Science and Engineering).
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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.
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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.
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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.
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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.
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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.
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Saket Gurukar, Srinivasan Parthasarathy, Rajiv Ramnath, Catherine A. Calder, Sobhan Moosavi: “LocationTrails: a federated approach to learning location embeddings,” IEEE ASONAM 2021: 377-384.
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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.
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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. [full paper]
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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.
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Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor, “RL for Latent MDPs: Regret Guarantees and a Lower Bound,” in the Proceedings of Neural Information Processing Systems (NeurIPS), 2021. [full paper]
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S. Zhang and N. Jiang, “Towards Hyperparameter-free Policy Selection for Offline Reinforcement Learning,” NeurIPS 2021.
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T. Xie, C. Cheng, N. Jiang, P. Mineiro, and A. Agarwal, “Bellman-consistent Pessimism for Offline Reinforcement Learning,” NeurIPS 2021 (selected for oral presentation).
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T. Xie, N. Jiang, H. Wang, C. Xiong, and Y. Bai, “Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning,” NeurIPS 2021.
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X. Liu, W. Kong, S. Kakade, S. Oh, “Robust and differentially private mean estimation,” NeurIPS 2021.
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K. Thekumparampil, P. Jain, P. Netrapalli, S. Oh, “Statistically and Computationally Efficient Linear Meta-Representation Learning,” in Proceedings of Neural Information Processing Systems (NeurIPS), 2021.
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Q. Li, C. Peng, “Reconfiguring Cell Selection in 4G/5G Networks,” ICNP 2021. [full paper]
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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.
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Junjie Yang, Kaiyi Ji, Yingbin Liang. “Provably faster algorithms for bilevel optimization,” Proc. Advances in Neural Information Processing Systems (NeurIPS), Spotlight, 2021.
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Karim, SR Hussain, AA Ishtiaq, O Chowdhury, and E Bertino, “Noncompliance as Deviant Behavior: An Automated Black-box Noncompliance Checker for 4G LTE Cellular Devices,” In Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security (CCS ’21). Association for Computing Machinery, New York, NY, USA, 1082–1099. [full paper]
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Zifeng Wang, Tong Jian, Aria Masoomi, Stratis Ioannidis, and Jennifer Dy, “Revisiting Hilbert-Schmidt Information Bottleneck for Adversarial Robustness,” 35th Conference on Neural Information Processing Systems (NeurIPS 2021). [full paper]
- L. Bonati, M. Polese, S. D’Oro, S. Basagni, and T. Melodia, “OpenRAN Gym: AI/ML Development, Data Collection, and Testing for O-RAN on PAWR Platforms,” Computer Networks, vol. 220, pp. 1–11, January 2023. [full paper]
- V. Chen, J. Li, J. Kim, G. Plumb, A. Talwalkar. Interpretable Machine Learning: Moving from Mythos to Diagnostics, Communications of the ACM (CACM), 2022.
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D. Roy, Y. Li, T. Jian, P. Tian, K. R. Chowdhury, and S. Ioannidis, “Multi-modality Sensing and Data Fusion for Multi-vehicle Detection”. IEEE Transactions on Multimedia, 2022.
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Yae Jee Cho, Jianyu Wang, Tarun Chiruvolu, Gauri Joshi, “Communication-Efficient and Model-Heterogeneous Personalized Federated Learning via Clustered Knowledge Transfer”, IEEE Journal of Selected Topics in Signal Processing, Dec 2022. [full paper]
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M. Polese, L. Bonati, S. D’Oro, S. Basagni, and T. Melodia, “ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platforms,” IEEE Transactions on Mobile Computing, pp. 1–14, 2022. [full paper]
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J. Pan, A. M. Bedewy, Y. Sun, and N. B. Shroff, “Age-optimal Scheduling over Hybrid Channels,” IEEE Trans on Mobile Computing, 2022. [full paper]
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J. Pan, A. M. Bedewy, Y. Sun, and N. B. Shroff, “Optimal Sampling for Data Freshness: Unreliable Transmissions with Random Two-way Delay,” IEEE/ACM Trans on Networking, 2022. [full paper]
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G. Yao, M. Hashemi, R. Singh, and N. B. Shroff, “Delay-Optimal Scheduling for Integrated mmWave – Sub-6 GHz Systems with Markovian Blockage Model,” IEEE Trans on Mobile Computing, 2022.
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G. Yao, A. M. Bedewy, and N. B. Shroff, “Age-Optimal Low-Power Status Update over Time-Correlated Fading Channel,” IEEE Trans on Mobile Computing, 2022.
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R. Singh, A. Gupta, and N. B. Shroff, “Learning in Constrained Markov Decision Processes,” IEEE Trans on Control of Network Systems, 2022. [full paper]
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Boubin, Jayson, Avishek Banerjee, Jihoon Yun, Haiyang Qi, Yuting Fang, Steve Chang, Kannan Srinivasan, Rajiv Ramnath, and Anish Arora, "PROWESS: An Open Testbed for Programmable Wireless Edge Systems," In Practice and Experience in Advanced Research Computing, pp. 1-9. 2022. [full paper]
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B. Salehi, G. Reus-Muns, D. Roy, Z. Wang, T. Jian, J. Dy, S. Ioannidis, and K. Chowdhury, “Deep Learning on Multimodal Sensor Data at the Wireless Edge for Vehicular Network,” IEEE Transactions on Vehicular Technology, vol. 71, no. 7, pp. 7639-7655, July 2022.
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S. Garcia Sanchez, G. Reus-Muns, C. Bocanegra, Y. Li, U. Muncuk, Y. Naderi, Y. Wang, S. Ioannidis, and K. R. Chowdhury, “AirNN: Over-the-Air Computation for Neural Networks via Reconfigurable Intelligent Surfaces,” IEEE/ACM Transactions on Networking, Nov. 2022.
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V. Chen, J. Li, J. Kim, G. Plumb, A. Talwalkar, “Interpretable Machine Learning: Moving from Mythos to Diagnostics,” Communications of the ACM (CACM), 2022.
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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.
Link to repository |
Description |
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Public release of the code of our paper titled "Noncompliance as Deviant Behavior: An Automated Black-box Noncompliance Checker for 4G LTE Cellular Devices" (CCS'21) |
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Public release of software for BLEDiff, an automated, scalable, property-agnostic, and black-box protocol noncompliance checker for BLE devices. |
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SPEC5G is a dataset for the analysis of natural language specification of 5G Cellular network protocol specification. SPEC5G contains 3,547,587 sentences with 134M words, from 13094 cellular network specifications and 13 online websites. PEC5G is the first-ever public 5G dataset for NLP research on network security. The repository contains the code and data of the paper titled "SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis" |
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Release of software associated with the paper “GALAXY: Graph-based Active Learning at the Extreme”. The Active Learning algorithm saves significant annotation costs under extreme class imbalance settings. It is generally applicable for classification tasks. |
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Software for mmWave Joint Radar Communication Transceiver Testbed [details] |
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Description of the mmWave Joint Radar Communication Transceiver Testbed |
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Multimodal Fusion Dataset for NextG V2X Communications |