publications

publications of Anand D. Sarwate

Preprints

NB: ArXiV versions of published conference papers are linked from the conference paper.

    1. Z. Wang, A. Engel, A. Sarwate, I. Dumitriu, and T. Chiang, “Spectral evolution and invariance in linear-width neural networks,” ArXiV, arXiv:2211.06506 [cs.LG], Nov. 2022.
    2. A. Engle, Z. Wang, A. D. Sarwate, S. Choudhury, and T. Chiang, “TorchNTK: A Library for Calculation of Neural Tangent Kernels of PyTorch Models,” ArXiV, arXiv:2205.12372 [cs.LG], May 2022.
    1. K. E. Nikolakakis, D. S. Kalogerias, and A. D. Sarwate, “Optimal Rates for Learning Hidden Tree Structures,” ArXiV, arXiv:1909.09596v4 [stat.ML], Mar. 2021.
                    1. A. D. Sarwate and M. Gastpar, “Relaxing the Gaussian AVC,” ArXiV, arXiv:1204.2587v1 [cs.IT], Sep. 2012.

                                  Journal Papers

                                  1. N. Tasnim, J. Mohammadi, A. D. Sarwate, and H. Imtiaz, “Approximating Functions with Approximate Privacy for Applications in Signal Estimation and Learning,” Entropy, vol. 25, no. 5, p. 825, May 2023.
                                  2. R. Islam, K. N. Keya, S. Pan, A. D. Sarwate, and J. R. Foulds, “Differential Fairness: An Intersectional Framework for Fair AI,” Entropy, vol. 25, no. 4, Apr. 2023.
                                  3. H. Gazula, K. Rootes-Murdy, B. Holla, S. Basodi, Z. Zhang, E. Verner, R. Kelly, P. Murthy, A. Chakrabarti, D. Basu, S. Bhagyalakshmi Nanjayya, R. Lenin Singh, R. Lourembam Singh, K. Kalyanram, K. Kartik, K. Kalyanaraman, K. Ghattu, R. Kuriyan, S. S. Kurpad, G. J. Barker, R. D. Bharath, S. Desrivieres, M. Purushottam, D. P. Orfanos, E. Sharma, M. Hickman, M. Toledano, N. Vaidya, T. Banaschewski, A. L. W. Bokde, H. Flor, A. Grigis, H. Garavan, P. Gowland, A. Heinz, R. Brühl, J.-L. Martinot, M.-L. Paillére Martinot, E. Artiges, F. Nees, T. Paus, L. Poustka, J. H. Fröhner, L. Robinson, M. N. Smolka, H. Walter, J. Winterer, R. Whelan, J. A. Turner, A. D. Sarwate, et al., “Federated Analysis in COINSTAC Reveals Functional Network Connectivity and Spectral Links to Smoking and Alcohol Consumption in Nearly 2,000 Adolescent Brains,” Neuroinformatics, vol. 21, pp. 287–301, 2022.
                                  1. S. Basodi, R. Raja, B. Ray, H. Gazula, A. D. Sarwate, S. Plis, J. Liu, E. Verner, and V. D. Calhoun, “Decentralized Brain Age Estimation Using MRI Data,” Neuroinformatics, vol. 20, no. 4, pp. 981–990, 2022.
                                  2. H. Gazula, K. Rootes-Murdy, B. Holla, S. Basodi, Z. Zhang, E. Verner, R. Kelly, P. Murthy, A. Chakrabarti, D. Basu, and others, “Federated analysis in COINSTAC reveals functional network connectivity and spectral links to smoking and alcohol consumption in nearly 2,000 adolescent brains,” Neuroinformatics, pp. 1–15, 2022.
                                  3. S. Basodi, R. Raja, B. Ray, H. Gazula, A. D. Sarwate, S. Plis, J. Liu, E. Verner, and V. D. Calhoun, “Decentralized Brain Age Estimation Using MRI Data,” Neuroinformatics, Apr. 2022.
                                  4. D. K. Saha, V. D. Calhoun, Y. Du, Z. Fu, S. M. Kwon, A. D. Sarwate, S. R. Panta, and S. M. Plis, “Privacy-preserving quality control of neuroimaging datasets in federated environments,” Human Brain Mapping, vol. 43, p. 2289—2310, Mar. 2022.
                                  5. K. Rootes-Murdy, H. Gazula, E. Verner, R. Kelly, T. DeRamus, S. Plis, A. Sarwate, J. Turner, and V. Calhoun, “Federated Analysis of Neuroimaging Data: A Review of the Field,” Neuroinformatics, vol. 20, no. 2, pp. 377–390, Apr. 2022.
                                  6. Y. Zhang, S. Vatedka, S. Jaggi, and A. D. Sarwate, “Quadratically Constrained Myopic Adversarial Channels,” IEEE Transactions on Information Theory, vol. 68, pp. 4901–4948, Aug. 2022.
                                  7. S. Xiong, A. D. Sarwate, and N. B. Mandayam, “Network Traffic Shaping for Enhancing Privacy in IoT Systems,” IEEE/ACM Transactions on Networking, vol. 30, no. 3, pp. 1162–1177, Jun. 2022.
                                  1. S. M. Kwon and A. D. Sarwate, “Learning Predictors from Multidimensional Data with Tensor Factorizations,” Aresty Rutgers Undergraduate Research Journal, vol. 1, no. 3, Oct. 2021.
                                  2. H. Imtiaz, J. Mohammadi, R. Silva, B. Baker, S. M. Plis, A. D. Sarwate, and V. D. Calhoun, “A Correlated Noise-Assisted Decentralized Differentially Private Estimation Protocol, and its Application to fMRI Source Separation,” IEEE Transactions on Signal Processing, vol. 69, pp. 6355–6370, Nov. 2021.
                                  3. G. R. Kurri, V. M. Prabhakaran, and A. D. Sarwate, “Coordination Through Shared Randomness,” IEEE Transactions on Information Theory, vol. 67, no. 8, pp. 4948–4974, Aug. 2021.
                                  4. K. E. Nikolakakis, D. S. Kalogerias, A. D. Sarwate, and O. Sheffet, “Quantile Multi-Armed Bandits: Optimal Best-Arm Identification and a Differentially Private Scheme,” IEEE Journal on Selected Areas in Information Theory, vol. 2, no. 2, pp. 534–548, Jun. 2021.
                                  5. H. Gazula, B. Holla, Z. Zhang, J. Xu, E. Verner, R. Kelly, S. Jain, R. D. Bharath, G. J. Barker, D. Basu, A. Chakrabarti, K. Kalyanram, K. Kumaran, L. Singh, R. Kuriyan, P. Murthy, V. Benega, S. M. Plis, A. D. Sarwate, J. A. Turner, G. Schumann, and V. D. Calhoun, “Decentralized Multisite VBM Analysis During Adolescence Shows Structural Changes Linked to Age, Body Mass Index, and Smoking: a COINSTAC Analysis,” Neuroinformatics, Jan. 2021.
                                  6. K. E. Nikolakakis, D. S. Kalogerias, and A. D. Sarwate, “Predictive Learning on Hidden Tree-Structured Ising Models,” Journal of Machine Learning Research, vol. 22, no. 59, pp. 1–82, Apr. 2021.
                                  1. H. Gazula, R. Kelly, J. Romero, E. Verner, B. T. Baker, R. F. Silva, H. Imtiaz, D. K. Saha, R. Raja, J. A. Turner, A. D. Sarwate, S. M. Plis, and V. D. Calhoun, “COINSTAC: Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation,” Journal of Open Source Software, vol. 5, no. 54, p. 2166, Oct. 2020.
                                  2. M. Ghassemi, Z. Shakeri, A. D. Sarwate, and W. U. Bajwa, “Learning Mixtures of Separable Dictionaries for Tensor Data: Analysis and Algorithms,” IEEE Transactions on Signal Processing, vol. 68, no. 1, pp. 33–48, Jan. 2020.
                                  3. D. M. Bittner, A. E. Brito, M. Ghassemi, S. Rane, A. D. Sarwate, and R. N. Wright, “Understanding Privacy-Utility Tradeoffs Using Differentially Private Online Active Learning,” Journal of Privacy and Confidentiality, vol. 10, no. 2, Jun. 2020.
                                  1. T. Hazan, F. Orabona, A. D. Sarwate, S. Maji, and T. Jaakkola, “High Dimensional Inference with Random Maximum A-Posteriori Perturbations,” IEEE Transactions on Information Theory, vol. 65, no. 10, pp. 6539–6560, Oct. 2019.
                                  2. B. Baker, A. Abrol, R. F. Silva, E. Damaraju, A. D. Sarwate, V. D. Calhoun, and S. M. Plis, “Decentralized Temporal Independent Component Analysis: Leveraging fMRI Data in Collaborative Settings,” NeuroImage, vol. 186, pp. 557–569, Feb. 2019.
                                  1. Z. Shakeri, A. D. Sarwate, and W. U. Bajwa, “Identifiability of Kronecker-Structured Dictionaries for Tensor Data,” IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 5, pp. 1047–1062, Oct. 2018.
                                  2. H. Imtiaz and A. D. Sarwate, “Distributed Differentially-Private Algorithms for Matrix and Tensor Factorization,” IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 6, pp. 1449–1464, Dec. 2018.
                                  3. K. Kalantari, L. Sankar, and A. D. Sarwate, “Robust Privacy-Utility Tradeoffs under Differential Privacy and Hamming Distortion,” IEEE Transactions on Information Forensics and Security, vol. 13, no. 11, pp. 2816–2830, Nov. 2018.
                                  4. Z. Shakeri, W. U. Bajwa, and A. D. Sarwate, “Minimax Lower Bounds on Dictionary Learning for Tensor Data,” IEEE Transactions on Information Theory, vol. 64, no. 4, pp. 2706–2726, Apr. 2018.
                                  5. A. Lalitha, T. Javidi, and A. D. Sarwate, “Social Learning and Distributed Hypothesis Testing,” IEEE Transactions on Information Theory, vol. 64, no. 9, pp. 6161–6179, Sep. 2018.
                                  1. J. Ming, E. Verner, A. Sarwate, R. Kelly, C. Reed, T. Kahleck, R. Silva, S. Panta, J. Turner, S. Plis, and V. Calhoun, “COINSTAC: Decentralizing the future of brain imaging analysis,” F1000Research, vol. 6, no. 1512, Aug. 2017.
                                  2. A. Bijral, A. D. Sarwate, and N. Srebro, “Data Dependent Convergence For Consensus Stochastic Optimization,” IEEE Transactions on Automatic Control, vol. 62, no. 9, pp. 4483–4498, Sep. 2017.
                                  1. N. D. Goldstein and A. D. Sarwate, “Privacy, security, and the public health researcher in the era of electronic health record research,” Online Journal of Public Health Informatics, vol. 8, no. 3, p. e207, Dec. 2016.
                                  2. S. Plis, A. D. Sarwate, D. Wood, C. Dieringer, D. Landis, C. Reed, S. R. Panta, J. A. Turner, J. M. Shoemaker, K. W. Carter, P. Thompson, K. Hutchison, and V. D. Calhoun, “COINSTAC: A Privacy Enabled Model and Prototype for Leveraging and Processing Decentralized Brain Imaging Data,” Frontiers in Neuroscience, vol. 10, no. 365, Aug. 2016.
                                  3. C. Huang, L. Sankar, and A. D. Sarwate, “Designing Incentive Schemes For Privacy-Sensitive Users,” Journal of Privacy and Confidentiality, vol. 7, no. 1, pp. 99–127, Mar. 2016.
                                  1. A. D. Sarwate and T. Javidi, “Distributed Learning of Distributions via Social Sampling,” IEEE Transactions on Automatic Control, vol. 60, no. 1, pp. 34–45, Jan. 2015.
                                  1. N. P. Santhanam, A. D. Sarwate, and J. O. Woo, “Redundancy of Exchangeable Estimators,” Entropy, vol. 16, no. 10, pp. 5339–5357, Oct. 2014.
                                  2. A. D. Sarwate, S. M. Plis, J. A. Turner, M. R. Arbabshirani, and V. D. Calhoun, “Sharing privacy-sensitive access to neuroimaging and genetics data: a review and preliminary validation,” Frontiers in Neuroinformatics, vol. 8, no. 35, Apr. 2014.
                                  1. A. D. Sarwate and K. Chaudhuri, “Signal processing and machine learning with differential privacy: theory, algorithms, and challenges,” IEEE Signal Processing Magazine, vol. 30, no. 5, pp. 86–94, Sep. 2013.
                                  2. K. Chaudhuri, A. D. Sarwate, and K. Sinha, “A Near-Optimal Algorithm for Differentially-Private Principal Components,” Journal of Machine Learning Research, vol. 14, pp. 2905–2943, Sep. 2013.
                                  3. B. K. Dey, S. Jaggi, M. Langberg, and A. D. Sarwate, “Upper Bounds on the Capacity of Binary Channels with Causal Adversaries,” IEEE Transactions on Information Theory, vol. 59, no. 6, pp. 3753–3763, Jun. 2013.
                                  4. X. Jiang, A. D. Sarwate, and L. Ohno-Machado, “Privacy Technology to Share Data for Comparative Effectiveness Research : a systematic review,” Medical Care, vol. 51, no. 8 Suppl. 3, pp. S58–S65, Aug. 2013.
                                  1. A. D. Sarwate, S. Checkoway, and H. Shacham, “Risk-Limiting Audits and the Margin of Victory in Nonplurality Elections,” Statistics, Politics and Policy, vol. 3, no. 3, pp. 29–64, Dec. 2012.
                                  2. A. D. Sarwate and A. G. Dimakis, “The Impact of Mobility on Gossip Algorithms,” IEEE Transactions on Information Theory, vol. 58, no. 3, pp. 1731–1742, Mar. 2012.
                                  3. S. A. Vinterbo, A. D. Sarwate, and A. Boxwala, “Protecting Count Queries in Study Design,” Journal of the American Medical Informatics Association, vol. 19, no. 5, pp. 750–757, Sep. 2012.
                                  4. A. D. Sarwate and M. Gastpar, “List-Decoding for the Arbitrarily Varying Channel Under State Constraints,” IEEE Transactions on Information Theory, vol. 58, no. 3, pp. 1372–1384, Mar. 2012.
                                  1. K. Chaudhuri, C. Monteleoni, and A. D. Sarwate, “Differentially private empirical risk minimization,” Journal of Machine Learning Research, vol. 12, pp. 1069–1109, Mar. 2011.
                                  1. A. D. Sarwate and M. Gastpar, “A little feedback can simplify sensor network cooperation,” IEEE Journal of Selected Areas in Communication, vol. 28, no. 7, pp. 1159–1168, Sep. 2010.
                                  2. A. D. Sarwate and M. Gastpar, “Rateless codes for AVC models,” IEEE Transactions on Information Theory, vol. 56, no. 7, pp. 3105–3114, Jul. 2010.
                                  3. K. Eswaran, A. D. Sarwate, A. Sahai, and M. Gastpar, “Zero-rate feedback can achieve the empirical capacity,” IEEE Transactions on Information Theory, vol. 56, no. 1, pp. 25–39, Jan. 2010.
                                  1. T. C. Aysal, M. E. Yildiz, A. D. Sarwate, and A. Scaglione, “Broadcast Gossip Algorithms for Consensus,” IEEE Transactions on Signal Processing, vol. 57, no. 7, pp. 2748–2761, Jul. 2009.
                                  1. A. G. Dimakis, A. D. Sarwate, and M. J. Wainwright, “Geographic Gossip: Efficient Averaging for Sensor Networks,” IEEE Transactions on Signal Processing, vol. 56, no. 3, pp. 1205–1215, Mar. 2008.
                                    1. A. D. Sarwate and V. Anantharam, “Exact emulation of a priority queue with a switch and delay lines,” Queuing Systems : Theory and Applications, vol. 53, no. 3, pp. 115–125, Jul. 2006.

                                      Conference Papers

                                        1. S. Li, P. Krishan, S. Jaggi, M. Langberg, and A. D. Sarwate, “Computationally Efficient Codes for Adversarial Binary-Erasure Channels,” in Proceedings of the 2023 IEEE International Symposium on Information Theory (ISIT), 2023, pp. to appear.
                                          1. Y. Tao, A. Chihoub, A. D. Sarwate, S. Panta, and V. Calhoun, “Privacy-Preserving Visualization of Functional Network Connectivity,” in International Conference of the IEEE Engineering in Medicine and Biology Society, Glasgow, Scotland, UK, 2022.
                                          2. Y. Zhang, S. Jaggi, M. Langberg, and A. D. Sarwate, “The Capacity of Causal Adversarial Channels,” in Proceedings of the 2022 IEEE International Symposium on Information Theory (ISIT), 2022.
                                          3. N. Sathyavageeswaran, R. D. Yates, A. D. Sarwate, and N. Mandayam, “Privacy Leakage in Discrete Time Updating Systems,” in Proceedings of the 2022 IEEE International Symposium on Information Theory (ISIT), 2022.
                                          4. F. Cangialosi, N. Agarwal, V. Arun, J. Jiang, S. Narayana, A. Sarwate, and R. Netravali, “Privid: Practical, Privacy-Preserving Video Analytics Queries,” in Proceedings of the 19th USENIX Symposium on Networked Systems Design and Implementation (NSDI ’22), 2022.
                                          5. S. M. Kwon, X. Li, and A. D. Sarwate, “Low-Rank Phase Retrieval with Structured Tensor Models,” in Proceedings of the 47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2022), 2022, pp. 3643–3647.
                                            1. B. Taki, M. Ghassemi, A. D. Sarwate, and W. U. Bajwa, “A Minimax Lower Bound for Low-Rank Matrix-Variate Logistic Regression,” in 2021 Asilomar Conference on Signals, Systems, and Computers, 2021, pp. 477–484.
                                            2. A. Rezaie, J. Gao, and A. D. Sarwate, “Influencers and the Giant Component: the Fundamental Hardness in Privacy Protection for Socially Contagious Attributes,” in SIAM International Conference on Data Mining, 2021, pp. 217–225.
                                              1. A. J. Budkuley, B. K. Dey, S. Jaggi, M. Langberg, A. D. Sarwate, and C. Wang, “Symmetrizability for Myopic AVCs,” in Proceedings of the 2020 IEEE International Symposium on Information Theory (ISIT), 2020.
                                              1. K. Nikolakakis, D. Kalogerias, and A. D. Sarwate, “Learning Tree Structures from Noisy Data,” in Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (AISTATS), vol. 89, K. Chaudhuri and R. Salakhutdinov, Eds. Naha, Okinawa, Japan: PMLR, 2019, pp. 1771–1782.
                                              1. H. Imtiaz and A. D. Sarwate, “Distributed Differentially Private Canonical Correlation Analysis,” in Proceedings of the 44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, 2019, pp. 3112–3116.
                                              2. B. K. Dey, S. Jaggi, M. Langberg, A. D. Sarwate, and C. Wang, “The Interplay of Causality and Myopia in Adversarial Channel Models,” in Proceedings of the 2019 IEEE International Symposium on Information Theory (ISIT), Paris, France, 2019.
                                              3. M. Ghassemi, Z. Shakeri, W. U. Bajwa, and A. D. Sarwate, “Sample Complexity Bounds for Low-Separation-Rank Dictionary Learning,” in Proceedings of the 2019 IEEE International Symposium on Information Theory (ISIT), Paris, France, 2019.
                                              1. D. Bittner, A. D. Sarwate, and R. Wright, “Using Noisy Binary Search for Differentially Private Anomaly Detection,” in Proceedings of the 2nd International Symposium on Cyber Security Cryptography and Machine Learning (CSCML), vol. 10879, I. Dinur, S. Dolev, and S. Lodha, Eds. Springer, 2018, pp. 20–37.
                                              1. Y. Zhang, S. Vatedka, S. Jaggi, and A. D. Sarwate, “Quadratically Constrained Myopic Adversarial Channels,” in Proceedings of the 2018 IEEE International Symposium on Information Theory (ISIT), Vail, Colorado, USA, 2018, pp. 611–615.
                                              2. T. Li, B. K. Dey, S. Jaggi, M. Langberg, and A. D. Sarwate, “Quadratically Constrained Channels with Causal Adversaries,” in Proceedings of the 2018 IEEE International Symposium on Information Theory (ISIT), Vail, Colorado, USA, 2018, pp. 621–625.
                                              3. G. R. Kurri, V. M. Prabhakaran, and A. D. Sarwate, “Coordination Using Individually Shared Randomness,” in Proceedings of the 2018 IEEE International Symposium on Information Theory (ISIT), Vail, Colorado, USA, 2018, pp. 2550–2554.
                                              4. H. Imtiaz and A. D. Sarwate, “Differentially Private Distributed Principal Component Analysis,” in Proceedings of the 43rd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada, 2018, pp. 2206–2210.
                                              5. S. Xiong, A. D. Sarwate, and N. B. Mandayam, “Defending Against Packet-Size Side-Channel Attacks in IoT Networks,” in Proceedings of the 43rd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada, 2018, pp. 2027–2031.
                                              6. M. Ghassemi, N. Goela, and A. D. Sarwate, “Global Optimality in Inductive Matrix Completion,” in Proceedings of the 43rd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada, 2018, pp. 2226–2230.
                                              7. H. Imtiaz and A. D. Sarwate, “Improved Algorithms for Differentially Private Orthogonal Tensor Decomposition,” in Proceedings of the 43rd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada, 2018, pp. 2201–2205.
                                                1. M. Ghassemi, Z. Shakeri, A. D. Sarwate, and W. U. Bajwa, “STARK: Structured Dictionary Learning Through Rank-one Tensor Recovery,” in Proceedings of the 7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Curaçao, Netherlands Antilles, 2017, pp. 1–5.
                                                2. Z. Shakeri, A. D. Sarwate, and W. U. Bajwa, “Identification of Kronecker-structured dictionaries: An asymptotic analysis,” in Proceedings of the 7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Curaçao, Netherlands Antilles, 2017, pp. 1–5.
                                                3. H. Imtiaz and A. D. Sarwate, “Differentially Private Canonical Correlation Analysis,” in Proceedings of the 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Montreal, QC, Canada, 2017, pp. 283–287.
                                                4. B. Liu, C. Wen, A. D. Sarwate, and M. M. Dehnavi, “A Unified Optimization Approach for Sparse Tensor Operations on GPUs,” in Proceedings of the 2017 IEEE International Conference on Cluster Computing (CLUSTER), Honolulu, HI, USA, 2017, pp. 47–57.
                                                5. Z. Shakeri, W. U. Bajwa, and A. D. Sarwate, “Sample Complexity Bounds for Dictionary Learning of Tensor Data,” in Proceedings of the 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA, 2017, pp. 4501–4505.
                                                6. N. Wojtalewicz, R. Silva, V. Calhoun, A. Sarwate, and S. Plis, “Decentralized Independent Vector Analysis,” in Proceedings of the 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA, 2017, pp. 826–830.
                                                  1. A. Bijral, A. D. Sarwate, and N. Srebro, “Data-Dependent Bounds on Network Gradient Descent,” in Proceedings of the 54th Annual Allerton Conference on Communication, Control, and Computing, Monticello, IL, USA, 2016, pp. 869–874.
                                                  2. M. Ghassemi, A. D. Sarwate, and R. Wright, “Differentially Private Online Active Learning with Applications to Anomaly Detection,” in Proceedings of the 9th ACM Workshop on Artificial Intelligence and Security (AISec), Vienna, Austria, 2016, pp. 117–128.
                                                  3. L. Wei, A. D. Sarwate, J. Corander, A. Hero, and V. Tarokh, “Analysis of a Privacy-preserving PCA Algorithm using Random Matrix Theory,” in Proceedings of the 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Washington, DC, USA, 2016, pp. 1335–1339.
                                                  4. B. K. Dey, S. Jaggi, M. Langberg, and A. D. Sarwate, “A bit of delay is sufficient and stochastic encoding is necessary to overcome online adversarial erasures,” in Proceedings of the 2016 IEEE International Symposium on Information Theory (ISIT), Barcelona, Spain, 2016, pp. 880–884.
                                                  5. Z. Shakeri, W. U. Bajwa, and A. D. Sarwate, “Minimax Lower Bounds for Kronecker-Structured Dictionary Learning,” in Proceedings of the 2016 IEEE International Symposium on Information Theory (ISIT), Barcelona, Spain, 2016, pp. 1148–1152.
                                                  6. K. Kalantari, L. Sankar, and A. D. Sarwate, “Optimal Differential Privacy Mechanisms under Hamming Distortion for Structured Source Classes,” in Proceedings of the 2016 IEEE International Symposium on Information Theory (ISIT), Barcelona, Spain, 2016, pp. 2069–2073.
                                                  7. H. Imtiaz, R. Silva, B. Baker, S. M. Plis, A. D. Sarwate, and V. D. Calhoun, “Privacy-preserving source separation for distributed data using independent component analysis,” in Proceedings of the 2016 Annual Conference on Information Science and Systems (CISS), Princeton, NJ, USA, 2016, pp. 123–127.
                                                  8. S. Xiong, A. D. Sarwate, and N. B. Mandayam, “Randomized Requantization with Local Differential Privacy,” in Proceedings of the 2016 International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China, 2016, pp. 2189–2193.
                                                  9. L. Xie, S. M. Plis, and A. Sarwate, “Data Weighted Ensemble Learning for Privacy-Preserving Distributed Learning,” in Proceedings of the 2006 International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China, 2016, pp. 2309–2313.
                                                  1. C. Huang, L. Sankar, and A. D. Sarwate, “Incentive Schemes For Privacy-Sensitive Consumers,” in Decision and Game Theory for Security, no. 9406, M. H. R. Khouzani, E. Panaousis, and G. Theodorakopoulos, Eds. Cham, Switzerland: Springer, 2015, pp. 358–369.
                                                  2. S. Song, K. Chaudhuri, and A. D. Sarwate, “Learning from Data with Heterogeneous Noise using SGD,” in Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics (AISTATS), vol. 38, G. Lebanon and S. V. N. Vishwanathan, Eds. San Diego, California, USA: PMLR, 2015, pp. 894–902.
                                                  3. T. Wu, A. D. Sarwate, and W. U. Bajwa, “Active dictionary learning for image representation,” in Unmanned Systems Technology XVII, vol. 9468, no. 946809, R. E. Karlsen, D. W. Gage, C. M. Shoemaker, and G. R. Gerhart, Eds. SPIE, 2015, pp. 1–10.
                                                  1. A. Chatterjee, A. D. Sarwate, and S. Vishwanath, “Generalized Opinion Dynamics from Local Optimization Rules,” in Proceedings of the 49th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, 2015, pp. 1075–1079.
                                                  2. M. Ghassemi and A. D. Sarwate, “Distributed Proportional Stochastic Coordinate Descent with Social Sampling,” in Proceedings of the 53rd Annual Allerton Conference on Communication, Control, and Computing, Monticello, IL, USA, 2015, pp. 17–24.
                                                  3. B. Baker, R. Silva, V. D. Calhoun, A. D. Sarwate, and S. Plis, “Large scale collaboration with autonomy: decentralized data ICA,” in Proceedings of the IEEE International Workshop on Machine Learning For Signal Processing (MLSP), Boston, MA, USA, 2015, pp. 1–6.
                                                  1. S. Plis, A. Sarwate, J. Turner, M. Arbabshirani, and V. Calhoun, “From Private Sites to Big Data Without Compromising Privacy: A Case of Neuroimaging Data Classification,” in International Society For Pharmacoeconomics and Outcomes Research (ISPOR) 19th Annual International Meeting, vol. 17, 2014, p. 3.
                                                  2. F. Orabona, T. Hazan, A. D. Sarwate, and T. Jaakkola, “On Measure Concentration of Random Maximum A-Posteriori Perturbations,” in Proceedings of the 31st International Conference on Machine Learning (ICML), vol. 32, E. P. Xing and T. Jebara, Eds. Beijing, China: PLMR, 2014, pp. 432–440.
                                                  1. V. K. Potluru, J. Diaz-Montes, A. D. Sarwate, S. M. Plis, V. D. Calhoun, B. A. Pearlmutter, and M. Parashar, “CometCloudCare (C^3): Distributed Machine Learning Platform-as-a-Service with Privacy Preservation,” in NIPS 2014 Workshop on Distributed Machine Learning and Matrix Computations, Montreal, Canada, 2014, pp. 1–9.
                                                  2. A. D. Sarwate and L. Sankar, “A Rate-Disortion [sic] Perspective on Local Differential Privacy,” in Proceedings of the 52nd Annual Allerton Conference on Communication, Control and Computation, Monticello, IL, USA, 2014.
                                                  3. K. I. Tsianos, A. D. Sarwate, and M. G. Rabbat, “Tradeoffs For Task Parallelization In Distributed Optimization,” in Proceedings of the IEEE International Workshop on Machine Learning For Signal Processing (MLSP), Reims, France, 2014, pp. 1–6.
                                                  4. A. Lalitha, A. D. Sarwate, and T. Javidi, “Social Learning and Distributed Hypothesis Testing,” in Proceedings of the 2014 IEEE International Symposium on Information Theory (ISIT), Honolulu, HI, USA, 2014, pp. 551–555.
                                                  1. S. Sabato, A. D. Sarwate, and N. Srebro, “Auditing: Active Learning with Outcome-Dependent Query Costs,” in Advances in Neural Information Processing Systems (NIPS) 26, C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2013, pp. 512–520.
                                                  1. S. Song, K. Chaudhuri, and A. D. Sarwate, “Stochastic Gradient Descent with Differentially Private Updates,” in Proceedings of the 2013 Global Conference on Signal and Information Processing (GlobalSIP), Austin, TX, USA, 2013, pp. 245–248.
                                                  2. V. M. Prabhakaran and A. D. Sarwate, “Assisted Sampling of Correlated Sources,” in Proceedings of the 2013 IEEE International Symposium on Information Theory (ISIT), Istanbul, Turkey, 2013, pp. 3155–3159.
                                                  1. K. Chaudhuri, A. D. Sarwate, and K. Sinha, “Near-optimal Differentially Private Principal Components,” in Advances in Neural Information Processing Systems (NIPS) 25, P. Bartlett, F. C. N. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2012, pp. 989–997.
                                                  1. A. D. Sarwate, “Merging Opinions by Social Sampling of Posteriors,” in Proceedings of the 50th Annual Allerton Conference on Communication, Control and Computation, Monticello, IL, USA, 2012, pp. 379–385.
                                                  2. A. D. Sarwate, “An AVC perspective on correlated jamming,” in Proceedings of the International Conference on Signal Processing and Communications (SPCOM), Bangalore, India, 2012, pp. 1–5.
                                                  3. A. D. Sarwate and T. Javidi, “Distributed learning from social sampling,” in Proceedings of the 46th Annual Conference on Information Sciences and Systems (CISS), Princeton, NJ, USA, 2012.
                                                  4. B. K. Dey, S. Jaggi, M. Langberg, and A. D. Sarwate, “Improved Upper Bounds on the Capacity of Binary Channels with Causal Adversaries,” in Proceedings of the 2012 IEEE International Symposium on Information Theory (ISIT), Cambridge, MA, USA, 2012, pp. 681–685.
                                                    1. A. D. Sarwate and T. Javidi, “Opinion Dynamics and Distributed Learning of Distributions,” in Proceedings of the 49th Annual Allerton Conference on Communication, Control and Computation, Monticello, IL, USA, 2011, pp. 1151–1158.
                                                    2. S. A. Vinterbo, A. D. Sarwate, and A. Boxwala, “Protecting Count Queries in Cohort Identification,” in Proceedings of the 2011 AMIA Summit on Clinical Research Informatics, San Francisco, CA, USA, 2011, pp. 1–1.
                                                      1. S. Checkoway, A. Sarwate, and H. Shacham, “Single-Ballot Risk-Limiting Audits Using Convex Optimization,” in Proceedings of the 2010 Electronic Voting Technology Workshop/Workshop on Trustworthy Elections (EVT/WOTE), Washington, DC, USA, 2010, pp. 1–15.
                                                      2. M. Wigger and A. D. Sarwate, “Linear Strategies for the Gaussian MAC With User Cooperation,” in Proceedings of the 48th Annual Allerton Conference on Communication, Control and Computation, Monticello, IL, USA, 2010, pp. 1046–1053.
                                                      3. A. D. Sarwate, “Coding against myopic adversaries,” in Proceedings of the 2010 Information Theory Workshop (ITW), Dublin, Ireland, 2010, pp. 1–5.
                                                      4. N. P. Santhanam, M. Madiman, and A. D. Sarwate, “Redundancy of exchangeable estimators,” in Proceedings of the 48th Annual Allerton Conference on Communication, Control, and Computing, Monticello, IL, USA, 2010, pp. 1153–1157.
                                                      5. B. K. Dey, M. Langberg, S. Jaggi, and A. D. Sarwate, “Coding against delayed adversaries,” in Proceedings of the 2010 IEEE International Symposium on Information Theory (ISIT), Austin, Texas, USA, 2010, pp. 285–289.
                                                        1. A. D. Sarwate and A. G. Dimakis, “The Impact of Mobility on Gossip Algorithms,” in Proceedings of the 28th Annual International Conference on Computer Communications (INFOCOM), Rio de Janeiro, Brazil, 2009, pp. 2088–2096.
                                                        2. A. D. Sarwate and M. Gastpar, “Some observations on limited feedback for multiaccess channels,” in Proceedings of the 2009 IEEE International Symposium on Information Theory (ISIT), Seoul, South Korea, 2009.
                                                        3. A. D. Sarwate and A. G. Dimakis, “Gossip and consensus in mobile networks,” in Proceedings of the Third International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Aruba, Duch Antilles, 2009, pp. 57–60.
                                                        4. T. C. Aysal, A. D. Sarwate, and A. G. Dimakis, “Reaching consensus in wireless networks with probabilistic broadcast,” in Proceedings of the 47th Annual Allerton Conference on Communication, Control, and Computation, Monticello, IL, USA, 2009, pp. 732–739.
                                                          1. T. C. Aysal, M. E. Yildiz, A. D. Sarwate, and A. Scaglione, “Broadcast Gossip Algorithms: Design and Analysis for Consensus,” in Proceedings of the 47th IEEE Conference on Decision and Control (CDC), Cancún, Mexico, 2008, pp. 4843–4848.
                                                          2. A. D. Sarwate and M. Gastpar, “Adversarial interference models for multiantenna cooperative systems,” in Proceedings of the 42nd Annual Conference on Information Sciences and Systems (CISS), Princeton, NJ, USA, 2008, pp. 785–790.
                                                          3. A. D. Sarwate and M. Gastpar, “Arbitrarily dirty paper coding and applications,” in Proceedings of the 2008 IEEE International Symposium on Information Theory (ISIT), Toronto, Canada, 2008, pp. 925–929.
                                                            1. A. D. Sarwate and M. Gastpar, “Channels with nosy ‘noise,’” in Proceedings of the 2007 IEEE International Symposium on Information Theory (ISIT), Nice, France, 2007, pp. 996–1000.
                                                            2. K. Eswaran, A. D. Sarwate, A. Sahai, and M. Gastpar, “Using zero-rate feedback on binary additive channels with individual noise sequences,” in Proceedings of the 2007 IEEE International Symposium on Information Theory (ISIT), Nice, France, 2007, pp. 1431–1435.
                                                            3. A. D. Sarwate and M. Gastpar, “Rateless coding with partial CSI at the decoder,” in Proceedings of the 2007 Information Theory Workshop (ITW), Lake Tahoe, CA, USA, 2007, pp. 378–383.
                                                            4. A. D. Sarwate, B. Nazer, and M. Gastpar, “Spatial filtering in sensor networks using computation codes,” in Proceedings of the 2007 IEEE Statistical Signal Processing Workshop (SSP), Madison, WI, USA, 2007, pp. 635–639.
                                                              1. A. D. G. Dimakis, A. D. Sarwate, and M. J. Wainwright, “Geographic Gossip : Efficient Aggregation for Sensor Networks,” in 5th International Symposium on Information Processing in Sensor Networks (IPSN), Nashville, TN, USA, 2006, pp. 69–76.
                                                              2. A. D. Sarwate and M. Gastpar, “Randomization for robust communication in networks, or ‘Brother, can you spare a bit?,’” in Proceedings of the 44th Annual Allerton Conference on Communication, Control and Computation, Monticello, IL, USA, 2006, pp. 978–976.
                                                              3. A. D. Sarwate and M. Gastpar, “Randomization bounds on Gaussian arbitrarily varying channels,” in Proceedings of the 2006 IEEE International Symposium on Information Theory (ISIT), Seattle, WA, USA, 2006, pp. 2161–2165.
                                                                1. A. D. Sarwate and M. Gastpar, “Fading observation alignment via feedback,” in Proceedings of the Fourth International Symposium on Information Processing in Sensor Networks (IPSN), Los Angeles, CA, USA, 2005, pp. 317–323.
                                                                2. A. D. Sarwate and M. Gastpar, “Estimation from Misaligned Observations with Limited Feedback,” in Proceedings of the 39th Conference on Information Sciences and Systems (CISS), Baltimore, MD, USA, 2005, pp. 1–6.

                                                                Book Chapters

                                                                    1. Z. Shakeri, A. D. Sarwate, and W. U. Bajwa, “Sample Complexity Bounds for Dictionary Learning from Vector- and Tensor-valued Data,” in Information-Theoretic Methods in Data Science, M. Rodrigues and Y. C. Eldar, Eds. Cambridge, UK: Cambridge University Press, 2021, pp. 134–162.

                                                                                                    Theses

                                                                                                    1. A. D. Sarwate, “Robust and adaptive communication under uncertain interference,” PhD thesis, University of California, Berkeley, 2008.
                                                                                                    1. A. D. Sarwate, “Observation Uncertainty in Gaussian Sensor Networks,” Master's thesis, University of California, Berkeley, Berkeley, CA, USA, 2005.
                                                                                                    1. A. Sarwate, “Longest Increasing Subsequences and Random Matrices,” MIT Undergraduate Journal of Mathematics, vol. 4, pp. 157–166, 2002.