Sitemap
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
Future Blog Post
Published:
This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.
Blog Post number 4
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
Short description of portfolio item number 2
publications
On the consistency of graph-based Bayesian semi-supervised learning and the scalability of sampling algorithms
Published in Journal of Machine Learning Research, 2020
This paper presents interesting theoretical properties of a Bayesian approach to semi-supervised learning on graphs.
Scalable Projection-Free Optimization Methods via MultiRadial Duality Theory
Published in ArXiv Preprint, 2024
This paper presents the multiradial method for solving constrained convex optimization problems. This method assumes minimal structure on the objective function and does not rely on projections to the feasible region.
The Optimal Smoothings of Sublinear Functions and Convex Cones
Published in ArXiv Preprint, 2025
This paper provides smooth, convex functions (or sets) of fixed curvature that minimize the distance to the original function (or set).
An Elementary Proof of the Near Optimality of LogSumExp Smoothing
Published in ArXiv Preprint, 2025
The paper proves a sharp limitation on smoothing the max-of-coordinates function in \(d\) dimensions. Any convex surrogate with the desired smoothness must incur a worst-case error that grows like \(\log d\), so the standard LogSumExp smoothing is essentially optimal up to constants. This result is proved using elementary inequalities about smooth, convex functions. The paper also proves that in small dimensions (\(d = 2, 3\)) LogSumExp fails to be optimal.
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.