no code implementations • 26 Jan 2024 • Parikshit Gopalan, Princewill Okoroafor, Prasad Raghavendra, Abhishek Shetty, Mihir Singhal
An \textit{omnipredictor} for a class $\mathcal L$ of loss functions and a class $\mathcal C$ of hypotheses is a predictor whose predictions incur less expected loss than the best hypothesis in $\mathcal C$ for every loss in $\mathcal L$.
no code implementations • 26 Jan 2021 • SiQi Liu, Sidhanth Mohanty, Prasad Raghavendra
For instance, in a planted constraint satisfaction problem such as planted 3-SAT, the clauses are sparse observations from which the hidden assignment is to be recovered.
Community Detection Data Structures and Algorithms Probability
no code implementations • 30 Jul 2018 • Prasad Raghavendra, Tselil Schramm, David Steurer
On one hand, there is a growing body of work utilizing sum-of-squares proofs for recovering solutions to polynomial systems when the system is feasible.
no code implementations • 10 Feb 2014 • Moritz Hardt, Raghu Meka, Prasad Raghavendra, Benjamin Weitz
Matrix Completion is the problem of recovering an unknown real-valued low-rank matrix from a subsample of its entries.