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Ali Siahkamari

3 accepted papers

2022

Faster Algorithms for Learning Convex Functions

ICML 2022spotlight

The task of approximating an arbitrary convex function arises in several learning problems such as convex regression, learning with a difference of convex (DC) functions, and learning Bregman or $f$-divergences. In this paper, we develop and analyze an approach for solving a broad range of convex fu…

2020

Learning to Approximate a Bregman Divergence

NeurIPS 2020poster

Bregman divergences generalize measures such as the squared Euclidean distance and the KL divergence, and arise throughout many areas of machine learning. In this paper, we focus on the problem of approximating an arbitrary Bregman divergence from supervision, and we provide a well-principled appro…

2020

Piecewise Linear Regression via a Difference of Convex Functions

ICML 2020poster

We present a new piecewise linear regression methodology that utilises fitting a \emph{difference of convex} functions (DC functions) to the data. These are functions $f$ that may be represented as the difference $\phi_1 - \phi_2$ for a choice of \emph{convex} functions $\phi_1, \phi_2$. The method…