Search Results for author:
Found 4 papers, 2 papers with code
Faster Algorithms for Learning Convex Functions
For the task of convex Lipschitz regression, we establish that our proposed algorithm converges with iteration complexity of $ O(n\sqrt{d}/\epsilon)$ for a dataset $\bm X \in \mathbb R^{n\times d}$ and $\epsilon > 0$.
Piecewise Linear Regression via a Difference of Convex Functions
We present a new piecewise linear regression methodology that utilizes fitting a difference of convex functions (DC functions) to the data.
Learning to Approximate a Bregman Divergence
Bregman divergences generalize measures such as the squared Euclidean distance and the KL divergence, and arise throughout many areas of machine learning.
Conditioning Deep Generative Raw Audio Models for Structured Automatic Music
Existing automatic music generation approaches that feature deep learning can be broadly classified into two types: raw audio models and symbolic models.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
