Anuran Makur

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• Q. Zhang, A. Makur, and K. Azizzadenesheli, "Functional linear regression of cumulative distribution functions," Transactions on Machine Learning Research, pp. 1-56, March 2024. (arXiv)

• A. Jadbabaie, A. Makur, and D. Shah, "Gradient-based empirical risk minimization using local polynomial regression," Stochastic Systems, INFORMS, March 2024. (older manuscript)

• A. Makur and J. Singh, "Doeblin coefficients and related measures," IEEE Transactions on Information Theory, February 2024. (arXiv)

• W. Lu and A. Makur, "Permutation capacity region of adder multiple-access channels," IEEE Transactions on Information Theory, January 2024. (arXiv)

• A. Jadbabaie, A. Makur, and D. Shah, "Federated optimization of smooth loss functions," IEEE Transactions on Information Theory, vol. 69, no. 12, pp. 7836-7866, December 2023. (arXiv)

• R. Cosson, A. Jadbabaie, A. Makur, A. Reisizadeh, and D. Shah, "Low-rank gradient descent," IEEE Open Journal of Control Systems, vol. 2, pp. 380-395, October 2023. (invited paper to special section on "Intersection of Machine Learning with Control"; older manuscript)

• A. Jadbabaie, A. Makur, and D. Shah, "Estimation of skill distributions," preprint under submission, September 2023. (older manuscript)

• A. Jadbabaie, A. Makur, E. Mossel, and R. Salhab, "Inference in opinion dynamics under social pressure," IEEE Transactions on Automatic Control, vol. 68, no. 6, pp. 3377-3392, June 2023. (arXiv)

• A. Makur, E. Mossel, and Y. Polyanskiy, "Broadcasting on two-dimensional regular grids," IEEE Transactions on Information Theory, vol. 68, no. 10, pp. 6297-6334, October 2022. (arXiv)

• A. Makur, "Coding theorems for noisy permutation channels," IEEE Transactions on Information Theory, vol. 66, no. 11, pp. 6723-6748, November 2020. (arXiv)

• A. Makur and L. Zheng, "Comparison of contraction coefficients for \(f\)-divergences," Problems of Information Transmission, vol. 56, no. 2, pp. 103-156, April 2020. (older manuscript)

• A. Makur, E. Mossel, and Y. Polyanskiy, "Broadcasting on random directed acyclic graphs," IEEE Transactions on Information Theory, vol. 66, no. 2, pp. 780-812, February 2020. (arXiv; older manuscript subsequently split into two papers)

• A. Makur and Y. Polyanskiy, "Comparison of channels: Criteria for domination by a symmetric channel," IEEE Transactions on Information Theory, vol. 64, no. 8, pp. 5704-5725, August 2018. (arXiv)

• A. Makur and L. Zheng, "Polynomial singular value decompositions of a family of source-channel models," IEEE Transactions on Information Theory, vol. 63, no. 12, pp. 7716-7728, December 2017.

• A. Makur, "Complex robust whitening with application to blind identification of same DoA multipath," Signal Processing, Elsevier, vol. 94, pp. 514-520, January 2014.

• S.-L. Huang, A. Makur, G. W. Wornell, and L. Zheng, "Universal features for high-dimensional learning and inference," Foundations and Trends in Communications and Information Theory, vol. 21, no. 1-2, pp. 1–299, February 2024. (older manuscript)

• W. Lu, A. Makur, and J. Singh, "On Doeblin curves and their properties," accepted to IEEE International Symposium on Information Theory (ISIT), Athens, Greece, July 7-12 2024.

• W. Lu and A. Makur, "On permutation capacity regions of multiple-access channels," accepted to IEEE International Symposium on Information Theory (ISIT), Athens, Greece, July 7-12 2024.

• K. Zhang, S. Cheng, G. Shen, G. Tao, S. An, A. Makur, S. Ma, and X. Zhang, "Exploring the orthogonality and linearity of backdoor attacks," accepted to the 45th IEEE Symposium on Security and Privacy (S & P), San Francisco, CA, USA, May 20-23 2024.

• A. Makur, M. Mertzanidis, A. Psomas, and A. Terzoglou, "On the robustness of mechanism design under total variation distance," in Proceedings of the Advances in Neural Information Processing Systems 36 (NeurIPS), New Orleans, LA, USA, December 10-16 2023, pp. 1620–1629. (arXiv)

• A. Jadbabaie, A. Makur, and A. Reisizadeh, "Adaptive low-rank gradient descent," in Proceedings of the 62nd IEEE Conference on Decision and Control (CDC), Singapore, December 13-15 2023, pp. 3315–3320.

• R. Cosson, A. Jadbabaie, A. Makur, A. Reisizadeh, and D. Shah, "Gradient descent with low-rank objective functions," in Proceedings of the 62nd IEEE Conference on Decision and Control (CDC), Singapore, December 13-15 2023, pp. 3309–3314.

• A. Makur and J. Singh, "Testing for the Bradley-Terry-Luce model," in Proceedings of the IEEE International Symposium on Information Theory (ISIT), Taipei, Taiwan, June 25-30 2023, pp. 1390–1395.

• A. Makur and J. Singh, "On properties of Doeblin coefficients," in Proceedings of the IEEE International Symposium on Information Theory (ISIT), Taipei, Taiwan, June 25-30 2023, pp. 72–77.

• W. Lu and A. Makur, "Permutation sum-capacity of binary adder multiple-access channels," in Proceedings of the IEEE International Symposium on Information Theory (ISIT), Taipei, Taiwan, June 25-30 2023, pp. 933–938.

• A. Makur, E. Mossel, and Y. Polyanskiy, "Reconstruction on 2D regular grids," in Proceedings of the IEEE International Symposium on Information Theory (ISIT), Melbourne, Australia, July 12-20 2021, pp. 2107–2112.

• A. Jadbabaie, A. Makur, and D. Shah, "Estimation of skill distribution from a tournament," in Proceedings of the Advances in Neural Information Processing Systems 33 (NeurIPS), Vancouver, BC, Canada, December 6-12 2020, pp. 8418–8429. (spotlight paper)

• A. Makur, "Bounds on permutation channel capacity," in Proceedings of the IEEE International Symposium on Information Theory (ISIT), Los Angeles, CA, USA, June 21-26 2020, pp. 2026–2031.

• A. Makur, G. W. Wornell, and L. Zheng, "On estimation of modal decompositions," in Proceedings of the IEEE International Symposium on Information Theory (ISIT), Los Angeles, CA, USA, June 21-26 2020, pp. 2717–2722.

• A. Makur, E. Mossel, and Y. Polyanskiy, "Broadcasting on random networks," in Proceedings of the IEEE International Symposium on Information Theory (ISIT), Paris, France, July 7-12 2019, pp. 1632–1636.

• A. Makur, "Information capacity of BSC and BEC permutation channels," in Proceedings of the 56th Annual Allerton Conference on Communication, Control, and Computing, Monticello, IL, USA, October 2-5 2018, pp. 1112–1119.

• D. Qiu, A. Makur, and L. Zheng, "Probabilistic clustering using maximal matrix norm couplings," in Proceedings of the 56th Annual Allerton Conference on Communication, Control, and Computing, Monticello, IL, USA, October 2-5 2018, pp. 1020–1027. (arXiv)

• S.-L. Huang, A. Makur, G. W. Wornell, and L. Zheng, "An information-theoretic view of learning in high dimensions: Universal features, maximal correlations, bottlenecks, and common information," in Information Theory and Applications Workshop (ITA), San Diego, CA, USA, February 11-16 2018. (presentation)

• A. Makur and Y. Polyanskiy, "Less noisy domination by symmetric channels," in Proceedings of the IEEE International Symposium on Information Theory (ISIT), Aachen, Germany, June 25-30 2017, pp. 2463–2467.

• S.-L. Huang, A. Makur, L. Zheng, and G. W. Wornell, "An information-theoretic approach to universal feature selection in high-dimensional inference," in Proceedings of the IEEE International Symposium on Information Theory (ISIT), Aachen, Germany, June 25-30 2017, pp. 1336–1340.

• A. Makur and L. Zheng, "Polynomial spectral decomposition of conditional expectation operators," in Proceedings of the 54th Annual Allerton Conference on Communication, Control, and Computing, Monticello, IL, USA, September 27-30 2016, pp. 633–640.

• A. Makur and L. Zheng, "Bounds between contraction coefficients," in Proceedings of the 53rd Annual Allerton Conference on Communication, Control, and Computing, Monticello, IL, USA, September 29-October 2 2015, pp. 1422–1429.

• A. Makur, F. Kozynski, S.-L. Huang, and L. Zheng, "An efficient algorithm for information decomposition and extraction," in Proceedings of the 53rd Annual Allerton Conference on Communication, Control, and Computing, Monticello, IL, USA, September 29-October 2 2015, pp. 972–979. (invited paper)

• S.-L. Huang, A. Makur, F. Kozynski, and L. Zheng, "Efficient statistics: Extracting information from iid observations," in Proceedings of the 52nd Annual Allerton Conference on Communication, Control, and Computing, Monticello, IL, USA, October 1-3 2014, pp. 699–706.

• A. Makur, "Information contraction and decomposition," Doctoral Thesis in Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA, May 2019. (Jin Au Kong Ph.D. Thesis Award, MIT; Thomas M. Cover Dissertation Award, IEEE Information Theory Society)

• A. Makur, "A study of local approximations in information theory," Master's Thesis in Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA, April 2015. (Ernst A. Guillemin S.M. Thesis Award, MIT)

Note: If you use any of these notes, please acknowledge or cite the source.

• A. Makur, "Purdue CS 18200 Supplementary Notes," Supplementary Lecture Notes for Course CS 18200 Foundations of Computer Science, Department of Computer Science, Purdue University, West Lafayette, IN, USA, Fall 2023. (This includes computability and complexity, some number theory and recursion, and practice problem solutions.)

• A. Makur, "Purdue ECE 20875 Supplementary Notes," Supplementary Lecture Notes for Course ECE 20875 Python for Data Science, School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA, Spring 2023. (This includes leave-one-out approximation, estimation and sampling, induced distributions, and regression.)

• A. Makur, "MIT 6.437 Recitation Notes," Recitation Notes for Course 6.437 Inference and Information, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA, Spring 2017. (This includes Bayesian estimation, sufficient statistics, differential entropy, and mixing of Markov chains.)

• A. Makur, "Free convolution and Jacobi matrices," Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA, May 2016. (Some exploratory notes from when I took the course 18.338 Random Matrix Theory at MIT.)

• T. A. Lahlou and A. Makur, "Transient signal spaces and decompositions," Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA, September 2015. (A technical report summarizing my discussions on the transient signal decomposition problem after I took the course 6.341 Discrete-Time Signal Processing at MIT.)