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        <title>papers:tr-bottou-2012</title>
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        <description>Counterfactual Reasoning and Learning Systems

Abstract:
This work shows how to leverage causal inference to understand the behavior of complex learning systems interacting with their environment and predict the consequences of changes to the system. Such predictions allow both humans and algorithms to select changes that improve both the short-term and long-term performance of such systems. This work is illustrated by experiments carried out on the ad placement system associated with the Bing s…</description>
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        <title>talks:counterfactuals - [Links] </title>
        <link>http://leon.bottou.org/talks/counterfactuals?rev=1368990501</link>
        <description>Variants of this talk were given in several occasions. The slides below were presented at the Machine Learning Summit 2013. See also lectures 9 and 11 in the NYU course on Big Data and Machine Learning.

Summary

Statistical machine learning technologies in the real world are never without a purpose. Using their predictions, humans or machines make decisions whose circuitous consequences often violate the modeling assumptions that justified the system design in the first place. Such contradictio…</description>
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        <title>talks</title>
        <link>http://leon.bottou.org/talks?rev=1368989343</link>
        <description>This page contains pointers to my most significant lectures.

All the slides are available under both the PDF and DjVu formats.

	*  The BackPropagation CookBook
	*  Graph Transformer Networks
	*  DjVu: Scanned Documents on the Web
	*  Online Learning and Stochastic Approximations
	*  The Tradeoffs of Large Scale Learning
	*  Stochastic Algorithms for One Pass Learning
	*  Counterfactual Reasoning and Learning Systems</description>
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        <title>wiki:playground</title>
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        <description>Foo</description>
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        <title>papers:bottou-fs-2013 - [See also] </title>
        <link>http://leon.bottou.org/papers/bottou-fs-2013?rev=1368961656</link>
        <description>In hindsight: Doklady Akademii Nauk SSSR, 181(4), 1968

Abstract:
This short contribution presents the first paper in which Vapnik and Chervonenkis
describe the foundations of the Statistical Learning Theory. The original paper
was published in the Doklady, the Proceedings of the USSR Academy of Sciences,
in 1968. An English translation was published the same year in Soviet Mathematics,
a journal from the American Mathematical Society publishing translations of the
mathematical section of the Do…</description>
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        <title>papers:bottou-mlj-2013</title>
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        <description>From machine learning to machine reasoning: an essay

This essay is an version of a 2011 tech report.

Abstract:
A plausible definition of “reasoning” could be “algebraically manipulating previously acquired knowledge in order to answer a new question”. This definition covers first-order logical inference or probabilistic inference. It also includes much simpler manipulations commonly used to build large learning systems. For instance, we can build an optical character recognition system by firs…</description>
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        <title>papers - [2012] </title>
        <link>http://leon.bottou.org/papers?rev=1368959456</link>
        <description>Follow each publication link to access papers and supplemental data.

Most papers are available in DjVu, PDF, and PS.GZ.

Download a DjVu viewer.

2013
Léon Bottou:  In hindsight: Doklady Akademii Nauk SSSR, 181(4), 1968,  Festschift for Vladimir Vapnik's 75th birthday, Springer, Springer.
more...</description>
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