8 citations · 18 across the 4 of their papers we have counts for
4 papers
Faster Algorithms for Learning Convex Functions
Ali Siahkamari, Durmus Alp Emre Acar, Christopher Liao +3
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 learn…
Piecewise Linear Regression via a Difference of Convex Functions
Ali Siahkamari, Aditya Gangrade, Brian Kulis +1
We present a new piecewise linear regression methodology that utilizes fitting a difference of convex functions (DC functions) to the data. These are functions that may be repr…
Learning to Approximate a Bregman Divergence
Ali Siahkamari, Xide Xia, Venkatesh Saligrama +2
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 o…
Conditioning Deep Generative Raw Audio Models for Structured Automatic Music
Rachel Manzelli, Vijay Thakkar, Ali Siahkamari +1
Existing automatic music generation approaches that feature deep learning can be broadly classified into two types: raw audio models and symbolic models. Symbolic models, which tra…