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Found 10 papers, 0 papers with code
Derivative-Free & Order-Robust Optimisation
In this paper, we formalise order-robust optimisation as an instance of online learning minimising simple regret, and propose Vroom, a zero'th order optimisation algorithm capable of achieving vanishing regret in non-stationary environments, while recovering favorable rates under stochastic reward-generating processes.
MANAS: Multi-Agent Neural Architecture Search
The Neural Architecture Search (NAS) problem is typically formulated as a graph search problem where the goal is to learn the optimal operations over edges in order to maximise a graph-level global objective.
A simple parameter-free and adaptive approach to optimization under a minimal local smoothness assumption
The difficulty of optimization is measured in terms of 1) the amount of \emph{noise} $b$ of the function evaluation and 2) the local smoothness, $d$, of the function.
Near Minimax Optimal Players for the Finite-Time 3-Expert Prediction Problem
We study minimax strategies for the online prediction problem with expert advice.
Hit-and-Run for Sampling and Planning in Non-Convex Spaces
We propose the Hit-and-Run algorithm for planning and sampling problems in non-convex spaces.
Approximate Dynamic Programming Finally Performs Well in the Game of Tetris
A close look at the literature of this game shows that while ADP algorithms, that have been (almost) entirely based on approximating the value function (value function based), have performed poorly in Tetris, the methods that search directly in the space of policies by learning the policy parameters using an optimization black box, such as the cross entropy (CE) method, have achieved the best reported results.
Adaptive Submodular Maximization in Bandit Setting
Maximization of submodular functions has wide applications in machine learning and artificial intelligence.
Best Arm Identification: A Unified Approach to Fixed Budget and Fixed Confidence
We study the problem of identifying the best arm(s) in the stochastic multi-armed bandit setting.
Approximate Modified Policy Iteration
Modified policy iteration (MPI) is a dynamic programming (DP) algorithm that contains the two celebrated policy and value iteration methods.
Multi-Bandit Best Arm Identification
We first propose an algorithm called Gap-based Exploration (GapE) that focuses on the arms whose mean is close to the mean of the best arm in the same bandit (i. e., small gap).
