Kei Takeuchi

Profiles.Takeuchi History

Hide minor edits - Show changes to output

May 15, 2017, at 09:01 AM by Vovk - Akimichi's corrections
Changed line 6 from:
* 1981        Graduated from University of Tokyo graduate school
to:
* 1961        Graduated from the University of Tokyo graduate school
Changed line 10 from:
* 1994 - 2008  Professor, Faculty of International Studies, Meiji Gakuin University
to:
* 1994 - 2006  Professor, Faculty of International Studies, Meiji Gakuin University
Changed lines 23-25 from:
In 1979 Kei Takeuchi wrote a handout (dated July 17 1979) for a seminar at Stanford university in which he anticipated some features of conformal prediction.  Developing the notion of an $1-\alpha$ expectation tolerance region, he posed the problem of finding the smallest (with respect to the Lebesgue measure) such region that would be valid under the [[Main/randomness assumption]].  Invoking the standard completeness result for the order statistics, he replaced the [[Main/randomness assumption]] by the [[Main/exchangeable probability distribution | exchangeability assumption]].  Assuming that the observations are generated from a probability distribution with density $f_0$, he showed that the optimal solution is the [[Main/conformal predictor]] assigning the conformity score $f_0(z_i)$ to an observation $z_i$.  This is a special case of [[Main/conformal prediction]] not involving learning, but he also developed a more general version that does require some learning (namely, parameter estimation).  If the true distribution is only supposed to belong to a parametric statistical model, he suggested using the plug-in density for computing the conformity scores $f_0(z_i\mid\hat\theta)$.  To preserve validity, $\hat\theta$ was to be computed from the extended training sequence with the test object assigned a postulated label $y$, making Prof. Takeuchi's procedure an instance of [[Main/conformal prediction]].

[[Attach:790717-takeuchi-memo.pdf]]
to:
In 1979 Kei Takeuchi wrote a handout (dated July 17 1979, [[(Attach:)790717-takeuchi-memo.pdf]]) for a seminar at Stanford university in which he anticipated some features of conformal prediction.  Developing the notion of an $1-\alpha$ expectation tolerance region, he posed the problem of finding the smallest (with respect to the Lebesgue measure) such region that would be valid under the [[Main/randomness assumption]].  Invoking the standard completeness result for the order statistics, he replaced the [[Main/randomness assumption]] by the [[Main/exchangeable probability distribution | exchangeability assumption]].  Assuming that the observations are generated from a probability distribution with density $f_0$, he showed that the optimal solution is the [[Main/conformal predictor]] assigning the conformity score $f_0(z_i)$ to an observation $z_i$.  This is a special case of [[Main/conformal prediction]] not involving learning, but he also developed a more general version that does require some learning (namely, parameter estimation).  If the true distribution is only supposed to belong to a parametric statistical model, he suggested using the plug-in density for computing the conformity scores $f_0(z_i\mid\hat\theta)$.  To preserve validity, $\hat\theta$ was to be computed from the extended training sequence with the test object assigned a postulated label $y$, making Prof. Takeuchi's procedure an instance of [[Main/conformal prediction]].
May 14, 2017, at 04:31 PM by Vovk - attached Takeuchi's notes
Changed lines 23-25 from:
In 1979 Kei Takeuchi wrote a handout (dated July 17 1979) for a seminar at Stanford university in which he anticipated some features of conformal prediction.  Developing the notion of an $1-\alpha$ expectation tolerance region, he posed the problem of finding the smallest (with respect to the Lebesgue measure) such region that would be valid under the [[Main/randomness assumption]].  Invoking the standard completeness result for the order statistics, he replaced the [[Main/randomness assumption]] by the [[Main/exchangeable probability distribution | exchangeability assumption]].  Assuming that the observations are generated from a probability distribution with density $f_0$, he showed that the optimal solution is the [[Main/conformal predictor]] assigning the conformity score $f_0(z_i)$ to an observation $z_i$.  This is a special case of [[Main/conformal prediction]] not involving learning, but he also developed a more general version that does require some learning (namely, parameter estimation).  If the true distribution is only supposed to belong to a parametric statistical model, he suggested using the plug-in density for computing the conformity scores $f_0(z_i\mid\hat\theta)$.  To preserve validity, $\hat\theta$ was to be computed from the extended training sequence with the test object assigned a postulated label $y$, making Prof. Takeuchi's procedure an instance of [[Main/conformal prediction]].
to:
In 1979 Kei Takeuchi wrote a handout (dated July 17 1979) for a seminar at Stanford university in which he anticipated some features of conformal prediction.  Developing the notion of an $1-\alpha$ expectation tolerance region, he posed the problem of finding the smallest (with respect to the Lebesgue measure) such region that would be valid under the [[Main/randomness assumption]].  Invoking the standard completeness result for the order statistics, he replaced the [[Main/randomness assumption]] by the [[Main/exchangeable probability distribution | exchangeability assumption]].  Assuming that the observations are generated from a probability distribution with density $f_0$, he showed that the optimal solution is the [[Main/conformal predictor]] assigning the conformity score $f_0(z_i)$ to an observation $z_i$.  This is a special case of [[Main/conformal prediction]] not involving learning, but he also developed a more general version that does require some learning (namely, parameter estimation).  If the true distribution is only supposed to belong to a parametric statistical model, he suggested using the plug-in density for computing the conformity scores $f_0(z_i\mid\hat\theta)$.  To preserve validity, $\hat\theta$ was to be computed from the extended training sequence with the test object assigned a postulated label $y$, making Prof. Takeuchi's procedure an instance of [[Main/conformal prediction]].

[[Attach:790717-takeuchi-memo.pdf]]
Changed line 23 from:
In 1979 Kei Takeuchi wrote a handout (dated July 17 1979) in which he anticipated some features of conformal prediction.  Developing the notion of an $1-\alpha$ expectation tolerance region, he posed the problem of finding the smallest (with respect to the Lebesgue measure) such region that would be valid under the [[Main/randomness assumption]].  Invoking the standard completeness result for the order statistics, he replaced the [[Main/randomness assumption]] by the [[Main/exchangeable probability distribution | exchangeability assumption]].  Assuming that the observations are generated from a probability distribution with density $f_0$, he showed that the optimal solution is the [[Main/conformal predictor]] assigning the conformity score $f_0(z_i)$ to an observation $z_i$.  This is a special case of [[Main/conformal prediction]] not involving learning, but he also developed a more general version that does require some learning (namely, parameter estimation).  If the true distribution is only supposed to belong to a parametric statistical model, he suggested using the plug-in density for computing the conformity scores $f_0(z_i\mid\hat\theta)$.  To preserve validity, $\hat\theta$ was to be computed from the extended training sequence with the test object assigned a postulated label $y$, making Prof. Takeuchi's procedure an instance of [[Main/conformal prediction]].
to:
In 1979 Kei Takeuchi wrote a handout (dated July 17 1979) for a seminar at Stanford university in which he anticipated some features of conformal prediction.  Developing the notion of an $1-\alpha$ expectation tolerance region, he posed the problem of finding the smallest (with respect to the Lebesgue measure) such region that would be valid under the [[Main/randomness assumption]].  Invoking the standard completeness result for the order statistics, he replaced the [[Main/randomness assumption]] by the [[Main/exchangeable probability distribution | exchangeability assumption]].  Assuming that the observations are generated from a probability distribution with density $f_0$, he showed that the optimal solution is the [[Main/conformal predictor]] assigning the conformity score $f_0(z_i)$ to an observation $z_i$.  This is a special case of [[Main/conformal prediction]] not involving learning, but he also developed a more general version that does require some learning (namely, parameter estimation).  If the true distribution is only supposed to belong to a parametric statistical model, he suggested using the plug-in density for computing the conformity scores $f_0(z_i\mid\hat\theta)$.  To preserve validity, $\hat\theta$ was to be computed from the extended training sequence with the test object assigned a postulated label $y$, making Prof. Takeuchi's procedure an instance of [[Main/conformal prediction]].
Changed line 23 from:
In 1977 Kei Takeuchi wrote a handout (dated July 17 1979) in which he anticipated some features of conformal prediction.  Developing the notion of an $1-\alpha$ expectation tolerance region, he posed the problem of finding the smallest (with respect to the Lebesgue measure) such region that would be valid under the [[Main/randomness assumption]].  Invoking the standard completeness result for the order statistics, he replaced the [[Main/randomness assumption]] by the [[Main/exchangeable probability distribution | exchangeability assumption]].  Assuming that the observations are generated from a probability distribution with density $f_0$, he showed that the optimal solution is the [[Main/conformal predictor]] assigning the conformity score $f_0(z_i)$ to an observation $z_i$.  This is a special case of [[Main/conformal prediction]] not involving learning, but he also developed a more general version that does require some learning (namely, parameter estimation).  If the true distribution is only supposed to belong to a parametric statistical model, he suggested using the plug-in density for computing the conformity scores $f_0(z_i\mid\hat\theta)$.  To preserve validity, $\hat\theta$ was to be computed from the extended training sequence with the test object assigned a postulated label $y$, making Prof. Takeuchi's procedure an instance of [[Main/conformal prediction]].
to:
In 1979 Kei Takeuchi wrote a handout (dated July 17 1979) in which he anticipated some features of conformal prediction.  Developing the notion of an $1-\alpha$ expectation tolerance region, he posed the problem of finding the smallest (with respect to the Lebesgue measure) such region that would be valid under the [[Main/randomness assumption]].  Invoking the standard completeness result for the order statistics, he replaced the [[Main/randomness assumption]] by the [[Main/exchangeable probability distribution | exchangeability assumption]].  Assuming that the observations are generated from a probability distribution with density $f_0$, he showed that the optimal solution is the [[Main/conformal predictor]] assigning the conformity score $f_0(z_i)$ to an observation $z_i$.  This is a special case of [[Main/conformal prediction]] not involving learning, but he also developed a more general version that does require some learning (namely, parameter estimation).  If the true distribution is only supposed to belong to a parametric statistical model, he suggested using the plug-in density for computing the conformity scores $f_0(z_i\mid\hat\theta)$.  To preserve validity, $\hat\theta$ was to be computed from the extended training sequence with the test object assigned a postulated label $y$, making Prof. Takeuchi's procedure an instance of [[Main/conformal prediction]].
Changed line 23 from:
In 1977 Kei Takeuchi wrote a handout (dated July 17 1979) in which he anticipated some features of conformal prediction.  Developing the notion of an $1-\alpha$ expectation tolerance region, he posed the problem of finding the smallest (with respect to the Lebesgue measure) such region that would be valid under the [[Main/randomness assumption]].  Invoking the standard completeness result for the order statistics, he replaced the [[Main/randomness assumption]] by the [[Main/exchangeable probability distribution | exchangeability assumption]].  Assuming that the observations are generated from a probability distribution with density $f_0$, he showed that the optimal solution is the [[Main/conformal predictor]] assigning the conformity score $f_0(z_i)$ to an observation $z_i$.  This is a special case of [[Main/conformal prediction]] not involving learning, but he also developed a more general version that does require some learning (namely, parameter estimation).  If the true distribution is only supposed to belong to a parametric statistical model, he suggested using the plug-in density for computing the conformity scores $f_0(z_i\mid\hat\theta)$.  To preserve validity $\theta$ was to be computed from the extended training sequence with the test object assigned a postulated label $y$, making Prof. Takeuchi's procedure an instance of conformal prediction.
to:
In 1977 Kei Takeuchi wrote a handout (dated July 17 1979) in which he anticipated some features of conformal prediction.  Developing the notion of an $1-\alpha$ expectation tolerance region, he posed the problem of finding the smallest (with respect to the Lebesgue measure) such region that would be valid under the [[Main/randomness assumption]].  Invoking the standard completeness result for the order statistics, he replaced the [[Main/randomness assumption]] by the [[Main/exchangeable probability distribution | exchangeability assumption]].  Assuming that the observations are generated from a probability distribution with density $f_0$, he showed that the optimal solution is the [[Main/conformal predictor]] assigning the conformity score $f_0(z_i)$ to an observation $z_i$.  This is a special case of [[Main/conformal prediction]] not involving learning, but he also developed a more general version that does require some learning (namely, parameter estimation).  If the true distribution is only supposed to belong to a parametric statistical model, he suggested using the plug-in density for computing the conformity scores $f_0(z_i\mid\hat\theta)$.  To preserve validity, $\hat\theta$ was to be computed from the extended training sequence with the test object assigned a postulated label $y$, making Prof. Takeuchi's procedure an instance of [[Main/conformal prediction]].
Changed line 23 from:
In 1977 Kei Takeuchi wrote a handout (dated July 17 1979) in which he anticipated some features of conformal prediction.  Developing the notion of an $1-\alpha$ expectation tolerance region, he posed the problem of finding the smallest (with respect to the Lebesgue measure) such region that would be valid under the [[Main/randomness assumption]].  Invoking the standard completeness result for the order statistics, he replaced the [[Main/randomness assumption]] by the [[Main/exchangeable probability distribution | exchangeability assumption]].  Assuming that the observations are generated from a probability distribution with density $f_0$, he showed that the optimal solution is the [[Main/conformal predictor]] assigning the conformity score $f_0(z_i)$ to an observation $z_i$.  This is a special case of [[Main/conformal prediction]] not involving learning, but he developed a more general version that does require some learning (namely, parameter estimation).  If the true distribution is only supposed to belong to a parametric statistical model, he suggested using the plug-in density for computing the conformity scores $f_0(z_i\mid\hat\theta)$.  To preserve validity $\theta$ was to be computed from the extended training sequence with the test object assigned a postulated label $y$, making Prof. Takeuchi's procedure an instance of conformal prediction.
to:
In 1977 Kei Takeuchi wrote a handout (dated July 17 1979) in which he anticipated some features of conformal prediction.  Developing the notion of an $1-\alpha$ expectation tolerance region, he posed the problem of finding the smallest (with respect to the Lebesgue measure) such region that would be valid under the [[Main/randomness assumption]].  Invoking the standard completeness result for the order statistics, he replaced the [[Main/randomness assumption]] by the [[Main/exchangeable probability distribution | exchangeability assumption]].  Assuming that the observations are generated from a probability distribution with density $f_0$, he showed that the optimal solution is the [[Main/conformal predictor]] assigning the conformity score $f_0(z_i)$ to an observation $z_i$.  This is a special case of [[Main/conformal prediction]] not involving learning, but he also developed a more general version that does require some learning (namely, parameter estimation).  If the true distribution is only supposed to belong to a parametric statistical model, he suggested using the plug-in density for computing the conformity scores $f_0(z_i\mid\hat\theta)$.  To preserve validity $\theta$ was to be computed from the extended training sequence with the test object assigned a postulated label $y$, making Prof. Takeuchi's procedure an instance of conformal prediction.
Changed line 23 from:
In 1977 Kei Takeuchi wrote a handout (dated July 17 1979) in which he anticipated some features of conformal prediction.  Developing the notion of an $1-\alpha$ expectation tolerance region, he posed the problem of finding the smallest (with respect to the Lebesgue measure) such region that would be valid under the [[randomness assumption]].  Invoking the standard completeness result for the order statistics, he replaced the [[randomness assumption]] by the [[exchangeable probability distribution | exchangeability assumption]].  Assuming that the observations are generated from a probability distribution with density $f_0$, he showed that the optimal solution is the [[conformal predictor]] assigning the conformity score $f_0(z_i)$ to an observation $z_i$.  This is a special case of [[conformal prediction]] not involving learning, but he developed a more general version that does require some learning (namely, parameter estimation).  If the true distribution is only supposed to belong to a parametric statistical model, he suggested using the plug-in density for computing the conformity scores $f_0(z_i\mid\hat\theta)$.  To preserve validity $\theta$ was to be computed from the extended training sequence with the test object assigned a postulated label $y$, making Prof. Takeuchi's procedure an instance of conformal prediction.
to:
In 1977 Kei Takeuchi wrote a handout (dated July 17 1979) in which he anticipated some features of conformal prediction.  Developing the notion of an $1-\alpha$ expectation tolerance region, he posed the problem of finding the smallest (with respect to the Lebesgue measure) such region that would be valid under the [[Main/randomness assumption]].  Invoking the standard completeness result for the order statistics, he replaced the [[Main/randomness assumption]] by the [[Main/exchangeable probability distribution | exchangeability assumption]].  Assuming that the observations are generated from a probability distribution with density $f_0$, he showed that the optimal solution is the [[Main/conformal predictor]] assigning the conformity score $f_0(z_i)$ to an observation $z_i$.  This is a special case of [[Main/conformal prediction]] not involving learning, but he developed a more general version that does require some learning (namely, parameter estimation).  If the true distribution is only supposed to belong to a parametric statistical model, he suggested using the plug-in density for computing the conformity scores $f_0(z_i\mid\hat\theta)$.  To preserve validity $\theta$ was to be computed from the extended training sequence with the test object assigned a postulated label $y$, making Prof. Takeuchi's procedure an instance of conformal prediction.
May 14, 2017, at 10:44 AM by Vovk - added information about his 1977 notes
Changed lines 21-23 from:
* [[Main/Game-theoretic Probability]]
to:
* [[Main/Game-theoretic Probability]]

In 1977 Kei Takeuchi wrote a handout (dated July 17 1979) in which he anticipated some features of conformal prediction.  Developing the notion of an $1-\alpha$ expectation tolerance region, he posed the problem of finding the smallest (with respect to the Lebesgue measure) such region that would be valid under the [[randomness assumption]].  Invoking the standard completeness result for the order statistics, he replaced the [[randomness assumption]] by the [[exchangeable probability distribution | exchangeability assumption]].  Assuming that the observations are generated from a probability distribution with density $f_0$, he showed that the optimal solution is the [[conformal predictor]] assigning the conformity score $f_0(z_i)$ to an observation $z_i$.  This is a special case of [[conformal prediction]] not involving learning, but he developed a more general version that does require some learning (namely, parameter estimation).  If the true distribution is only supposed to belong to a parametric statistical model, he suggested using the plug-in density for computing the conformity scores $f_0(z_i\mid\hat\theta)$.  To preserve validity $\theta$ was to be computed from the extended training sequence with the test object assigned a postulated label $y$, making Prof. Takeuchi's procedure an instance of conformal prediction.
April 20, 2008, at 01:46 PM by Volodya Vovk -
Changed line 1 from:
(:title Professor Kei Takeuchi:)
to:
(:title Kei Takeuchi:)
April 20, 2008, at 01:43 PM by Volodya Vovk -
Changed lines 1-2 from:
Kei Takeuchi holds PhD in Economics.  Until recently he was Professor at the Faculty of International Studies, MeijiGakuin University.  He is also Professor Emeritus, University of Tokyo;
Director, Institute for International Studies, MeijiGakuin University; Chairman, Statistics Council of Japan; President, Japan Statistical Association; President, Center for Academic Publications Japan; Former President, Japan Statistical Society.
to:
(:title Professor Kei Takeuchi:)
Kei Takeuchi holds PhD in Economics.  Until recently he was Professor at the Faculty of International Studies, MeijiGakuin University.  He is also Professor Emeritus, University of
Tokyo; Director, Institute for International Studies, MeijiGakuin University; Chairman, Statistics Council of Japan; President, Japan Statistical Association; President, Center for Academic Publications Japan; Former President, Japan Statistical Society.
April 16, 2008, at 10:25 AM by Volodya Vovk -
Changed lines 5-13 from:
* 1933      Born in Tokyo, Japan
* 1981      Graduated from University of Tokyo graduate school
* 1963-1994  Associate Professor then Professor, Faculty of Economics, University of Tokyo
* 1987-1994  Professor, Research Center for Advanced Science and Technology (Joint Appointment)
* 1994 
     Retired from the University of Tokyo
* 1994-2008  Professor, Faculty
of International Studies, Meiji Gakuin University
* 1988-1994  Member
of Science Council of Japan
* 1997-2000
  Member of Science Council of Japan
to:
* 1933        Born in Tokyo, Japan
* 1981        Graduated from University of Tokyo graduate school
* 1963 - 1994  Associate Professor then Professor, Faculty of Economics, University of Tokyo
* 1987 - 1994  Professor, Research Center for Advanced Science and Technology (Joint Appointment)
* 1994
         Retired from the University of Tokyo
* 1994 - 2008  Professor, Faculty
of International Studies, Meiji Gakuin University
* 1988 - 1994  Member of Science Council of Japan
* 1997 -
2000  Member of Science Council of Japan
Changed lines 17-18 from:
* Japanese Economy,
* Socio-Economic Impacts of Science and technology
to:
* Japanese Economy
* Socio-Economic Impacts of Science and Technology
April 16, 2008, at 10:23 AM by Volodya Vovk - created profile based on an OCDE web page
Added lines 1-21:
Kei Takeuchi holds PhD in Economics.  Until recently he was Professor at the Faculty of International Studies, MeijiGakuin University.  He is also Professor Emeritus, University of Tokyo;
Director, Institute for International Studies, MeijiGakuin University; Chairman, Statistics Council of Japan; President, Japan Statistical Association; President, Center for Academic Publications Japan; Former President, Japan Statistical Society.

Personal History:
* 1933      Born in Tokyo, Japan
* 1981      Graduated from University of Tokyo graduate school
* 1963-1994  Associate Professor then Professor, Faculty of Economics, University of Tokyo
* 1987-1994  Professor, Research Center for Advanced Science and Technology (Joint Appointment)
* 1994      Retired from the University of Tokyo
* 1994-2008  Professor, Faculty of International Studies, Meiji Gakuin University
* 1988-1994  Member of Science Council of Japan
* 1997-2000  Member of Science Council of Japan

Research Areas:
* Mathematical Statistics
* Econometrics
* Japanese Economy,
* Socio-Economic Impacts of Science and technology
* General History of Civilization
* Global Environment Problems
* [[Main/Game-theoretic Probability]]