1 code implementation • 19 Jul 2023 • Tony Tu, Gautham Krishna, Amirali Aghazadeh
Prokaryotic gene prediction plays an important role in understanding the biology of organisms and their function with applications in medicine and biotechnology.
1 code implementation • 15 Jan 2023 • Yigit Efe Erginbas, Justin Singh Kang, Amirali Aghazadeh, Kannan Ramchandran
Fourier transformations of pseudo-Boolean functions are popular tools for analyzing functions of binary sequences.
no code implementations • 5 Oct 2022 • Amirali Aghazadeh, Nived Rajaraman, Tony Tu, Kannan Ramchandran
Data-driven machine learning models are being increasingly employed in several important inference problems in biology, chemistry, and physics which require learning over combinatorial spaces.
no code implementations • 18 Jun 2021 • Farzan Farnia, Amirali Aghazadeh, James Zou, David Tse
Robust training methods against perturbations to the input data have received great attention in the machine learning literature.
1 code implementation • 26 Oct 2020 • Amirali Aghazadeh, Vipul Gupta, Alex DeWeese, O. Ozan Koyluoglu, Kannan Ramchandran
We consider feature selection for applications in machine learning where the dimensionality of the data is so large that it exceeds the working memory of the (local) computing machine.
1 code implementation • ICML 2018 • Amirali Aghazadeh, Ryan Spring, Daniel LeJeune, Gautam Dasarathy, Anshumali Shrivastava, baraniuk
We demonstrate that MISSION accurately and efficiently performs feature selection on real-world, large-scale datasets with billions of dimensions.
1 code implementation • 12 Jun 2018 • Amirali Aghazadeh, Ryan Spring, Daniel LeJeune, Gautam Dasarathy, Anshumali Shrivastava, Richard G. Baraniuk
We demonstrate that MISSION accurately and efficiently performs feature selection on real-world, large-scale datasets with billions of dimensions.