Introduction to Efficient Algorithms And Lower Bounds For Robust Regression
Welcome to our comprehensive guide on Efficient Algorithms And Lower Bounds For Robust Regression. Efficient Algorithms and Lower Bounds for Robust Regression
Efficient Algorithms And Lower Bounds For Robust Regression Comprehensive Overview
Adam Klivans, Pravesh K Kothari and Raghu Meka Adam Klivans (University of Texas, Austin) https://simons.berkeley.edu/talks/ Jerry Li (Microsoft Research) https://simons.berkeley.edu/talks/tbd-350 Rigorous Evidence for Information-Computation Trade-offs.
CMU Theory lunch talk from April 24, 2019 by Jerry Li on Nearly Optimal
Summary & Highlights for Efficient Algorithms And Lower Bounds For Robust Regression
- Linear regression
- We study high-dimensional estimation in a setting where an adversary is allowed to arbitrarily corrupt an $\varepsilon$-fraction of ...
- This video discusses how least-squares
- Po-Ling Loh (University of Wisconsin, Madison) ...
- David Woodruff, IBM Almaden Information Theory, Learning and Big Data ...
In summary, understanding Efficient Algorithms And Lower Bounds For Robust Regression gives us a better perspective.
