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Found 7 papers, 1 papers with code
A simpler approach to accelerated optimization: iterative averaging meets optimism
In this paper, we show that there is a simpler approach to obtaining accelerated rates: applying generic, well-known optimistic online learning algorithms and using the online average of their predictions to query the (deterministic or stochastic) first-order optimization oracle at each time step.
Adapting to Delays and Data in Adversarial Multi-Armed Bandits
We consider the adversarial multi-armed bandit problem under delayed feedback.
Adaptive Approximate Policy Iteration
This is an improvement over the best existing bound of $\tilde{O}(T^{3/4})$ for the average-reward case with function approximation.
Think out of the "Box": Generically-Constrained Asynchronous Composite Optimization and Hedging
ASYNCADA is, to our knowledge, the first asynchronous stochastic optimization algorithm with finite-time data-dependent convergence guarantees for generic convex constraints.
A Modular Analysis of Adaptive (Non-)Convex Optimization: Optimism, Composite Objectives, and Variational Bounds
Recently, much work has been done on extending the scope of online learning and incremental stochastic optimization algorithms.
Fast Cross-Validation for Incremental Learning
Cross-validation (CV) is one of the main tools for performance estimation and parameter tuning in machine learning.
Online Learning under Delayed Feedback
Online learning with delayed feedback has received increasing attention recently due to its several applications in distributed, web-based learning problems.
