# Black-box optimization with a politician

arXiv: https://arxiv.org/abs/1602.04847 · HTML: https://mlsanity.com/paper/1602.04847/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2016.

## Built on by (most cited first, 1 of 1)

- [Confidence Sets under Generalized Self-Concordance](https://mlsanity.com/paper/2301.00260/) (2022)

## Abstract (arXiv)

We propose a new framework for black-box convex optimization which is well-suited for situations where gradient computations are expensive. We derive a new method for this framework which leverages several concepts from convex optimization, from standard first-order methods (e.g. gradient descent or quasi-Newton methods) to analytical centers (i.e. minimizers of self-concordant barriers). We demonstrate empirically that our new technique compares favorably with state of the art algorithms (such as BFGS).