Mahdi Haghifam
 |
Ph.D. candidate, |
About Me
I am a final year PhD candidate at University of Toronto and a graduate student researcher at Vector Institute. I am honored to be advised by Prof. Daniel M. Roy.
I also work closely with Dr. Gintare Karolina Dziugaite. I received my B.Sc. and M.Sc. degrees in Electrical Engineering from Sharif University of Technology in 2014 and 2016 respectively.
Previously, I was a research intern at Element AI in Winter 2019 and Fall 2020. In early 2020, I was a visiting student at Institute of Advanced Study (IAS) for special-year program on Optimization, Statistics, and Theoretical Machine Learning.
I am Research Intern at Google Brain working with Dr. Thomas Steinke and Dr. Abhradeep Guha Thakurta for Summer 2022.
My Erdős number is 3!
Research Interests
My research focuses broadly on information theory and statistical learning theory. In particular, I have been working on several areas of Generalization Theory in Machine Learing with a focus on deriving provable guarantees for Machine Learing methods using information-theoretic tools. I am also interested in statistical inference and learning under privacy constraints. For a complete list of my publications, please visit the Publications page.
Selected Papers
M. Haghifam, S. Moran, D. M. Roy, G. K. Dziugaite, ‘‘Understanding Generalization via Leave-One-Out Conditional Mutual Information’’, ISIT 2022 [paper].
M. Haghifam, G. K. Dziugaite, S. Moran, D. M. Roy, ‘‘Towards a Unified Information–Theoretic Framework for Generalization’’, NeurIPS 2021 (Spotlight, <3% of submissions) [paper].
G. Neu, G. K. Dziugaite, M. Haghifam, D. M. Roy , ‘‘Information-Theoretic Generalization Bounds for Stochastic Gradient Descent’’, COLT 2021 [paper].
M. Haghifam, V. Y. F. Tan, A. Khisti, ‘‘Sequential Classification with Empirically Observed Statistics’’, IEEE Transactions on Information Theory [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’’, 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’’, NeurIPS 2019 [paper].