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Found 8 papers, 0 papers with code
Continuous Prediction with Experts' Advice
Finally, we design an anytime continuous-time algorithm with regret matching the optimal fixed-time rate when the gains are independent Brownian Motions; in many settings, this is the most difficult case.
Efficient and Optimal Fixed-Time Regret with Two Experts
In instances with $T$ rounds and $n$ experts, the classical Multiplicative Weights Update method suffers at most $\sqrt{(T/2)\ln n}$ regret when $T$ is known beforehand.
Regret Bounds without Lipschitz Continuity: Online Learning with Relative-Lipschitz Losses
We extend the known regret bounds for classical OCO algorithms to the relative setting.
Online mirror descent and dual averaging: keeping pace in the dynamic case
Online mirror descent (OMD) and dual averaging (DA) -- two fundamental algorithms for online convex optimization -- are known to have very similar (and sometimes identical) performance guarantees when used with a fixed learning rate.
Optimal anytime regret with two experts
In the fixed-time setting, where the time horizon is known in advance, algorithms that achieve the optimal regret are known when there are two, three, or four experts or when the number of experts is large.
Simple and optimal high-probability bounds for strongly-convex stochastic gradient descent
We consider a simple, non-uniform averaging strategy of Lacoste-Julien et al. (2011) and prove that it achieves the optimal $O(1/T)$ convergence rate with high probability.
Tight Analyses for Non-Smooth Stochastic Gradient Descent
We prove that after $T$ steps of stochastic gradient descent, the error of the final iterate is $O(\log(T)/T)$ with high probability.
The complexity of UNO
This paper investigates the popular card game UNO from the viewpoint of algorithmic combinatorial game theory.
Discrete Mathematics Computational Complexity G.2; F.1
