Adam Klivans

Adam Klivans is a recipient of the NSF Career Award. His research interests lie in machine learning and theoretical computer science, in particular, Learning Theory, Computational Complexity, Pseudorandomness, Limit Theorems, and Gaussian Space. He also serves on the editorial board for the Theory of Computing and Machine Learning Journal.


Research Interests: 
  • Learning Theory
  • Computational Complexity
  • Pseudorandomness
  • Limit Theorems
  • Gaussian Space

Select Publications

Ilias Diakonikolas, Surbhi Goel, Sushrut Karmalkar, Adam Klivans, Mahdi Soltanolkotabi. 2020. Approximation Schemes for Relu Regression. COLT.
Surbhi Goel, Aravind Gollakota, Zhihan Jin, Sushrut Karmalkar, Adam Klivans. 2020. Superpolynomial Lower Bounds for Learning One Layer Neural Networks Using Gradient Descent. ICML.
Sushrut Karmalkar, Pravesh Kothari, Adam Klivans. 2019. List-Decodable Linear Regression. NeurIPS. (Spotlight).
Surbhi Goel, Sushrut Karmalkar, Adam Klivans. 2019. Time/Accuracy Tradeoffs for Learning a ReLU with Gaussian Marginals. NeurIPS. (Spotlight).
Surbhi Goel, Adam Klivans. 2019. Learning Neural Networks with Two Nonlinear Layers in Polynomial-Time. COLT.

Awards & Honors

2019 - Member, IAS School of Mathematics
2019 - Two Spotlight Presentations, NeurIPS 2019
2018 - Long-Term Participant, Simons Institute Program on Foundations of Deep Learning
2017 - Microsoft Azure Data Science Initiative Award
2013 - College of Natural Sciences Teaching Excellence
2011 - Research Professorship, MSRI
2007 - NSF CAREER Award
2006 - Best Student Paper Award, COLT
2004 - NSF Mathematical Postdoctoral Research Fellowship
1997 - Andrew Carnegie Presidential Scholar