106 citations · 525 across the 44 of their papers we have counts for
Showing 2017Show all
3 papers · 1 filter
stat.ML2017★ 3 cited
Online Learning for Changing Environments using Coin Betting
Kwang-Sung Jun, Francesco Orabona, Stephen Wright +1
A key challenge in online learning is that classical algorithms can be slow to adapt to changing environments. Recent studies have proposed "meta" algorithms that convert any onlin…
cs.LG2017★ 7 cited
Training Deep Networks without Learning Rates Through Coin Betting
Francesco Orabona, Tatiana Tommasi
Deep learning methods achieve state-of-the-art performance in many application scenarios. Yet, these methods require a significant amount of hyperparameters tuning in order to achi…
cs.LG2017★ 1 cited
Efficient Online Bandit Multiclass Learning with Regret
Alina Beygelzimer, Francesco Orabona, Chicheng Zhang
We present an efficient second-order algorithm with regret for the bandit online multiclass problem. The regret bound holds simultaneously with respe…