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Found 5 papers, 3 papers with code
On the Oracle Complexity of Higher-Order Smooth Non-Convex Finite-Sum Optimization
We prove lower bounds for higher-order methods in smooth non-convex finite-sum optimization.
Iterative Refinement for $\ell_p$-norm Regression
We give improved algorithms for the $\ell_{p}$-regression problem, $\min_{x} \|x\|_{p}$ such that $A x=b,$ for all $p \in (1, 2) \cup (2,\infty).$ Our algorithms obtain a high accuracy solution in $\tilde{O}_{p}(m^{\frac{|p-2|}{2p + |p-2|}}) \le \tilde{O}_{p}(m^{\frac{1}{3}})$ iterations, where each iteration requires solving an $m \times m$ linear system, $m$ being the dimension of the ambient space.
Fast, Provable Algorithms for Isotonic Regression in all L_p-norms
Given a directed acyclic graph $G,$ and a set of values $y$ on the vertices, the Isotonic Regression of $y$ is a vector $x$ that respects the partial order described by $G,$ and minimizes $\|x-y\|,$ for a specified norm.
Fast, Provable Algorithms for Isotonic Regression in all $\ell_{p}$-norms
Given a directed acyclic graph $G,$ and a set of values $y$ on the vertices, the Isotonic Regression of $y$ is a vector $x$ that respects the partial order described by $G,$ and minimizes $||x-y||,$ for a specified norm.
Algorithms for Lipschitz Learning on Graphs
We develop fast algorithms for solving regression problems on graphs where one is given the value of a function at some vertices, and must find its smoothest possible extension to all vertices.
