Adam Klivans
Professor
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
Research Areas:
Research Interests:
- Learning Theory
- Computational Complexity
- Pseudorandomness
- Limit Theorems
- Gaussian Space
Select Publications
. 2020. Approximation Schemes for Relu Regression. COLT.
. 2020. Superpolynomial Lower Bounds for Learning One Layer Neural Networks Using Gradient Descent. ICML.
. 2019. List-Decodable Linear Regression. NeurIPS.(Spotlight).
. 2019. Time/Accuracy Tradeoffs for Learning a ReLU with Gaussian Marginals. NeurIPS.(Spotlight).
. 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