# A Note on How to Remove the lnln T Term from the Squint Bound

arXiv: https://arxiv.org/abs/2604.26926 · HTML: https://mlsanity.com/paper/2604.26926/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 4 references that are in the map.
- First posted on arXiv 29 Apr 2026.

## Builds on (most cited first, 4 of 4)

- [Online Learning: A Modern Introduction Using Convex Optimization](https://mlsanity.com/paper/1912.13213/) (2019)
- [Coin Betting and Parameter-Free Online Learning](https://mlsanity.com/paper/1602.04128/) (2016)
- [Second-order Quantile Methods for Experts and Combinatorial Games](https://mlsanity.com/paper/1502.08009/) (2015)
- [Prediction with Advice of Unknown Number of Experts](https://mlsanity.com/paper/1408.2040/) (2014)

## Abstract (arXiv)

In Orabona and P'al [2016], we introduced the shifted KT potentials, to remove the ln ln T factor in the parameter-free learning with expert bound. In this short technical note, I show that this is equivalent to changing the prior in the Krichevsky--Trofimov algorithm. Then, I show how to use the same idea to remove the ln ln T factor in the data-independent bound for the Squint algorithm.