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Guy Kornowski

I'm a Ph.D. student at the Department of Computer Science & Applied Mathematics, Weizmann Institute of Science, where I'm very fortunate to be advised by Prof. Ohad Shamir. I am an Azrieli graduate fellow.

My main research interests are machine learning theory, optimization, statistical learning and deep learning theory.

I completed my M.Sc. at Weizmann as well, for which I received the Dean's prize for outstanding M.Sc. student.

Before that I completed by B.Sc. in mathematics at Tel Aviv University. 

In somewhat of a past life, I studied guitar at the Center for Jazz Studies in the Israel Conservatory of Music, was supported by the America-Israel Cultural Foundation and used to play in several musical groups. I'm still enthusiastic about music.

Publications

Guy Kornowski, Ohad Shamir

COLT 2024 open problem. [arxiv]

Guy Kornowski, Swati Padmanabhan, Kai Wang, Zhe Zhang, Suvrit Sra

arxiv preprint. [arxiv]

Steve Hanneke, Aryeh Kontorovich, Guy Kornowski

Algorithmic Learning Theory (ALT) 2024. [arxiv]

Guy Kornowski, Ohad Shamir 

NeurIPS Workshop on Optimization for Machine Learning 2023, oral presentation.

Journal of Machine Learning Research (JMLR) 2024.  [arxiv] [JMLR]

Guy Kornowski*, Gilad Yehudai*, Ohad Shamir 

Neural Information Processing Systems (NeurIPS) 2023, spotlight. [arxiv]

Steve Hanneke, Aryeh Kontorovich, Guy Kornowski

Neural Information Processing Systems (NeurIPS) 2023. [arxiv]

Michael I. Jordan, Guy Kornowski, Tianyi Lin, Ohad Shamir, Manolis Zampetakis

Conference on Learning Theory (COLT) 2023. [arxiv]

Guy Kornowski, Ohad Shamir

NeurIPS Workshop on Optimization for Machine Learning 2022, oral presentation. [arxiv]

Guy Kornowski, Ohad Shamir

Neural Information Processing Systems (NeurIPS) 2021, oral presentation.

Journal of Machine Learning Research (JMLR) 2022.  [arxiv] [JMLR]

Guy Kornowski, Ohad Shamir

arxiv technical note, 2021. [arxiv]

Since 2021, I volunteer as a machine learning mentor at Magshimim (site in Hebrew). Magshimim is a national education program that provides computer science education to underserved youth, equipping them with knowledge and tools for a future career in tech.