David Wolpert
David H. Wolpert



Nationality  American 
Alma mater 
Princeton University
University of California, Santa Barbara 
Scientific career  
Fields 
Mathematics
Computer science 
Institutions  Santa Fe Institute 
Doctoral advisor  Anthony Zee 
David Hilton Wolpert is an American mathematician, physicist and computer scientist. He is a professor at Santa Fe Institute. He is the author of three books, three patents, over one hundred refereed papers, and has received numerous awards. His name is particularly associated with a group of theorems in computer science known as "no free lunch".
Career [ edit ]
David Wolpert took a B.A. in Physics at Princeton University (1984), then attended the University of California, Santa Barbara, where he took the degrees of M.A. (1987) and Ph.D. (1989).
Between 1989 and 1997 he pursued a research career at Los Alamos National Laboratory, IBM, TXN Inc. and Santa Fe Institute.
From 1997 to 2011 he worked as senior computer scientist at NASA Ames Research Center, and became visiting scholar at the Max Planck Institute. He spent the year 201011 as Ulam Scholar at the Center for Nonlinear Studies at Los Alamos.^{[1]}
He joined the faculty of Santa Fe Institute in 2011 and became a professor there in September 2013.^{[2]} His research interests have included statistics, game theory, machine learning applications, information theory, optimization methods and complex systems theory.
"No free lunch" [ edit ]
One of Wolpert’s most discussed achievements is known as No free lunch in search and optimization.^{[3]}^{[4]}^{[5]}^{[6]} By this theorem, all algorithms for search and optimization perform equally well averaged over all problems in the class with which they are designed to deal. The theorem holds only under certain conditions that are not often encountered precisely in real life,^{[7]}^{[8]}^{[9]} although it has been claimed that the conditions can be met approximately.^{[10]} The theorem lies within the domain of computer science, but a weaker version known as the “folkloric no free lunch theorem” has been drawn upon by William A. Dembski in support of intelligent design.^{[11]} This use of the theorem has been rejected by Wolpert himself^{[12]} and others.^{[13]}^{[14]}
Limitation on knowledge [ edit ]
Wolpert has put forward a formal argument to show that it is in principle impossible for any intellect to know everything about the universe of which it forms a part, in other words disproving "Laplace's demon".^{[15]} This has been seen as an extension of the limitative theorems of the twentieth century such as those of Heisenberg and Gödel.^{[16]} In 2018 Wolpert published a proof revealing the fundamental limits of scientific knowledge.^{[17]}
Machine learning [ edit ]
Wolpert made many contributions to the early work on machine learning. These include the first Bayesian estimator of the entropy of a distribution based on samples of the distribution,^{[18]}^{[19]} disproving formal claims that the "evidence procedure" is equivalent to hierarchical Bayes,^{[20]} a Bayesian alternative to the chisquared test,^{[21]} a proof that there is no prior for which the bootstrap procedure is Bayesoptimal,^{[22]} and Bayesian extensions of the biasplusvariance decomposition.^{[23]} Most prominently, he introduced "stacked generalization",^{[24]} a more sophisticated version of crossvalidation that uses heldin / heldout partitions of a data set to combine learning algorithms rather than just choose one of them. This work was developed further by Breiman, Smyth, Clarke and many others, and in particular the top two winners of 2009 Netflix competition made extensive use of stacked generalization (rebranded as "blending").^{[25]}
Academic memberships [ edit ]
 Fellow of IEEE
 Member of FQXi
 Research Associate of Infometrics Institute, American University
 Associate Editor (as of January 2017)
 Advances in Complex Systems
 IEEE Transactions on Evolutionary Computation
 ACM Transactions on Autonomous and Adaptive Systems
 Member of Editorial Board (as of January 2017)
 Journal of Artificial Intelligence Research
 Theory in Biosciences
 Journal of Economic Interaction and Coordination
 Reviews of Behavioral Economics
 Entropy
 Cancer Convergence
 Member on multiple NSF panels
Awards [ edit ]
 Princeton University Physics Department Kusaka Prize
 Best Paper Award for IEEE Transactions on Evolutionary Computation, Vols .1 & 2
 Superior Accomplishment Award for NASA Code IC for 1999
Publications (books only) [ edit ]
 Wolpert, D.H. (ed.), The Mathematics of Generalization, AddisonWesley, 1994. ISBN 0201409852
 Wolpert, D.H. An Incompleteness Theorem for Calculating the Future, SFI Economics Program, Santa Fe Institute, 1996.
 Tumer, K. and Wolpert, D.H. (ed.), Collectives And The Design Of Complex Systems, Springer, 2004. ISBN 0387401652
 Guy, T.V., Karny M., Wolpert D.H. (eds.), Decision making with imperfect decision makers, Springer, 2012. ISBN 3642246478
 Wolpert, D.H. Theory of Collective Intelligence, NASA Technical Reports Server, 2003. ISBN 1289283427
References [ edit ]
 ^ "CNLS Ulam Scholar". Archived from the original on 20141026. Retrieved 20140922.
 ^ David Wolpert, Santa Fe Institute
 ^ Wolpert, D.H., Macready, W.G. (1995), No Free Lunch Theorems for Search, Technical Report SFITR9502010 (Santa Fe Institute).
 ^ Wolpert D.H., Macready W.G. (1997). "No Free Lunch Theorems for Optimization" (PDF). IEEE Transactions on Evolutionary Computation. 1: 67. CiteSeerX 10.1.1.138.6606. doi:10.1109/4235.585893.
 ^ Wolpert, David (1996), The Lack of A Priori Distinctions between Learning Algorithms, Neural Computation, pp. 1341–1390.
 ^ David H. Wolpert, What the No Free Lunch Theorems Really Mean; How to Improve Search Algorithms, SFI Working Paper 201210017, Santa Fe Institute 2012
 ^ Streeter, M. (2003) Two Broad Classes of Functions for Which a No Free Lunch Result Does Not Hold, Genetic and Evolutionary Computation – GECCO 2003, pp. 1418–1430.
 ^ Igel C., Toussaint M. (2004). "A NoFreeLunch Theorem for NonUniform Distributions of Target Functions". Journal of Mathematical Modelling and Algorithms. 3 (4): 313–322. CiteSeerX 10.1.1.71.9744. doi:10.1023/b:jmma.0000049381.24625.f7.
 ^ English, T. (2004), No More Lunch: Analysis of Sequential Search, Proceedings of the 2004 IEEE Congress on Evolutionary Computation, pp. 227–234.
 ^ Droste S., Jansen T., Wegener I. (2002). "Optimization with randomized search heuristics: the (A)NFL theorem, realistic scenarios, and difficult functions". Theoretical Computer Science. 287 (1): 131–144. doi:10.1016/s03043975(02)000944. hdl:2003/5394. CS1 maint: multiple names: authors list (link)
 ^ Dembski, W. A. (2002) No Free Lunch, Rowman & Littlefield, ISBN 0742512975
 ^ Wolpert, D. (2003), William Dembski's treatment of the No Free Lunch theorems is written in jello, Talk Reason
 ^ Perakh, M. (2003), The No Free Lunch Theorems and Their Application to Evolutionary Algorithms, Talk Reason.
 ^ Richard Wein (2002), Not a Free Lunch But a Box of Chocolates (Sect. 5.3), The TalkOrigins Archive
 ^ David H. Wolpert (2008). "Physical limits of inference". Physica D. 237 (9): 1257–1281. arXiv:0708.1362. Bibcode:2008PhyD..237.1257W. doi:10.1016/j.physd.2008.03.040. full text
 ^ Graham P. Collins, Within Any Possible Universe, No Intellect Can Ever Know It All, Scientific American, 16 February 2009
 ^ "New proof reveals fundamental limits of scientific knowledge". Retrieved 20181004.
 ^ David H. Wolpert and David Wolf (1995). "Estimating Functions of Probability Distributions from a Finite Set of Samples". Physical Review E. 52 (6): 6841–6854. Bibcode:1995PhRvE..52.6841W. CiteSeerX 10.1.1.55.7122. doi:10.1103/physreve.52.6841. PMID 9964199.
 ^ David H. Wolpert and Simon DeDeo (2013). "Estimating Functions of Distributions Defined over Spaces of Unknown Size". Entropy. 15 (12): 4668–4699. arXiv:1311.4548. Bibcode:2013Entrp..15.4668W. doi:10.3390/e15114668.
 ^ David H. Wolpert and Charles E. Strauss (1996). "What Bayes has to say about the evidence procedure". Maximum Entropy and Bayesian Methods 1993.
 ^ David H. Wolpert (1996). "Determining Whether Two Data Sets are from the Same Distribution". Maximum Entropy and Bayesian Methods 1995.
 ^ David H. Wolpert (1996). "The Bootstrap is Inconsistent with Probability Theory". Maximum Entropy and Bayesian Methods 1995.
 ^ David H. Wolpert (1997). "On Bias plus Variance". Neural Computation. 9 (6): 1211–1243. doi:10.1162/neco.1997.9.6.1211.
 ^ David H. Wolpert (1992). "Stacked Generalization". Neural Networks. 5 (2): 241–259. CiteSeerX 10.1.1.133.8090. doi:10.1016/s08936080(05)800231.
 ^ Joseph Sill; et al. (2008). "FeatureWeighted Linear Stacking". Physica D: Nonlinear Phenomena. 237 (9): 1257–1281. arXiv:0708.1362. Bibcode:2008PhyD..237.1257W. doi:10.1016/j.physd.2008.03.040.