Publications

Representation Learning, Invariance and Generative Models

1. "Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks", L. Collins, H. Hassani, M. Soltanolkotabi, A. Mokhtari, S. Shakkottai. To appear in the Proceedings of Forty-first International Conference on Machine Learning (ICML), July 2024. (oral presentation)

2. "Beyond First-Order Tweedie: Solving Inverse Problems using Latent Diffusion", L. Rout, Y. Chen, A. Kumar, C. Caramanis, S. Shakkottai, W. Chu. To appear in the IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), Seattle, WA, June 2024. Project page.

3. "Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models",  L. Rout, N. Raoof, G. Daras, C. Caramanis, A. Dimakis and S. Shakkottai.  Proceedings of the Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS) , New Orleans, LA, December 2023.

4. "InfoNCE Loss Provably Learns Cluster-Preserving Representations", A. Parulekar, L. Collins, K. Shanmugam, A. Mokhtari and S. Shakkottai. Proceedings of the 36th Annual Conference on Learning Theory (COLT), Bangalore, India, July 2023.

5. "PAC Generalization via Invariant Representations", A. Parulekar, K. Shanmugam and S. Shakkottai. Proceedings of the 40th International Conference on Machine Learning (ICML), Honolulu, HI, July 2023.

6. “FedAvg with Fine Tuning: Local Updates Lead to Representation Learning”, L. Collins, H. Hassani, A. Mokhtari, S. Shakkottai. Proceedings of  Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS), New Orleans, December 2022.

7. “MAML and ANIL Provably Learn Representations”, L. Collins, A. Mokhtari, S. Oh and S. Shakkottai. Proceedings of the 39th International Conference on Machine Learning (ICML), Baltimore, MD, July 2022.

8. “Exploiting Shared Representations for Personalized Federated Learning”, L. Collins, H. Hassani, A. Mokhtari and S. Shakkottai. Proceedings of the Thirty-eighth International Conference on Machine Learning (ICML), Virtual Conference, July 2021.

9. "Applications of Common Entropy in Causal Inference", M. Kocaoglu, S. Shakkottai, A. Dimakis, C. Caramanis and S. Vishwanath. Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 2020.

10. "Task-Robust Model-Agnostic Meta-Learning", L. Collins, A. Mokhtari and S. Shakkottai, Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 2020.

11. “Model-Powered Conditional Independence Test”, R. Sen, A. T. Suresh, K. Shanmugam, A. Dimakis and S. Shakkottai, Proceedings of the 31st Annual Conference on Neural Information Processing Systems (NeurIPS), Long Beach,  CA December 2017.

Bandits, Online Learning, Adaptation and RL

1. “Finite-Sample Analysis of Contractive Stochastic Approximation Using Smooth Convex Envelopes”, Z. Chen, S. Maguluri, S. Shakkottai and K. Shanmugam. Accepted for publication to Operations Research (INFORMS), 2023.

2. "Beyond Uniform Smoothness: A Stopped Analysis of Adaptive SGD", M. Faw, L. Rout, C. Caramanis and S. Shakkottai. Proceedings of the 36th Annual Conference on Learning Theory (COLT), Bangalore, India, July 2023.

3. "Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits", R. Chawla, D. Vial, S. Shakkottai and R. Srikant. Proceedings of the 40th International Conference on Machine Learning (ICML), Honolulu, HI July 2023.

4. “Robust Multi-Agent Bandits Over Undirected Graphs”, D. Vial, S. Shakkottai and R. Srikant. To appear in the Proceedings of the ACM Sigmetrics Conference on Measurement and Modeling of Computer Systems (Sigmetrics), Orlando, FL, June 2023.

5. “Multi-Agent Low-Dimensional Linear Bandits”, R. Chawla, A. Sankararaman and S. Shakkottai. IEEE Transactions on Automatic Control, May 2023.

6. “Minimax Regret for Cascading Bandits”, D. Vial, S. Sanghavi, S. Shakkottai and R. Srikant. Proceedings of the Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS), New Orleans, December 2022.  (oral presentation)

7. “Non-Stationary Bandits under Recharging Payoffs: Improved Planning with Sublinear Regret”, O. Papadigenopolous, C. Caramanis and S. Shakkottai, . Proceedings of the Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS), New Orleans, December 2022.

8. “Regret Bounds for Stochastic Shortest Path Problems with Linear Function Approximation”, D. Vial, A. Parulekar, S. Shakkottai and R. Srikant. Proceedings of the 39th International Conference on Machine Learning (ICML), Baltimore, MD, July 2022. 

9. “Asymptotically-Optimal Gaussian Bandits with Side Observations”, A. Atsidakou, O. Papadigenopoulos, C. Caramanis, S. Sanghavi and S. Shakkottai. Proceedings of the 39th International Conference on Machine Learning (ICML), Baltimore, MD, July 2022.

10. “Linear Bandit Algorithms with Sublinear Time Complexity”, S. Yang, T. Ren, S. Shakkottai, E. Price, I. Dhillon and S. Sanghavi. Proceedings of the 39th International Conference on Machine Learning (ICML), Baltimore, MD, July 2022.

11. “The Power of Adaptivity in SGD: Self-Tuning Step Sizes with Unbounded Gradients and Affine Variance”, M. Faw, I. Tziotis, A. Mokhtari, C. Caramanis, S. Shakkottai and R. Ward. Proceedings of the 35th Annual Conference on Learning Theory (COLT), London, UK, July 2022.

12. “Learning to Maximize Welfare with a Reusable Resource”, M. Faw, O. Papadigenopoulos, C. Caramanis and S. Shakkottai. Proceedings of ACM Sigmetrics, Boston, MA, June 2022. 

13. “Improved Algorithms for Misspecified Linear Markov Decision Processes”, D. Vial, A. Parulekar, S. Shakkottai and R. Srikant. To appear in Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS), Virtual Conference, April 2022.

14. “Finite-Sample Analysis of Off-Policy TD-Learning via Generalized Bellman Operators”, Z. Chen, S. Maguluri, S. Shakkottai and K. Shanmugam. Proceedings of the Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS), Virtual Conference, December 2021. 

15. “Combinatorial Blocking Bandits with Stochastic Delays”, A. Atsidakou, O. Papadigenopoulos, S. Basu. C. Caramanis and S. Shakkottai. Proceedings of Proceedings of the Thirty-eighth International Conference on Machine Learning (ICML), Virtual Conference, July 2021.

16. “Robust Multi-Agent Multi-Armed Bandits”, D. Vial, S. Shakkottai and R. Srikant. Proceedings of the Twenty-Second ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc), Shanghai, China, July 2021. (best paper runner up)

17. “Job Dispatching Policies for Queueing Systems with Unknown Service Rates”, T. Choudhury, G. Joshi, W. Wang and S. Shakkottai, Proceedings of the Twenty-Second ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc), Shanghai, China, July 2021.

18. "Contextual Blocking Bandits", S. Basu, O. Papadigenopoulos, C. Caramanis and S. Shakkottai. The 24th International Conference on Artificial Intelligence and Statistics (AISTATS), April 2021.

19. "Finite-Sample Analysis of Stochastic Approximation Using Smooth Convex Envelopes," Z. Chen, S. Maguluri, S. Shakkottai and K. Shanmugam. Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 2020.

20. “Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions”, M. Faw, R. Sen, K. Shanmugam, C. Caramanis, S. Shakkottai, Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 2020.

21. “The Gossiping Insert-Eliminate Algorithm for Multi-Agent Bandits”, R. Chawla, A. Sankararaman, A. J. Ganesh and S. Shakkottai. The 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), Palermo, Sicily, June 2020.

22. “Social Learning in Multi Agent Multi Armed Bandits”, A. Sankararaman, A. J. Ganesh and S. Shakkottai. Proceedings of ACM Sigmetrics,  Boston, MA, June 2020.

23. "Warm Starting Bandits with Side Information from Confounded Data", N. Sharma, S. Basu, K. Shanmugam and S. Shakkottai, arXiv 2002.08405, 2020.

24. “Blocking Bandits”, S. Basu, R. Sen, S. Sanghavi and S. Shakkottai. Proceedings of the Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada, December 2019. 

25. “Noisy Blackbox Optimization using Multi-fidelity Queries: A Tree Search Approach”, R. Sen, K. Kandasamy and S. Shakkottai. Proceedings of the 21st Annual Conference on Artificial Intelligence and Statistics (AISTATS 2019), Okinawa, Japan, April 2019. Code available here: MF Tree Search Code

26. “Multi-Fidelity Black-Box Optimization with Hierarchical Partitions”, R. Sen, K. Kandasamy and S. Shakkottai, Proceedings of the 35th International Conference on Machine Learning (ICML 2018), Stockholm, Sweden, July 2018.

27. “On Learning the c mu Rule: Single and Multiserver Settings”, S. Krishnasamy, A. Arapostathis, R. Johari and S. Shakkottai, UT Ausitn Technical Report, February 2018.

28. “Contextual Bandits with Stochastic Experts”, R. Sen, K. Shanmugam and S. Shakkottai,     Proceedings of the 21st Annual Conference on Artificial Intelligence and Statistics (AISTATS 2018), Canary Islands, April 2018.

29. “Identifying Best Interventions through Online Importance Sampling”, R. Sen, K. Shanmugam, A. Dimakis and S. Shakkottai, Proceedings of the 34th International Conference on Machine Learning (ICML 2017), Sydney, Australia, August 2017.

30. “Contextual Bandits with Latent Confounders: An NMF Approach”, R. Sen, K. Shanmugam, M. Kocaoglu, A. Dimakis and S. Shakkottai. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS 2017), Ft. Lauderdale, FL, April 2017.

31. “Regret of Queueing Bandits”, S. Krishnasamy, R. Sen, R. Johari and S. Shakkottai. Proceedings of the Thirtieth Annual Conference on Neural Information Processing Systems (NeurIPS), Barcelona, Spain, December 2016.

Wireless and Ad Hoc Networks

1. “Meta-Scheduling for the Wireless Downlink through Learning with Bandit Feedback”, J. Song, G. de Veciana and S. Shakkottai,. IEEE/ACM Transactions on Networking, Volume 30, April 2022.

2. “Online Learning for Multi-Agent Based Resource Allocation in Weakly Coupled Wireless Systems”, J. Song, G. de Veciana and S. Shakkottai. Proceedings of the Twenty-Third ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc), Seoul, South Korea, October 2022.

3. “Online Learning for Hierarchical Scheduling to Support Network Slicing in Cellular Networks”, J. Song, G. de Veciana and S. Shakkottai. Proceedings of the 39th IFIP WG 7.3 International Symposium on Computer Performance, Modeling, Measurements and Evaluation (Performance 2021), Milan, Italy, November 2021. Longer version in Performance Evaluation, December 2021.

4. “MmWave Codebook Selection in Rapidly-Varying Channels via Multinomial Thompson Sampling”, Y. Zhang, S. Basu, S. Shakkottai and R. W. Heath Jr.. Proceedings of the Twenty-Second ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc), Shanghai, China, July 2021. (best paper runner up)

5. "Auto-tuning for Cellular Scheduling through Bandit-Learning and Low-Dimensional Clustering", I. Tariq, R. Sen, T. Novlan, S. Akoum, M. Majmundar,  G. de Veciana and S. Shakkottai, To appear in IEEE/ACM Transactions on Networking, 2021 (longer version of paper that appeared in IEEE Infocom 2019).

6. “Joint Scheduling of URLLC and eMBB Traffic in 5G Wireless Networks”, Arjun Anand, Gustavo de Veciana and Sanjay Shakkottai, Proceedings of IEEE Infocom, Honolulu, HI, April 2018. Longer version in IEEE/ACM Transactions on Networking 2020.  (received the IEEE ComSoc William R. Bennett Prize, 2021)

7. “Side-information-aided Non-coherent Beam Alignment Design for Millimeter Wave Systems”, Y. Zhang, K. Patel, S. Shakkottai and R. W. Heath Jr. Proceedings of the Twentieth International Symposium on Mobile Ad Hoc Networking and Computing (ACM MobiHoc 2019), Catania, Italy, July 2019.

8. “Switching Constrained Max-Weight Scheduling for Wireless Networks”, S. Basu and S. Shakkottai. Proceedings of IEEE Infocom, Paris, France, April 2019.

9. “Augmenting Max-Weight with Explicit Learning for Wireless Scheduling with Switching Costs”, S. Krishnasamy, P. T. Akhil, A. Arapostathis, S. Shakkottai and R. Sundaresan. Proceedings of IEEE Infocom, Atlanta, GA, April 2017. Longer version in IEEE/ACM Transactions on Networking, December 2018.

10. “Scheduling in Densified Networks: Algorithms and Performance,” S. Moharir, S. Krishnasamy and S. Shakkottai. IEEE/ACM Transactions on Networking, Volume 25, Number 1, pp 164 – 178, February 2017. (Earlier version appeared in the Proc. of the 52nd Allerton Conference on Communication, Control, and Computing, Urbana, IL, October 2014).

11. “Wireless Scheduling with Partial Channel State Information: Large Deviations and Optimality”, A. Gopalan, C. Caramanis, and S. Shakkottai. Queueing Systems, Volume 80, Issue 4, pp. 293 – 340, August 2015.

12. “Spectrum Sharing and Scheduling in D2D-Enabled Dense Cellular Networks”, S. Krishnasamy and S. Shakkottai. Proceedings of 13th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt), Mumbai, India, May 2015.

13. “MaxWeight vs. BackPressure: Routing and Scheduling in Multi-Channel Relay Networks,” S. Moharir and S. Shakkottai. IEEE/ACM Transaction on Networking, Volume 23, Number 5, pp. 1584 – 1598, October 2015.

14. “Scheduling in Multi-Channel Wireless Networks: Rate Function Optimality in the Small-Buffer Regime”. S. Bodas, S. Shakkottai, L. Ying and R. Srikant. IEEE Transactions on Information Theory, Volume 60, Issue 2, pp. 1101 – 1125, February 2014.

15. “FlashLinQ: A Synchronous Distributed Scheduler for Peer-to-Peer Ad-hoc Networks,” X. Wu, S. Tavildar, S. Shakkottai, T. Richardson, J. Li, R. Laroia, A. Jovicic. IEEE/ACM Transaction on Networking, August 2013.

16. “On the Role of Mobility for Multi-message Gossip,” Y. Chen, S. Shakkottai and J. Andrews. IEEE Transactions on Information Theory, June 2013.

17. “On the Value of Coordination and Delayed Queue Information in Multicellular Scheduling,” A. Gopalan, C. Caramanis and S. Shakkottai. IEEE Transactions on Automatic Control, June 2013.

18. .“On Optimizing CSMA for Wide Areas Ad-Hoc Networks”. F. Baccelli, J. Li, T. Richardson, S. Shakkottai, S. Subramanian and X. Wu. Queueing Systems, Special Issue on Recent Trends in the Mathematics of Wireless Communication Networks: Algorithms, Models and Methods – Part 1, Volume 72, Issue 1-2, pages 31 – 68, October 2012.

19. “On Wireless Scheduling with Heterogeneously Delayed Network-State Information,” A. Reddy, S. Banerjee, A. Gopalan, S. Shakkottai and L. Ying. Queueing Systems, December 2012.

20. “On the Effect of Channel Fading on Greedy Scheduling,” A. Reddy, S. Sanghavi and S. Shakkottai, Proceedings of IEEE Infocom, Orlando, FL, 2012.

21. “Low-delay Wireless Scheduling with Partial Channel-State Information,” A. Gopalan, C. Caramanis and S. Shakkottai, Proceedings of IEEE Infocom, Orlando, FL, 2012.

22. “Scheduling for Small Delay in Multi-rate Multi-channel Wireless Networks,” S. Bodas, S. Shakkottai, L. Ying and R. Srikant, Proceedings of IEEE Infocom, Shanghai, China, 2011.

23. “On Wireless Scheduling with Partial Channel-State Information,” A. Gopalan, C. Caramanis and S. Shakkottai. Accepted for publication in IEEE Transactions on Information Theory, 2011.

24. “Back-Pressure Routing and Rate Control for ICNs,” J. Ryu, V. Bhargava, N. Paine, and S. Shakkottai. Proceedings of ACM Mobicom, Chicago, IL, 2010.

25. “Low-complexity Scheduling Algorithms for Multi-channel Downlink Wireless Networks,”  S. Bodas, S. Shakkottai , L. Ying and R. Srikant. Proceedings of IEEE Infocom, San Diego, CA, 2010.

26. “On Scheduling for Minimizing End-to-End Buffer Usage over Multihop Wireless Networks,”  V. J. Venkataraman, X. Lin, L. Ying and S. Shakkottai. Proceedings of IEEE Infocom, San Diego, CA, 2010.

27. “Back-pressure Routing for Intermittently Connected Networks,”  J. Ryu, L. Ying and S. Shakkottai. Proceedings of IEEE Infocom mini-conference, San Diego, CA, 2010.

28. “TCP with Feed-Forward Source Coding for Wireless Downlink Networks,”   J. Ryu, S. Bhadra and S. Shakkottai. Proceedings of the 47th Allerton Conference, Monticello, IL 2009.

29. “On Combining Shortest-Path and Back-Pressure Routing Over Multihop Wireless Networks,”  L. Ying, S. Shakkottai and A. Reddy. Proceedings of IEEE Infocom, Rio de Janeiro, Brazil, 2009.

30. “Scheduling in Mobile Wireless Networks with Topology and Channel-State Uncertainty,” L. Ying, and S. Shakkottai. Proceedings of IEEE Infocom, Rio de Janeiro, Brazil, 2009.

31. “Routing over multi-hop wireless networks with non-ergodic mobility,” C. Milling, S. Subramanian, S. Shakkottai and R. Berry. Proceedings of IEEE Infocom, Rio de Janeiro, Brazil, 2009.

32. “Rethinking information theory for mobile ad hoc networks,” J. Andrews, N. Jindal, M. Haenggi, R. A. Berry, S. Jafar, D. Guo, S. Shakkottai, R. Heath Jr, M. Neely, S. Weber, A. Yener and P. Stone. IEEE Communications Magazine, Dec. 2008.

33. “Distributed Power Control in Wireless Ad Hoc Networks Using Message Passing: Throughput Optimality and Network Utility Maximization,” A. Reddy, S. Shakkottai and L. Ying. In Proc. CISS 2008, Princeton. 

34. “On Throughput-Optimal Scheduling with Delayed Channel State Feedback,” Lei Ying and S. Shakkottai. IEEE Transactions on Information Theory, August 2011. (longer version of paper)

35. “On Optimal MAC Scheduling With Physical Interference,” Y. Yi, G. de Veciana and S. Shakkottai. To appear, Proceedings of IEEE Infocom, Anchorage, AK, 2007. A related paper with a graph-based interference model is available as a technical report here.

36. “Hop-by-hop Congestion Control over a Wireless Multi-hop Network,” Y. Yi and S. Shakkottai. IEEE/ACM Transactions on Networking, Vol. 15, pp. 133 – 144, February 2007.

37. “Optimal Power Allocation for a Time-varying Wireless Channel under Heavy Traffic Approximation,” W. Wu, A. Arapostathis and S. Shakkottai, IEEE Transactions on Automatic Control, Vol. 51, No. 4, April 2006.

38. 47.“Pathwise Optimality of the Exponential Scheduling Rule for Wireless Channels,” S. Shakkottai, R. Srikant and A. Stolyar. Advances in Applied Probability, December 2004.

39. “Cross-layer Design for Wireless Networks,” S. Shakkottai, T. S. Rappaport and P. C. Karlsson, IEEE Communications magazine, October, 2003.

40. “Scheduling for Multiple flows Sharing a Time-Varying Channel: The Exponential Rule,” S. Shakkottai & A. Stolyar,  American Mathematical Society Translations, Series 2, A volume in memory of F. Karpelevich, Yu. M. Suhov, Editor, Vol. 207, 2002.

41. “Scheduling Algorithms for a Mixture of Real-Time and Non-Real-Time Data in HDR,” S. Shakkottai  & A. Stolyar,  Proc. of the 17th International Teletraffic Congress (ITC-17), Salvador  da Bahia, Brazil, September 2001.

42. “Scheduling Real-time Traffic With Deadlines Over a Wireless Channel,” S. Shakkottai  & R. Srikant, ACM/Baltzer Wireless Networks Journal, Vol. 8, No. 1, pp. 13--26, January 2002.

43. “Effective Capacity and QoS for Wireless Scheduling,” S. Shakkottai, To appear, IEEE Transactions on Automatic Control, February 2008.

44. “Communication Through Jamming Over a Slotted ALOHA Channel,” S. Bhadra, S. Shakkottai and S. Vishwanath. A shorter version appeared in the Proceedings of the 42nd Allerton Conference on Communication, Control, and Computing, Urbana, IL, October 2004.

Epidemics, Graphical Models, and Statistical Inference

1. “Pareto Optimal Streaming Unsupervised Classification”, S. Basu, S. Gutstein, B. Lance and S. Shakkottai. Proceedings of the Thirty-sixth International Conference on Machine Learning (ICML 2019), Long Beach, CA, June 2019. 

2. “The Search Problem in Mixture Models”, A. Ray, J. Neeman, S. Sanghavi and S. Shakkottai. Journal of Machine Learning Research (JMLR), 2018.

3. “Searching for A Single Community in a Graph”, A. Ray, S. Sanghavi and S. Shakkottai. Proceedings of ACM Sigmetrics (poster paper), Antibes Juan-Les-Pins, France, June 2016.

4. “Online Collaborative-Filtering on Graphs”, S. Banerjee, S. Sanghavi and S. Shakkottai, Operations Research (INFORMS), Special Issue on Information and Decisions in Social and Economic Networks, Volume 64, Number 3, pp 756 – 769, May 2016.

5. “Detecting Sponsored Recommendations”, S. Krishnasamy, R. Sen, S. Shakkottai and S. Oh, “Detecting Sponsored Recommendations”. ACM Transactions on Modeling and Performance Evaluation of Computing Systems (ACM ToMPECS), Volume 2, Issue 1, November 2016 (shorter version appeared in ACM Sigmetrics 2015 as a poster paper). Arxiv version available here.

6. “Localized epidemic detection in networks with overwhelming noise”, E. Meirom, C. Caramanis, S. Mannor, S. Shakkottai, A. Orda. Proceedings of ACM Sigmetrics (poster paper), Portland, OR, June 2015. Longer version available here.

7. “Improved Greedy Algorithms for Learning Graphical Models”, A. Ray, S. Sanghavi and S. Shakkottai. IEEE Transactions on Information Theory, 2015.

8. “Distinguishing Infections on Different Graph Topologies”, C. Milling, C. Caramanis, S. Mannor and S. Shakkottai. IEEE Transactions on Information Theory, Vol. 61, No. 6, June 2015.

9. “Local Detection of Infections in Heterogeneous Networks”, C. Milling, C. Caramanis, S. Mannor and S. Shakkottai, To appear in Proc. of IEEE Infocom, Hong Kong, 2015.

10. “Overlap Graph Clustering via Successive Removal”, A. Ray, J. Ghaderi, S. Sanghavi, S. Shakkottai. Proceedings of the 52nd Allerton Conference on Communication, Control, and Computing, Urbana, IL, October 2014. Technical report available here.

11. “Epidemic Thresholds with External Agents,” S. Banerjee, A. Chatterjee and S. Shakkottai. Proceedings of IEEE Infocom, Toronto, Canada, April 2014.

12. “Epidemic Spreading with External Agents,” A. Gopalan, S. Banerjee, A. Das and S. Shakkottai, IEEE Transactions on Information Theory, July 2014.

13. “The Behavior of Epidemics under Bounded Susceptibility,” S. Krishnasamy, S. Banerjee and S. Shakkottai. Proceedings of ACM Sigmetrics, Austin, TX June 2014.

14. “Topic Modeling from Network Spread,” A. Ray, S. Sanghavi and S. Shakkottai. Proceedings of ACM Sigmetrics (poster paper), Austin, TX June 2014.

15. “Detecting Epidemics using Highly Noisy Data,” C. Milling, C. Caramanis, S. Mannor and S. Shakkottai, Proc. of ACM MobiHoc 2013.

16. “Network Forensics: Random Infection vs. Spreading Epidemic,” C. Milling, C. Caramanis, S. Mannor and S. Shakkottai, Proc. of ACM Sigmetrics 2012. Longer version available here.

17. “On File Sharing over a Wireless Social Network,” Y-T. Chen, C. Caramanis and S. Shakkottai, Proceedings of the IEEE Symp. on Information Theory (ISIT), St. Petersburg, Russia, August 2011.

18. “Greedy Learning of Markov Network Structure” P. Netrapalli, S. Banerjee, S. Sanghavi and S. Shakkottai, Proceedings of the 48th Annual Allerton Conference, Monticello, IL, 2010.

Cloud Resource Allocation

1. “Darwin: Flexible Learning-based CDN Caching”, J. Chen, N. Sharma, T. Khan, S. Liu, B. Chang, A. Akella, S. Shakkottai, R. Sitaraman. To appear in the Proceedings of ACM Sigcomm, New York, NY, September 2023.

2. “Adaptive TTL-Based Caching for Content Delivery”, S. Basu, A. Sundarrajan, J. Ghaderi, S. Shakkottai and R. Sitaraman. Shorter version in the Proceedings of ACM Sigmetrics 2017 (poster paper, June 2017). Longer version in IEEE/ACM Transactions on Networking, 2018.

3. “Job Dispatching Policies for Queueing Systems with Unknown Service Rates”, T. Choudhury, G. Joshi, W. Wang and S. Shakkottai, To appear in the Proceedings of the Twenty-Second ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc), Shanghai, China, July 2021.

4. “Adaptive TTL-Based Caching for Content Delivery”, S. Basu, A. Sundarrajan, J. Ghaderi, S. Shakkottai and R. Sitaraman. Shorter version in the Proceedings of ACM Sigmetrics 2017 as a poster paper.

5. “Scheduling Storms and Streams in the Cloud”, J. Ghaderi, S. Shakkottai and R. Srikant. ACM Transactions on Modeling and Performance Evaluation of Computing Systems (ACM ToMPECS), Volume 1, Issue 4, September 2016 (shorter version appeared in ACM Sigmetrics 2015 as a poster paper). Arxiv version available here.

6. “Online Load Balancing Under Graph Constraints”, S. Moharir, S. Sanghavi and S. Shakkottai. IEEE/ACM Transaction on Networking, Volume 24, Number 3, pp. 1690 – 1703, June 2016. Earlier version appeared in Proc. of ACM Sigmetrics (poster paper), Pittsburgh, PA June 2013.

7. “Serving Content with Unknown Demand: the High-Dimensional Regime”, S. Moharir, J. Ghaderi, S. Sanghavi and S. Shakkottai. Proceedings of ACM Sigmetrics, Austin, TX June 2014. Longer version in Queueing Systems, 2015.

Sensor Networks

1. “Towards a Queueing-Based Framework for In-Network Function Computation,” S. Banerjee, P. Gupta and S. Shakkottai, Queueing Systems, December 2012.

2. “Optimal Geographic Routing for Wireless Networks with Near-arbitrary Holes and Traffic,” S. Subramanian, S. Shakkottai and P. Gupta. Proceedings of IEEE Infocom, Phoenix, AR, 2008.

3. “Oblivious Routing with Mobile Fusion Centers over a Sensor Network,” D. Shah and S. Shakkottai. Proceedings of IEEE Infocom, Anchorage, AK, 2007.

4. “On Optimal Geographic Routing in Networks with Holes and Non-Uniform Traffic,” S. Subramanian, S. Shakkottai and P. Gupta. Proceedings of IEEE Infocom, Anchorage, AK, 2007.

5. “Asymptotics of Query Strategies over a Sensor Network,” S. Shakkottai. IEEE Transactions on Automatic Control, June, 2005.

6. “Unreliable Sensor Grids: Coverage, Connectivity and Diameter,” (Errata) S. Shakkottai, R. Srikant and N. Shroff. Ad Hoc Networks,  Volume 3, Issue 6, pp. 702 – 716, November 2005.

7. “Geographic Routing with Limited Information in Sensor Networks,” S. Subramanian and S. Shakkottai. To appear in IEEE Transactions on Information Theory. (A shorter version appeared in Proc. IPSN 2005).

8. ``The Role of Information on Broadcasting in Sensor Networks,'' S. Subramanian, S. Shakkottai and A. Arapostathis. IEEE/ACM Transactions on Networking, October 2008.

Network Coding

1. “Min-Cost Selfish Multicast with Network Coding,” S. Bhadra, S. Shakkottai and P. Gupta. IEEE Transactions on Information Theory, Vol. 52, pp. 5077 – 5087, November, 2006.

2. “Looking at Large Networks: Coding vs. Queueing,” (longer version), S. Bhadra and S. Shakkottai. Proceedings of IEEE Infocom, Barcelona, 2006. Longer version to appear in IEEE Transactions on Information Theory.

3. “On Network Coding for Interference Networks,” S. Bhadra, P. Gupta and S. Shakkottai. Proceedings of IEEE Symposium on Information Theory (ISIT), Seattle, WA, July, 2006.

Internet Congestion Control

1. “Time-scale Decomposition and Equivalent Rate Based Marking,” Y. Yi, S. Deb and S. Shakkottai. IEEE/ACM Transactions on Networking, Vol. 14, pp. 938 – 950, October 2006.

2. “Asymptotic Behavior of Internet Congestion Controllers in a Many-flows Regime,” S. Deb, S. Shakkottai and R. Srikant, Mathematics of Operations Research, Volume 30, Number 2, pp. 420 – 440, May 2005.

3. “Mean FDE Models for Internet Congestion Control under a Many-Flows  Regime,”  S. Shakkottai  and R. Srikant, IEEE Transactions on Information Theory, Vol. 50, No. 6, pp. 1050 – 1072, June, 2004.

4. “Bounds on the Throughput of Congestion Controllers in the Presence of Feedback Delay,” S. Shakkottai, R. Srikant  & Sean Meyn, IEEE/ACM Transactions on Networking, December, 2003.

5. “TCP Performance over End-to-End Rate Control and Stochastic Available Capacity,” S. Shakkottai,  A. Kumar, A. Karnik  & A. Anvekar, IEEE/ACM Transactions on  Networking,  Vol. 9, No. 4, pp. 377--391, August, 2001.

6. “On the Elasticity of Marking Functions in an Integrated Network,” Y. Yi and S. Shakkottai. IEEE Trans.actions on Automatic Control, Vol. 54, Issue 2, February 2009.

7. “FluNet: A Hybrid Internet Simulation/Emulation Environment at a Fast Queue Regime,” Y. Yi and S. Shakkottai. Elsevier Computer Networks, 2007.

Queueing Networks and Scheduling

1. “Many-sources Delay Asymptotics with Applications to Priority Queues,” S. Shakkottai and R. Srikant, Queueing Systems Theory and Applications (QUESTA), , Vol. 39, pp. 183--200, October, 2001.

2. “Asymptotic Evaluation of Delay with the SRPT Scheduler,” C. W. Yang and S. Shakkottai. IEEE Transactions on Automatic Control, November, 2006.

3. “Tail asymptotics for policies favoring short jobs in a many-flows regime,” Chang Woo Yang, Adam Wierman, Sanjay Shakkottai and Mor Harchol-Balter. Proceedings of ACM Sigmetrics, Saint Malo, France, June, 2006.

4. “Fast Matching Algorithms for Repetitive Optimization: An Application to Switch Scheduling,” S. Deb, D. Shah and S. Shakkottai. Proceedings of the 40th Annual Conference on Information Sciences and Systems (CISS), Princeton, NJ, March, 2006.