Low-rank matrix approximations, such as the truncated singular value decomposition and
the rank-revealing QR decomposition, play a central role in data analysis and scientific …

Over the last few years, the Shapley value, a solution concept from cooperative game theory,
has found numerous applications in machine learning. In this paper, we first discuss …

This survey highlights the recent advances in algorithms for numerical linear algebra that
have come from the technique of linear sketching, whereby given a matrix, one first …

Abstract {\em “How much is my data worth?”} is an increasingly common question posed by
organizations and individuals alike. An answer to this question could allow, for instance …

We design a new distribution over m× n matrices S so that, for any fixed n× d matrix A of rank
r, with probability at least 9/10,∥ SAx∥ 2=(1±ε)∥ Ax∥ 2 simultaneously for all x∈ R d …

Low-rank matrix approximations, such as the truncated singular value decomposition and
the rank-revealing QR decomposition, play a central role in data analysis and scientific …

Given a set F of n positive functions over a ground set X, we consider the problem of
computing x* that minimizes the expression∑ f∈ Ff (x), over x∈ X. A typical application is …

An oblivious subspace embedding (OSE) given some parameters ε, d is a distribution D over
matrices Π∈ R m× n such that for any linear subspace W⊆ R n with dim (W)= d, P Π~ D (∀ …

This article presents a general sampling method, called granular-ball sampling (GBS), for
classification problems by introducing the idea of granular computing. The GBS method …

Many convex problems in machine learning and computer science share the same
form:\begin {align*}\min_ {x}\sum_ {i} f_i (A_i x+ b_i),\end {align*} where $ f_i $ are convex …