Logo

Mathematical Sciences Research Institute

Home > Scientific > Colloquia & Seminars

Colloquia & Seminars

All Seminars

Postdoc Seminars

Graduate Seminars

Other Colloquia & Seminars



Current Seminars

  1. Welcome Tea

    Location: MSRI: Atrium
    Updated on Aug 25, 2021 11:32 AM PDT
  2. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT

Upcoming Seminars

  1. Random Matrices and Random Landscapes

    Location: MSRI: Simons Auditorium, Online/Virtual
    UC Berkeley, 740 Evans Hall
    Speakers: Gérard Ben Arous (New York University, Courant Institute)

     

    To register for this course, go to: https://www.msri.org/seminars/26228

    This class aims at understanding some important classes of smooth random functions of very many variables.

    What can be said about the complexity of the topology of the landscapes they define?

    How efficient are the natural exploration or optimization algorithms in these landscapes?

    The toolbox of Random Matrix Theory will be used for both questions.

     

    We will concentrate on two wide classes of interesting smooth random functions of many variables.

    A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spin-glasses. There the randomness is assumed to model quenched disorder in the medium.

    Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in high-dimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.

    Updated on Sep 03, 2021 12:07 PM PDT
  2. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Sep 08, 2021 11:17 AM PDT
  3. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  4. Random Matrices and Random Landscapes

    Location: MSRI: Simons Auditorium, Online/Virtual
    UC Berkeley, 740 Evans Hall
    Speakers: Gérard Ben Arous (New York University, Courant Institute)

    To register for this course, go to: https://www.msri.org/seminars/26228

    This class aims at understanding some important classes of smooth random functions of very many variables.

    What can be said about the complexity of the topology of the landscapes they define?

    How efficient are the natural exploration or optimization algorithms in these landscapes?

    The toolbox of Random Matrix Theory will be used for both questions.

     

    We will concentrate on two wide classes of interesting smooth random functions of many variables.

    A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spin-glasses. There the randomness is assumed to model quenched disorder in the medium.

    Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in high-dimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.

    Updated on Sep 03, 2021 12:14 PM PDT
  5. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  6. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  7. Welcome Tea

    Location: MSRI: Atrium
    Updated on Aug 25, 2021 11:32 AM PDT
  8. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  9. Random Matrices and Random Landscapes

    Location: MSRI: Simons Auditorium, Online/Virtual
    UC Berkeley, 740 Evans Hall
    Speakers: Gérard Ben Arous (New York University, Courant Institute)

    To register for this course, go to: https://www.msri.org/seminars/26228

    This class aims at understanding some important classes of smooth random functions of very many variables.

    What can be said about the complexity of the topology of the landscapes they define?

    How efficient are the natural exploration or optimization algorithms in these landscapes?

    The toolbox of Random Matrix Theory will be used for both questions.

     

    We will concentrate on two wide classes of interesting smooth random functions of many variables.

    A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spin-glasses. There the randomness is assumed to model quenched disorder in the medium.

    Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in high-dimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.

    Updated on Sep 03, 2021 12:14 PM PDT
  10. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  11. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  12. Random Matrices and Random Landscapes

    Location: MSRI: Simons Auditorium, Online/Virtual
    UC Berkeley, 740 Evans Hall
    Speakers: Gérard Ben Arous (New York University, Courant Institute)

    To register for this course, go to: https://www.msri.org/seminars/26228

    This class aims at understanding some important classes of smooth random functions of very many variables.

    What can be said about the complexity of the topology of the landscapes they define?

    How efficient are the natural exploration or optimization algorithms in these landscapes?

    The toolbox of Random Matrix Theory will be used for both questions.

     

    We will concentrate on two wide classes of interesting smooth random functions of many variables.

    A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spin-glasses. There the randomness is assumed to model quenched disorder in the medium.

    Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in high-dimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.

    Updated on Sep 03, 2021 12:14 PM PDT
  13. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  14. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  15. Welcome Tea

    Location: MSRI: Atrium
    Updated on Aug 25, 2021 11:32 AM PDT
  16. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  17. Random Matrices and Random Landscapes

    Location: MSRI: Simons Auditorium, Online/Virtual
    UC Berkeley, 740 Evans Hall
    Speakers: Gérard Ben Arous (New York University, Courant Institute)

    To register for this course, go to: https://www.msri.org/seminars/26228

    This class aims at understanding some important classes of smooth random functions of very many variables.

    What can be said about the complexity of the topology of the landscapes they define?

    How efficient are the natural exploration or optimization algorithms in these landscapes?

    The toolbox of Random Matrix Theory will be used for both questions.

     

    We will concentrate on two wide classes of interesting smooth random functions of many variables.

    A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spin-glasses. There the randomness is assumed to model quenched disorder in the medium.

    Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in high-dimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.

    Updated on Sep 03, 2021 12:15 PM PDT
  18. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  19. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  20. Random Matrices and Random Landscapes

    Location: MSRI: Simons Auditorium, Online/Virtual
    UC Berkeley, 740 Evans Hall
    Speakers: Gérard Ben Arous (New York University, Courant Institute)

    To register for this course, go to: https://www.msri.org/seminars/26228

    This class aims at understanding some important classes of smooth random functions of very many variables.

    What can be said about the complexity of the topology of the landscapes they define?

    How efficient are the natural exploration or optimization algorithms in these landscapes?

    The toolbox of Random Matrix Theory will be used for both questions.

     

    We will concentrate on two wide classes of interesting smooth random functions of many variables.

    A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spin-glasses. There the randomness is assumed to model quenched disorder in the medium.

    Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in high-dimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.

    Updated on Sep 03, 2021 12:15 PM PDT
  21. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  22. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  23. Welcome Tea

    Location: MSRI: Atrium
    Updated on Aug 25, 2021 11:32 AM PDT
  24. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  25. Random Matrices and Random Landscapes

    Location: MSRI: Simons Auditorium, Online/Virtual
    UC Berkeley, 740 Evans Hall
    Speakers: Gérard Ben Arous (New York University, Courant Institute)

    To register for this course, go to: https://www.msri.org/seminars/26228

    This class aims at understanding some important classes of smooth random functions of very many variables.

    What can be said about the complexity of the topology of the landscapes they define?

    How efficient are the natural exploration or optimization algorithms in these landscapes?

    The toolbox of Random Matrix Theory will be used for both questions.

     

    We will concentrate on two wide classes of interesting smooth random functions of many variables.

    A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spin-glasses. There the randomness is assumed to model quenched disorder in the medium.

    Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in high-dimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.

    Updated on Sep 03, 2021 12:15 PM PDT
  26. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  27. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  28. Random Matrices and Random Landscapes

    Location: MSRI: Simons Auditorium, Online/Virtual
    UC Berkeley, 740 Evans Hall
    Speakers: Gérard Ben Arous (New York University, Courant Institute)

    To register for this course, go to: https://www.msri.org/seminars/26228

    This class aims at understanding some important classes of smooth random functions of very many variables.

    What can be said about the complexity of the topology of the landscapes they define?

    How efficient are the natural exploration or optimization algorithms in these landscapes?

    The toolbox of Random Matrix Theory will be used for both questions.

     

    We will concentrate on two wide classes of interesting smooth random functions of many variables.

    A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spin-glasses. There the randomness is assumed to model quenched disorder in the medium.

    Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in high-dimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.

    Updated on Sep 03, 2021 12:17 PM PDT
  29. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  30. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  31. Welcome Tea

    Location: MSRI: Atrium
    Updated on Aug 25, 2021 11:32 AM PDT
  32. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  33. Random Matrices and Random Landscapes

    Location: MSRI: Simons Auditorium, Online/Virtual
    UC Berkeley, 740 Evans Hall
    Speakers: Gérard Ben Arous (New York University, Courant Institute)

    To register for this course, go to: https://www.msri.org/seminars/26228

    This class aims at understanding some important classes of smooth random functions of very many variables.

    What can be said about the complexity of the topology of the landscapes they define?

    How efficient are the natural exploration or optimization algorithms in these landscapes?

    The toolbox of Random Matrix Theory will be used for both questions.

     

    We will concentrate on two wide classes of interesting smooth random functions of many variables.

    A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spin-glasses. There the randomness is assumed to model quenched disorder in the medium.

    Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in high-dimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.

    Updated on Sep 03, 2021 12:17 PM PDT
  34. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  35. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  36. Random Matrices and Random Landscapes

    Location: MSRI: Simons Auditorium, Online/Virtual
    UC Berkeley, 740 Evans Hall
    Speakers: Gérard Ben Arous (New York University, Courant Institute)

    To register for this course, go to: https://www.msri.org/seminars/26228

    This class aims at understanding some important classes of smooth random functions of very many variables.

    What can be said about the complexity of the topology of the landscapes they define?

    How efficient are the natural exploration or optimization algorithms in these landscapes?

    The toolbox of Random Matrix Theory will be used for both questions.

     

    We will concentrate on two wide classes of interesting smooth random functions of many variables.

    A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spin-glasses. There the randomness is assumed to model quenched disorder in the medium.

    Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in high-dimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.

    Updated on Sep 03, 2021 12:20 PM PDT
  37. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  38. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  39. Welcome Tea

    Location: MSRI: Atrium
    Updated on Aug 25, 2021 11:32 AM PDT
  40. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  41. Random Matrices and Random Landscapes

    Location: MSRI: Simons Auditorium, Online/Virtual
    UC Berkeley, 740 Evans Hall
    Speakers: Gérard Ben Arous (New York University, Courant Institute)

    To register for this course, go to: https://www.msri.org/seminars/26228

    This class aims at understanding some important classes of smooth random functions of very many variables.

    What can be said about the complexity of the topology of the landscapes they define?

    How efficient are the natural exploration or optimization algorithms in these landscapes?

    The toolbox of Random Matrix Theory will be used for both questions.

     

    We will concentrate on two wide classes of interesting smooth random functions of many variables.

    A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spin-glasses. There the randomness is assumed to model quenched disorder in the medium.

    Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in high-dimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.

    Updated on Sep 03, 2021 12:21 PM PDT
  42. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  43. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  44. Random Matrices and Random Landscapes

    Location: MSRI: Simons Auditorium, Online/Virtual
    UC Berkeley, 740 Evans Hall
    Speakers: Gérard Ben Arous (New York University, Courant Institute)

    To register for this course, go to: https://www.msri.org/seminars/26228

    This class aims at understanding some important classes of smooth random functions of very many variables.

    What can be said about the complexity of the topology of the landscapes they define?

    How efficient are the natural exploration or optimization algorithms in these landscapes?

    The toolbox of Random Matrix Theory will be used for both questions.

     

    We will concentrate on two wide classes of interesting smooth random functions of many variables.

    A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spin-glasses. There the randomness is assumed to model quenched disorder in the medium.

    Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in high-dimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.

    Updated on Sep 03, 2021 12:22 PM PDT
  45. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  46. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  47. Welcome Tea

    Location: MSRI: Atrium
    Updated on Aug 25, 2021 11:32 AM PDT
  48. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  49. Random Matrices and Random Landscapes

    Location: MSRI: Simons Auditorium, Online/Virtual
    UC Berkeley, 740 Evans Hall
    Speakers: Gérard Ben Arous (New York University, Courant Institute)

    To register for this course, go to: https://www.msri.org/seminars/26228

    This class aims at understanding some important classes of smooth random functions of very many variables.

    What can be said about the complexity of the topology of the landscapes they define?

    How efficient are the natural exploration or optimization algorithms in these landscapes?

    The toolbox of Random Matrix Theory will be used for both questions.

     

    We will concentrate on two wide classes of interesting smooth random functions of many variables.

    A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spin-glasses. There the randomness is assumed to model quenched disorder in the medium.

    Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in high-dimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.

    Updated on Sep 03, 2021 12:22 PM PDT
  50. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  51. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  52. Random Matrices and Random Landscapes

    Location: MSRI: Simons Auditorium, Online/Virtual
    UC Berkeley, 740 Evans Hall
    Speakers: Gérard Ben Arous (New York University, Courant Institute)

    To register for this course, go to: https://www.msri.org/seminars/26228

    This class aims at understanding some important classes of smooth random functions of very many variables.

    What can be said about the complexity of the topology of the landscapes they define?

    How efficient are the natural exploration or optimization algorithms in these landscapes?

    The toolbox of Random Matrix Theory will be used for both questions.

     

    We will concentrate on two wide classes of interesting smooth random functions of many variables.

    A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spin-glasses. There the randomness is assumed to model quenched disorder in the medium.

    Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in high-dimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.

    Updated on Sep 03, 2021 12:23 PM PDT
  53. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  54. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  55. Welcome Tea

    Location: MSRI: Atrium
    Updated on Aug 25, 2021 11:32 AM PDT
  56. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  57. Random Matrices and Random Landscapes

    Location: MSRI: Simons Auditorium, Online/Virtual
    UC Berkeley, 740 Evans Hall
    Speakers: Gérard Ben Arous (New York University, Courant Institute)

    To register for this course, go to: https://www.msri.org/seminars/26228

    This class aims at understanding some important classes of smooth random functions of very many variables.

    What can be said about the complexity of the topology of the landscapes they define?

    How efficient are the natural exploration or optimization algorithms in these landscapes?

    The toolbox of Random Matrix Theory will be used for both questions.

     

    We will concentrate on two wide classes of interesting smooth random functions of many variables.

    A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spin-glasses. There the randomness is assumed to model quenched disorder in the medium.

    Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in high-dimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.

    Updated on Sep 03, 2021 12:23 PM PDT
  58. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  59. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  60. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  61. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  62. Welcome Tea

    Location: MSRI: Atrium
    Updated on Aug 25, 2021 11:32 AM PDT
  63. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  64. Random Matrices and Random Landscapes

    Location: MSRI: Simons Auditorium, Online/Virtual
    UC Berkeley, 740 Evans Hall
    Speakers: Gérard Ben Arous (New York University, Courant Institute)

    To register for this course, go to: https://www.msri.org/seminars/26228

    This class aims at understanding some important classes of smooth random functions of very many variables.

    What can be said about the complexity of the topology of the landscapes they define?

    How efficient are the natural exploration or optimization algorithms in these landscapes?

    The toolbox of Random Matrix Theory will be used for both questions.

     

    We will concentrate on two wide classes of interesting smooth random functions of many variables.

    A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spin-glasses. There the randomness is assumed to model quenched disorder in the medium.

    Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in high-dimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.

    Updated on Sep 03, 2021 12:23 PM PDT
  65. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  66. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  67. Random Matrices and Random Landscapes

    Location: MSRI: Simons Auditorium, Online/Virtual
    UC Berkeley, 740 Evans Hall
    Speakers: Gérard Ben Arous (New York University, Courant Institute)

    To register for this course, go to: https://www.msri.org/seminars/26228

    This class aims at understanding some important classes of smooth random functions of very many variables.

    What can be said about the complexity of the topology of the landscapes they define?

    How efficient are the natural exploration or optimization algorithms in these landscapes?

    The toolbox of Random Matrix Theory will be used for both questions.

     

    We will concentrate on two wide classes of interesting smooth random functions of many variables.

    A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spin-glasses. There the randomness is assumed to model quenched disorder in the medium.

    Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in high-dimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.

    Updated on Sep 03, 2021 12:23 PM PDT
  68. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  69. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  70. Welcome Tea

    Location: MSRI: Atrium
    Updated on Aug 25, 2021 11:32 AM PDT
  71. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  72. Random Matrices and Random Landscapes

    Location: MSRI: Simons Auditorium, Online/Virtual
    UC Berkeley, 740 Evans Hall
    Speakers: Gérard Ben Arous (New York University, Courant Institute)

    To register for this course, go to: https://www.msri.org/seminars/26228

    This class aims at understanding some important classes of smooth random functions of very many variables.

    What can be said about the complexity of the topology of the landscapes they define?

    How efficient are the natural exploration or optimization algorithms in these landscapes?

    The toolbox of Random Matrix Theory will be used for both questions.

     

    We will concentrate on two wide classes of interesting smooth random functions of many variables.

    A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spin-glasses. There the randomness is assumed to model quenched disorder in the medium.

    Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in high-dimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.

    Updated on Sep 03, 2021 12:24 PM PDT
  73. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  74. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  75. Welcome Tea

    Location: MSRI: Atrium
    Updated on Aug 25, 2021 11:32 AM PDT
  76. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  77. Random Matrices and Random Landscapes

    Location: MSRI: Simons Auditorium, Online/Virtual
    UC Berkeley, 740 Evans Hall
    Speakers: Gérard Ben Arous (New York University, Courant Institute)

    To register for this course, go to: https://www.msri.org/seminars/26228

    This class aims at understanding some important classes of smooth random functions of very many variables.

    What can be said about the complexity of the topology of the landscapes they define?

    How efficient are the natural exploration or optimization algorithms in these landscapes?

    The toolbox of Random Matrix Theory will be used for both questions.

     

    We will concentrate on two wide classes of interesting smooth random functions of many variables.

    A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spin-glasses. There the randomness is assumed to model quenched disorder in the medium.

    Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in high-dimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.

    Updated on Sep 03, 2021 12:24 PM PDT
  78. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  79. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  80. Random Matrices and Random Landscapes

    Location: MSRI: Simons Auditorium, Online/Virtual
    UC Berkeley, 740 Evans Hall
    Speakers: Gérard Ben Arous (New York University, Courant Institute)

    To register for this course, go to: https://www.msri.org/seminars/26228

    This class aims at understanding some important classes of smooth random functions of very many variables.

    What can be said about the complexity of the topology of the landscapes they define?

    How efficient are the natural exploration or optimization algorithms in these landscapes?

    The toolbox of Random Matrix Theory will be used for both questions.

     

    We will concentrate on two wide classes of interesting smooth random functions of many variables.

    A first source of such functions is to be found in statistical mechanics of disordered systems, i.e. the Hamiltonians of disordered models, like spin-glasses. There the randomness is assumed to model quenched disorder in the medium.

    Another rich class of such functions comes from Data Science and studies the random landscapes of inference problems in high-dimensional statistical estimation. Here the randomness of these landscapes is the randomness inherent in sampling.

    Updated on Sep 03, 2021 12:24 PM PDT
  81. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  82. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  83. Welcome Tea

    Location: MSRI: Atrium
    Updated on Aug 25, 2021 11:32 AM PDT
  84. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  85. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  86. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  87. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  88. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  89. Welcome Tea

    Location: MSRI: Atrium
    Updated on Aug 25, 2021 11:32 AM PDT
  90. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  91. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  92. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  93. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
  94. Afternoon Tea

    Location: MSRI: Atrium
    Updated on Aug 24, 2021 11:21 AM PDT
No upcoming events under African Diaspora Joint Mathematics Workshop

Past Seminars

  1. Seminar Meet the Staff

    Updated on Sep 15, 2021 03:06 PM PDT
  2. Seminar Afternoon Tea

    Updated on Aug 24, 2021 11:21 AM PDT
  3. Seminar Afternoon Tea

    Updated on Aug 24, 2021 11:21 AM PDT
  4. Seminar UIRM Five Minute Talks

    Updated on Sep 03, 2021 09:28 AM PDT
  5. Seminar Afternoon Tea

    Updated on Aug 24, 2021 11:21 AM PDT
  6. Seminar Afternoon Tea

    Updated on Aug 24, 2021 11:21 AM PDT
  7. Seminar UIRM Five Minute Talks

    Updated on Sep 03, 2021 11:03 AM PDT
  8. Seminar Welcome Tea

    Updated on Aug 25, 2021 11:32 AM PDT
  9. Seminar Afternoon Tea

    Updated on Aug 24, 2021 11:21 AM PDT
  10. Seminar UIRM Five Minute Talks

    Updated on Sep 03, 2021 09:26 AM PDT
  11. Seminar Afternoon Tea

    Updated on Aug 24, 2021 11:21 AM PDT
  12. Seminar Afternoon Tea

    Updated on Aug 24, 2021 11:21 AM PDT
  13. Seminar Afternoon Tea

    Updated on Aug 24, 2021 11:21 AM PDT
  14. Seminar Afternoon Tea

    Updated on Aug 24, 2021 11:21 AM PDT
There are more then 30 past seminars. Please go to Past seminars to see all past seminars.