* equal contribution authors. alphabetical authorship.
Papers
A. Ganesh, M. Haghifam, T. Steinke, A. Thakurta ‘‘ Faster Differentially Private Convex Optimization via Second-Order Methods’’, preprint, 2023.
A. Ganesh, M. Haghifam, M. Nasr, S. Oh, T. Steinke, O. Thakkar, A. Thakurta, L. Wang ‘‘ Why Is Public Pretraining Necessary for Private Model Training?’’, preprint, 2023 [paper]
M. Haghifam*, B. Rodriguez-Galvez*, R. Thobaben, M. Skoglund, D. M. Roy, G. K. Dziugaite ‘‘Limitations of Information-Theoretic Generalization Bounds for Gradient Descent Methods in Stochastic Convex Optimization’’, International Conference on Algorithmic Learning Theory 34 (ALT), 2023 [paper].
M. Haghifam, S. Moran, D. M. Roy, G. K. Dziugaite, ‘‘Understanding Generalization via Leave-One-Out Conditional Mutual Information’’, International Symposium on Information Theory 2022 [paper].
M.Haghifam*, M. N. Krishnan*, A. Khisti, X. Zhu, W. Dan, J. Apostolopoulos, ‘‘On Streaming Codes With Unequal Error Protection’’, IEEE Journal on Selected Areas in Information Theory (Volume: 2, Issue: 4, Dec. 2021)
[paper].
M. Haghifam, V. Y. F. Tan, A. Khisti, ‘‘Sequential Classification with Empirically Observed Statistics’’, IEEE Transactions on Information Theory (Volume: 67, Issue: 5, May 2021)
[paper].
M. Haghifam, G. K. Dziugaite, S. Moran, D. M. Roy, ‘‘Towards a Unified Information–Theoretic Framework for Generalization’’, Advances in Neural Information Processing Systems 35 (NeurIPS), 2021 (Spotlight, <3% of submissions)
[paper].
(Also Presented as a workshop paper at ICML-21 Workshop on Information-Theoretic Methods for Rigorous, Responsible, and Reliable Machine Learning.)
G. Neu, G. K. Dziugaite, M. Haghifam, D. M. Roy , ‘‘Information-Theoretic Generalization Bounds for Stochastic Gradient Descent’’, Annual Conference on Learning Theory 34 (COLT), 2021
[paper].
M. Haghifam, J. Negrea, A. Khisti, D. M. Roy , G. K. Dziugaite, ‘‘Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms’’, Advances in Neural Information Processing Systems 34 (NeurIPS), 2020
[paper].
J. Negrea*, M. Haghifam*, G. K. Dziugaite, A. Khisti, D. M. Roy, ‘‘Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates’’, Advances in Neural Information Processing Systems 33 (NeurIPS), 2019
[paper].
(Also Presented as a workshop paper at ICML-19 Workshop in Understanding and Improving Generalization in Deep Learning.)
M. Haghifam, M. Robat Mili, B. Makki, M. Nasiri-Kenari, T. Svensson, ‘‘Joint Sum Rate And Error Probability Optimization: Finite Blocklength Analysis’’, IEEE Wireless Communications Letters (Volume: 6, Issue: 6, Dec. 2017)
[paper].
Workshop Papers
M. Haghifam, V. Y. F. Tan, and A. Khisti, ‘‘Sequential Classification with Empirically Observed Statistics’’, IEEE Information Theory Workshop 2019, Visby, Gotland, Sweden
[paper].
M. Haghifam, A. Badr, A. Khisti, X. Zhu, W. Dan and J. Apostolopoulos, ‘‘Streaming Codes with Unequal Error Protection against Burst Losses’’, The 29th Biennial Symposium on Communications (BSC 2018)
[paper].
M. Haghifam, B. Makki, M. Nasiri-Kenari, T. Svensson, ‘‘On joint information and energy transfer in relay networks with an imperfect power amplifier’’, 27th Annual IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Valencia, Spain, 2016
[paper].
M. Haghifam, M. R. Haghifam, B. Safari Chabook, ‘‘State estimation in electric distribution networks in presence of distributed generation using the PMUs’’, CIRED 2012, Lisbon, Portugal
[paper]
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