David H. Wolpert

David H. Wolpert, Chiu Fan Lee, D. Wolpert, 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

Beyond Number of Bit Erasures: New Complexity Questions Raisedby Recently discovered thermodynamic costs of computation

Joshua A. Grochow, David H. Wolpert, D. Wolpert, 2018, SIGA.

Distributed Control with Collective Intelligence

Kagan Tumer, David H. Wolpert, Kevin R. Wheeler, 1998 .

Inference Concerning Physical Systems

David H. Wolpert, D. Wolpert, 2010, CiE.

Trembling hand perfection for mixed quantal/best response equilibria

David H. Wolpert, D. Wolpert, 2009, Int. J. Game Theory.

Using self-dissimilarity to quantify complexity: Research Articles

David H. Wolpert, William G. Macready, 2007 .

Bayesian Backpropagation Over I-O Functions Rather Than Weights

David H. Wolpert, D. Wolpert, 1993, NIPS.

A space–time tradeoff for implementing a function with master equation dynamics pdf

David H. Wolpert, Artemy Kolchinsky, Jeremy A. Owen, 2017, Nature Communications.

Adaptivity in agent-based routing for data networks

Kagan Tumer, David H. Wolpert, Sergey Kirshner, 1999, AGENTS '00.

Using Machine Learning to Improve Stochastic Optimization

David H. Wolpert, Dev G. Rajnarayan, D. Wolpert, 2013, AAAI.

Mathematics of Generalization: Proceedings: SFI-CNLS Workshop on Formal Approaches to Supervised Learning (1992: Santa Fe, N. M.)

David H. Wolpert, D. Wolpert, 1995 .

Using self-dissimilarity to quantify complexity

David H. Wolpert, William G. Macready, 2007, Complex..

Collective Intelligence and Braess' Paradox

Kagan Tumer, David H. Wolpert, D. Wolpert, 2000, AAAI/IAAI.

The Existence of A Priori Distinctions Between Learning Algorithms

David H. Wolpert, D. Wolpert, 1996, Neural Computation.

Why Income Comparison is Rational

David H. Wolpert, D. Wolpert, 2009, Games Econ. Behav..

Reinforcement Learning in Distributed Domains: Beyond Team Games

Kagan Tumer, David H. Wolpert, Joseph Sill, 2001, IJCAI.

Correction: Wolpert, D.H. The Free Energy Requirements of Biological Organisms; Implications for Evolution. Entropy 2016, 18, 138

David H. Wolpert, 2016, Entropy.

Filter likelihoods and exhaustive learning

David H. Wolpert, D. Wolpert, 1994, COLT 1994.

Using Supervised Learning to Improve Monte Carlo Integral Estimation pdf

David H. Wolpert, Juan J. Alonso, Brendan D. Tracey, 2011, ArXiv.

The Relationship Between Occam's Razor and Convergent Guessing

David H. Wolpert, D. Wolpert, 1990, Complex Syst..

A Survey of Collective Intelligence

Kagan Tumer, David H. Wolpert, D. Wolpert, 2013 .

Designing agent collectives for systems with markovian dynamics

David H. Wolpert, John W. Lawson, D. Wolpert, 2002, AAMAS '02.

Predicting what reinforcement learning will tell you: a model of human decision-making in multi-stage games

Russell Bent, Scott Backhaus, David H. Wolpert, 2011 .

No free lunch theorems for optimization

David H. Wolpert, William G. Macready, D. Wolpert, 1997, IEEE Trans. Evol. Comput..

Collective Intelligence

David H. Wolpert, D. Wolpert, 1999 .

Predicting Behavior in Unstructured Bargaining with a Probability Distribution

David H. Wolpert, James W. Bono, D. Wolpert, 2010, J. Artif. Intell. Res..

Towards a bayesian network game framework for evaluating DDoS attacks and defense

Guanhua Yan, Alex Kent, David H. Wolpert, 2012, CCS '12.

Probability Collectives for Optimization of Computer Simulations

Ilan Kroo, David H. Wolpert, Dev G. Rajnarayan, 2007 .

Bias-Variance Trade-offs: Novel Applications

David H. Wolpert, Dev G. Rajnarayan, D. Wolpert, 2008, Encyclopedia of Machine Learning.

Anytime Exploratory Data Analysis for Massive Data Sets

Padhraic Smyth, David H. Wolpert, 1997, KDD.

Bias Plus Variance Decomposition for Zero-One Loss Functions

Ron Kohavi, David H. Wolpert, D. Wolpert, 1996, ICML.

Stacked generalization

David H. Wolpert, D. Wolpert, 1992, Neural Networks.

Predicting the behavior of interacting humans by fusing data from multiple sources

Mykel J. Kochenderfer, David H. Wolpert, Erik J. Schlicht, 2012, UAI.

Beyond Mechanism Design

David H. Wolpert, Kagan Turner, 2004 .

A Survey of Collectives

Kagan Tumer, David H. Wolpert, D. Wolpert, 2004 .

Metrics for more than two points at once

David H. Wolpert, D. Wolpert, 2004, ArXiv.

Collective Intelligence, Data Routing and Braess' Paradox

Kagan Tumer, David H. Wolpert, D. Wolpert, 2002, J. Artif. Intell. Res..

What makes an optimization problem hard?

David H. Wolpert, William G. Macready, D. Wolpert, 1995, Complex..

Constraints on physical reality arising from a formalization of knowledge pdf

David H. Wolpert, 2017, ArXiv.

Providing Effective Access to Shared Resources: A COIN Approach

Sandip Sen, Kagan Tumer, David H. Wolpert, 2003, Engineering Self-Organising Systems.

Distributed control by Lagrangian steepest descent

David H. Wolpert, Stefan Bieniawski, Stefan R. Bieniawski, 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

What Does Newcomb's Paradox Teach Us?

David H. Wolpert, Gregory Benford, D. Wolpert, 2009, ArXiv.

The Mathematics of Generalization: The Proceedings of the SFI/CNLS Workshop on Formal Approaches to Supervised Learning

David H. Wolpert, D. Wolpert, 1994 .

Physical limits of inference pdf

David H. Wolpert, D. Wolpert, 2007, ArXiv.

Product Distribution Field Theory

David H. Wolpert, D. Wolpert, 2003, ArXiv.

Improving Search Algorithms by Using Intelligent Coordinates

Kagan Tumer, David H. Wolpert, Esfandiar Bandari, 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

Adaptive, distributed control of constrained multi-agent systems

David H. Wolpert, Stefan Bieniawski, Stefan R. Bieniawski, 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

Nonlinear Information Bottleneck

David H. Wolpert, Artemy Kolchinsky, Brendan D. Tracey, 2017, Entropy.

The Relationship Between PAC, the Statistical Physics Framework, the Bayesian Framework, and the VC Framework

David H. Wolpert, D. Wolpert, 1995 .

Information Theory - The Bridge Connecting Bounded Rational Game Theory and Statistical Physics

David H. Wolpert, D. Wolpert, 2004, ArXiv.

An Efficient Method To Estimate Bagging's Generalization Error

David H. Wolpert, William G. Macready, D. Wolpert, 1999, Machine Learning.

On the computational capabilities of physical systems part I: the impossibility of infallible computation

David H. Wolpert, D. Wolpert, 2000, ArXiv.

Modeling Social Organizations as Communication Networks pdf

David H. Wolpert, Justin Grana, Artemy Kolchinsky, 2017, ArXiv.

ON THE COMPUTATIONAL CAPABILITIES OF PHYSICAL SYSTEMS PART II: RELATIONSHIP WITH CONVENTIONAL COMPUTER SCIENCE

David H. Wolpert, 2000, ArXiv.

Novel physical constraints on implementation of computational processes

David H. Wolpert, Artemy Kolchinsky, 2017 .

Game Theoretic Modeling of Pilot Behavior During Mid-Air Encounters

David H. Wolpert, Ritchie Lee, D. Wolpert, 2011, ArXiv.

Some results concerning off-training-set and IID error for the Gibbs and the Bayes optimal generalizers

David H. Wolpert, Emanuel Knill, Tal Grossman, 1998, Stat. Comput..

Theories of Knowledge and Theories of Everything

David H. Wolpert, 2018 .

Number of hidden states needed to physically implement a given conditional distribution pdf

David H. Wolpert, Artemy Kolchinsky, Jeremy A. Owen, 2017, New Journal of Physics.

PGT: A Statistical Approach to Prediction and Mechanism Design

David H. Wolpert, James W. Bono, D. Wolpert, 2010, SBP.

General principles of learning-based multi-agent systems

Kagan Tumer, David H. Wolpert, Kevin R. Wheeler, 1999, AGENTS '99.

Collectives for the Optimal Combination of Imperfect Objects

Kagan Tumer, David H. Wolpert, D. Wolpert, 2003, ArXiv.

Hysteresis Effects of Changing Parameters of Noncooperative Games

Eckehard Olbrich, David H. Wolpert, Nils Bertschinger, 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

Remarks on a recent paper on the "no free lunch" theorems

David H. Wolpert, Mario Köppen, William G. Macready, 2001, IEEE Trans. Evol. Comput..

Coevolutionary free lunches

David H. Wolpert, William G. Macready, D. Wolpert, 2005, IEEE Transactions on Evolutionary Computation.

Improving Simulated Annealing by Recasting it as a Non-Cooperative Game

Kagan Tumer, David H. Wolpert, Esfandiar Bandari, 2001 .

On the Connection between In-sample Testing and Generalization Error

David H. Wolpert, D. Wolpert, 1992, Complex Syst..

Bayesian Optimization with a Finite Budget: An Approximate Dynamic Programming Approach

Karen Willcox, David H. Wolpert, Rémi Lam, 2016, NIPS.

Adaptive Programming of Unconventional Nano-Architectures

David H. Wolpert, John W. Lawson, D. Wolpert, 2006, cond-mat/0609489.

The Lack of A Priori Distinctions Between Learning Algorithms

David H. Wolpert, D. Wolpert, 1996, Neural Computation.

Optimal Wonderful Life Utility Functions in Multi-Agent Systems

Kagan Tumer, David H. Wolpert, Keith Swanson, 2000 .

On The Bayesian Occam Factors Argument For Occam's Razor

David H. Wolpert, 1992 .

Stacked Density Estimation

Padhraic Smyth, David H. Wolpert, Padhraic Smyth, 1997, NIPS.

A likelihood ratio anomaly detector for identifying within-perimeter computer network attacks

Russell Bent, David H. Wolpert, Joshua Neil, 2016, J. Netw. Comput. Appl..

A comparative study of probability collectives based multi-agent systems and genetic algorithms

David H. Wolpert, Stefan Bieniawski, Chien-Feng Huang, 2005, GECCO '05.

Advances in Distributed Optimization Using Probability Collectives

David H. Wolpert, Dev G. Rajnarayan, Charlie E. M. Strauss, 2006, Adv. Complex Syst..

Flight Control with Distributed Efiectors

David H. Wolpert, Stefan R. Bieniawski, Ilan Kroo, 2005 .

Bias-Variance Trade-offs: Novel Applications

David H. Wolpert, Dev G. Rajnarayan, 2010, Encyclopedia of Machine Learning.

Guest Editors' Introduction

Salvatore J. Stolfo, Philip K. Chan, David H. Wolpert, 2004, Machine Learning.

Bias-Variance Tradeoffs: Novel Applications

David H. Wolpert, Dev G. Rajnarayan, 2008 .

Distributed Constrained Optimization with Semicoordinate Transformations pdf

David H. Wolpert, William G. Macready, D. Wolpert, 2008, ArXiv.

The Past as a Stochastic Process pdf

David H. Wolpert, Peter F. Stadler, Hajime Shimao, 2021, Journal of Computer Applications in Archaeology.

Dependence of integrated, instantaneous, and fluctuating entropy production on the initial state in quantum and classical processes

David H. Wolpert, Artemy Kolchinsky, 2021, Physical Review E.

The state dependence of integrated, instantaneous, and fluctuating entropy production in quantum and classical processes pdf

David H. Wolpert, Artemy Kolchinsky, D. Wolpert, 2021, ArXiv.

The Implications of the No-Free-Lunch Theorems for Meta-induction pdf

David H. Wolpert, 2021, ArXiv.

Extending Landauer's Bound to Arbitrary Computation

David H. Wolpert, 2015 .

David H. Wolpert, William G. Macready, D. Wolpert, 1997 .

Scale and information-processing thresholds in Holocene social evolution

David H. Wolpert, Brendan D. Tracey, Hajime Shimao, 2020, Nature Communications.

Product Distributions for Distributed Optimization. Chapter 1

David H. Wolpert, Stefan Bieniawski, 2004 .

Minimal work required for arbitrary computation

David H. Wolpert, D. Wolpert, 2015 .

What Bayes has to Say about the Evidence Procedure

David H. Wolpert, Charlie E. M. Strauss, D. Wolpert, 1996 .

Optimization Under Uncertainty Using Probability Collectives

Ilan Kroo, David H. Wolpert, Dev G. Rajnarayan, 2006 .

Information width: a way for the second law to increase complexity

David H. Wolpert, Michael Ruse, Charles H. Lineweaver, 2013 .

An Incompleteness Theorem for Calculating the Future

David H. Wolpert, D. Wolpert, 1996 .

Estimating Functions of Distributions from A Finite Set of Samples, Part 2: Bayes Estimators for Mutual Information, Chi-Squared, Covariance and other Statistics

David H. Wolpert, David R. Wolf, D. R. Wolf, 1994, comp-gas/9403002.

Memory systems, computation, and the second law of thermodynamics

David H. Wolpert, D. Wolpert, 1992 .

Adaptive Metropolis Sampling with Product Distributions

David H. Wolpert, D. Wolpert, Chiu Fan Lee, 2005 .

Any Two Learning Algorithms Are (Almost) Exactly Identical

David H. Wolpert, 2000 .

The Mathematics of Search

David H. Wolpert, William G. Macready, D. Wolpert, 1996 .

DETERMINING WHETHER TWO DATA SETS ARE FROM THE SAME DISTRIBUTION

David H. Wolpert, 1996 .

What Information Theory Says About Best Response and About Binding Contracts

David H. Wolpert, D. Wolpert, 2004 .