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Found 4 papers, 0 papers with code
Efficient Convex Optimization Requires Superlinear Memory
We show that any memory-constrained, first-order algorithm which minimizes $d$-dimensional, $1$-Lipschitz convex functions over the unit ball to $1/\mathrm{poly}(d)$ accuracy using at most $d^{1. 25 - \delta}$ bits of memory must make at least $\tilde{\Omega}(d^{1 + (4/3)\delta})$ first-order queries (for any constant $\delta \in [0, 1/4]$).
Big-Step-Little-Step: Efficient Gradient Methods for Objectives with Multiple Scales
We consider the problem of minimizing a function $f : \mathbb{R}^d \rightarrow \mathbb{R}$ which is implicitly decomposable as the sum of $m$ unknown non-interacting smooth, strongly convex functions and provide a method which solves this problem with a number of gradient evaluations that scales (up to logarithmic factors) as the product of the square-root of the condition numbers of the components.
On Misspecification in Prediction Problems and Robustness via Improper Learning
We study probabilistic prediction games when the underlying model is misspecified, investigating the consequences of predicting using an incorrect parametric model.
Sequential Matrix Completion
We propose a novel algorithm for sequential matrix completion in a recommender system setting, where the $(i, j)$th entry of the matrix corresponds to a user $i$'s rating of product $j$.
