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Microstructural Kinetics Group

Department of Materials Science & Metallurgy
 

Thu 25 Apr 11:45: Stochastic approximation with heavy tailed noise TMLW02 - SGD: stability, momentum acceleration and heavy tails

School of Physical Sciences - Tue, 16/04/2024 - 17:30
Stochastic approximation with heavy tailed noise

TMLW02 - SGD: stability, momentum acceleration and heavy tails

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Thu 25 Apr 11:00: Stochastic Gradient Descent with Adaptive Data TMLW02 - SGD: stability, momentum acceleration and heavy tails

School of Physical Sciences - Tue, 16/04/2024 - 17:30
Stochastic Gradient Descent with Adaptive Data

TMLW02 - SGD: stability, momentum acceleration and heavy tails

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Wed 24 Apr 15:15: Aggregating regularly varying vectors: phenomena of a few large jumps TMLW02 - SGD: stability, momentum acceleration and heavy tails

School of Physical Sciences - Tue, 16/04/2024 - 17:30
Aggregating regularly varying vectors: phenomena of a few large jumps

TMLW02 - SGD: stability, momentum acceleration and heavy tails

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Wed 24 Apr 14:30: Algorithmic stability for heavy-tailed SGD TMLW02 - SGD: stability, momentum acceleration and heavy tails

School of Physical Sciences - Tue, 16/04/2024 - 17:30
Algorithmic stability for heavy-tailed SGD

TMLW02 - SGD: stability, momentum acceleration and heavy tails

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Wed 24 Apr 11:45: Learning Operators - Recent Advances TMLW02 - SGD: stability, momentum acceleration and heavy tails

School of Physical Sciences - Tue, 16/04/2024 - 17:30
Learning Operators - Recent Advances

TMLW02 - SGD: stability, momentum acceleration and heavy tails

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Wed 24 Apr 09:45: About small jumps of Lévy processes: approximations and estimation TMLW02 - SGD: stability, momentum acceleration and heavy tails

School of Physical Sciences - Tue, 16/04/2024 - 17:30
About small jumps of Lévy processes: approximations and estimation

TMLW02 - SGD: stability, momentum acceleration and heavy tails

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Tue 23 Apr 09:45: Adaptive Importance Sampling for accelerating the minimization of tail risks TMLW02 - SGD: stability, momentum acceleration and heavy tails

School of Physical Sciences - Tue, 16/04/2024 - 17:30
Adaptive Importance Sampling for accelerating the minimization of tail risks

TMLW02 - SGD: stability, momentum acceleration and heavy tails

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Mon 22 Apr 16:45: Reception TMLW02 - SGD: stability, momentum acceleration and heavy tails

School of Physical Sciences - Tue, 16/04/2024 - 17:30
Reception

TMLW02 - SGD: stability, momentum acceleration and heavy tails

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Mon 22 Apr 16:00: Title TBA TMLW02 - SGD: stability, momentum acceleration and heavy tails

School of Physical Sciences - Tue, 16/04/2024 - 17:30
Title TBA

TMLW02 - SGD: stability, momentum acceleration and heavy tails

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Mon 22 Apr 14:30: Progress Report Presentations TMLW02 - SGD: stability, momentum acceleration and heavy tails

School of Physical Sciences - Tue, 16/04/2024 - 17:30
Progress Report Presentations

TMLW02 - SGD: stability, momentum acceleration and heavy tails

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Mon 10 Jun 14:50: TBA ADIW04 - Anti-Diffusion in Multiphase and Active Flows

School of Physical Sciences - Tue, 16/04/2024 - 16:30
TBA

ADIW04 - Anti-Diffusion in Multiphase and Active Flows

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Tue 16 Apr 14:00: Lie Fu

School of Physical Sciences - Tue, 16/04/2024 - 14:30
Lie Fu

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Fri 26 Apr 15:30: Leveraging multi-study, multi-outcome data to improve external validity and efficiency of clinical trials for managing schizophrenia

School of Physical Sciences - Tue, 16/04/2024 - 13:37
Leveraging multi-study, multi-outcome data to improve external validity and efficiency of clinical trials for managing schizophrenia

As data sources have become more plentiful and readily accessible, the practice of data fusion has become increasingly ubiquitous. However, when the focus is on a causal effect on a particular outcome, a major limitation is that this outcome may not be available in all data sources. In fact, different randomized experiments or observational studies of a common exposure will often focus on potentially related, yet distinct outcomes. One such example is the Database of Cognitive Training and Remediation Studies (DoCTRS), which consists of several randomized trials of the effect of cognitive remediation therapy on various outcomes among patients with schizophrenia. We develop causally principled methodology for fusing data sets when multiple outcomes are observed across studies, which leverages outcomes of secondary interest as informative proxies for the missing outcome of primary interest, thereby maximizing power and efficiency by making full use of the available data. As this methodology relies on a key transportability assumption, we also develop methods to assess the degree of sensitivity to violations of this assumption. We apply this methodology to data from the DoCTRS trials to make improved causal inferences about the effectiveness of cognitive remediation therapy on cognition among patients with schizophrenia.

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Fri 26 Apr 15:30: Leveraging multi-study, multi-outcome data to improve external validity and efficiency of clinical trials for managing schizophrenia

School of Physical Sciences - Tue, 16/04/2024 - 13:37
Leveraging multi-study, multi-outcome data to improve external validity and efficiency of clinical trials for managing schizophrenia

As data sources have become more plentiful and readily accessible, the practice of data fusion has become increasingly ubiquitous. However, when the focus is on a causal effect on a particular outcome, a major limitation is that this outcome may not be available in all data sources. In fact, different randomized experiments or observational studies of a common exposure will often focus on potentially related, yet distinct outcomes. One such example is the Database of Cognitive Training and Remediation Studies (DoCTRS), which consists of several randomized trials of the effect of cognitive remediation therapy on various outcomes among patients with schizophrenia. We develop causally principled methodology for fusing data sets when multiple outcomes are observed across studies, which leverages outcomes of secondary interest as informative proxies for the missing outcome of primary interest, thereby maximizing power and efficiency by making full use of the available data. As this methodology relies on a key transportability assumption, we also develop methods to assess the degree of sensitivity to violations of this assumption. We apply this methodology to data from the DoCTRS trials to make improved causal inferences about the effectiveness of cognitive remediation therapy on cognition among patients with schizophrenia.

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Fri 26 Apr 15:45: Final Remarks and Close OFBW65 - Connecting Heavy Tails and Differential Privacy in Machine Learning

School of Physical Sciences - Tue, 16/04/2024 - 12:30
Final Remarks and Close

OFBW65 - Connecting Heavy Tails and Differential Privacy in Machine Learning

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Fri 26 Apr 10:30: Welcome and Introduction OFBW65 - Connecting Heavy Tails and Differential Privacy in Machine Learning

School of Physical Sciences - Tue, 16/04/2024 - 12:30
Welcome and Introduction

OFBW65 - Connecting Heavy Tails and Differential Privacy in Machine Learning

  • Speaker: Jorge Gonzalez-Cazares (National Autonomous University of Mexico (UNAM), IIMAS), Sofia Sanz Del Pino (Isaac Newton Institute)
  • Friday 26 April 2024, 10:30-10:40
  • Venue: External.
  • Series: Isaac Newton Institute Seminar Series; organiser: nobody.

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