185 citations · 1.7k across the 78 of their papers we have counts for
6 papers · 1 filter
The Implications of the No-Free-Lunch Theorems for Meta-induction
David H. Wolpert
The important recent book by G. Schurz appreciates that the no-free-lunch theorems (NFL) have major implications for the problem of (meta) induction. Here I review the NFL theorems…
What is important about the No Free Lunch theorems?
David H. Wolpert
The No Free Lunch theorems prove that under a uniform distribution over induction problems (search problems or learning problems), all induction algorithms perform equally. As I di…
Parametric Learning and Monte Carlo Optimization
David H. Wolpert, Dev G. Rajnarayan
This paper uncovers and explores the close relationship between Monte Carlo Optimization of a parametrized integral (MCO), Parametric machine-Learning (PL), and `blackbox' or `orac…
An Introduction to Collective Intelligence
David H. Wolpert, Kagan Tumer
This paper surveys the emerging science of how to design a ``COllective INtelligence'' (COIN). A COIN is a large multi-agent system where: (i) There is little to no centralized com…
Collective Intelligence for Control of Distributed Dynamical Systems
David H. Wolpert, Kevin R. Wheeler, Kagan Tumer
We consider the El Farol bar problem, also known as the minority game (W. B. Arthur, ``The American Economic Review'', 84(2): 406--411 (1994), D. Challet and Y.C. Zhang, ``Physica…
Using Collective Intelligence to Route Internet Traffic
David H. Wolpert, Kagan Tumer, Jeremy Frank
A COllective INtelligence (COIN) is a set of interacting reinforcement learning (RL) algorithms designed in an automated fashion so that their collective behavior optimizes a globa…