21 citations · 101 across the 20 of their papers we have counts for
7 papers · 1 filter
Efficient and Optimal Fixed-Time Regret with Two Experts
Laura Greenstreet, Nicholas J. A. Harvey, Victor Sanches Portella
Prediction with expert advice is a foundational problem in online learning. In instances with rounds and experts, the classical Multiplicative Weights Update method suffers…
Regret Bounds without Lipschitz Continuity: Online Learning with Relative-Lipschitz Losses
Yihan Zhou, Victor S. Portella, Mark Schmidt +1
In online convex optimization (OCO), Lipschitz continuity of the functions is commonly assumed in order to obtain sublinear regret. Moreover, many algorithms have only logarithmic…
Online mirror descent and dual averaging: keeping pace in the dynamic case
Huang Fang, Nicholas J. A. Harvey, Victor S. Portella +1
Online mirror descent (OMD) and dual averaging (DA) -- two fundamental algorithms for online convex optimization -- are known to have very similar (and sometimes identical) perform…
Optimal anytime regret with two experts
Nicholas J. A. Harvey, Christopher Liaw, Edwin Perkins +1
We consider the classical problem of prediction with expert advice. In the fixed-time setting, where the time horizon is known in advance, algorithms that achieve the optimal regre…
Simple and optimal high-probability bounds for strongly-convex stochastic gradient descent
Nicholas J. A. Harvey, Christopher Liaw, Sikander Randhawa
We consider stochastic gradient descent algorithms for minimizing a non-smooth, strongly-convex function. Several forms of this algorithm, including suffix averaging, are known to…
Tight Analyses for Non-Smooth Stochastic Gradient Descent
Nicholas J. A. Harvey, Christopher Liaw, Yaniv Plan +1
Consider the problem of minimizing functions that are Lipschitz and strongly convex, but not necessarily differentiable. We prove that after steps of stochastic gradient descen…