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Found 4 papers, 0 papers with code
Omnipredictors for Regression and the Approximate Rank of Convex Functions
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 $\mathcal L$.
On statistical inference when fixed points of belief propagation are unstable
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
High-dimensional estimation via sum-of-squares proofs
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.
Computational Limits for Matrix Completion
Matrix Completion is the problem of recovering an unknown real-valued low-rank matrix from a subsample of its entries.
