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Network Estimation from Point Process Data
Description:
Consider observing a collection of discrete events within a network that reflects how network nodes influence one another. Such data are common in spike trains recorded from biological neural networks, interactions within a social network, and a variety of other settings. Data of this form may be modeled as self-exciting point processes, in whic...
Creator:
Willett, Rebecca (University of Wisconsin, Madison)
Created:
2018-04-24
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2019-02-04
Network Estimation from Point Process Data
Neural net word embeddings: What was old is new again
Description:
A first step of many machine learning approaches is the embedding the input data in a vector representation. In the case of the linguistic data, the word2vec algorithm (specifically skipgram with negative sampling) is popular and often considered to be the state of the art. We will discuss how word2vec and related algorithms work, and some inter...
Creator:
Bellay, Jeremy (Battelle)
Created:
2017-10-06
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2019-02-04
Neural net word embeddings: What was old is new again
New biomarker-adaptive designs of clinical trial for precision medicine
Description:
Precision medicine is the systematic use of information pertaining to an individual patient to select or optimize the patient’s preventative and therapeutic care. In recent literature, biomarkers have been classified to predictive biomarkers and prognostic biomarkers based one their role in clinical studies. To design a clinical trial for prec...
Creator:
Hu, Feifang (George Washington University)
Created:
2017-09-14
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2019-02-04
New biomarker-adaptive designs of clinical trial for precision medicine
Nonlinear electrohydrodynamics of drops and particles in strong electric fields
Description:
Weakly conducting dielectric solid particles and liquid droplets in strong electric fields are known to undergo symmetry-breaking bifurcations leading to steady electrorotation. This so-called Quincke effect, which results from the antiparallel electrostatic dipole induced by the applied field inside the particles, is well described by the class...
Creator:
Saintillan, David (University of California, San Diego)
Created:
2018-03-12
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2019-02-04
Nonlinear electrohydrodynamics of drops and particles in strong electric fields
Nonlinear Valuation in Theory and Practice
Description:
Theoretically equilibrium possibilities may be expanded by reducing the number of critical states. Furthermore, nonlinear valuation procedures may be used to assist the attainment of an expanded equilibrium with fewer securities than even the number of critical states. Practically, risk is an exposure to change in value or the variation in value...
Creator:
Madan, Dilip (University of Maryland)
Created:
2018-06-14
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2019-02-04
Nonlinear Valuation in Theory and Practice
Nonparametric inference for Hawkes processes. Applications for estimating functional connectivity graphs of neurons.
Description:
Functional connectivity in neuroscience is considered as one of the main features of the neural code. It is nowadays possible to obtain the spike activities of tens to hundreds of neurons simultaneously and the issue is then to infer the functional connectivity thanks to those complex data. To deal with this problem, we consider estimation of sp...
Creator:
Rivoirard, Vincent (Universite Paris-Dauphine)
Created:
2018-04-25
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2019-02-04
Nonparametric inference for Hawkes processes. Applications for estimating functional connectivity graphs of neurons.
Non-parametric Sparse Additive Auto-regressive Network Models
Description:
Consider a multi-variate time series, which may correspond to spike train responses for multiple neurons in a brain, crime event data across multiple regions, and many others. An important challenge associated with these time series models is to estimate an influence network between the d variables, especially when the number of variables d is l...
Creator:
Raskutti, Garvesh (University of Wisconsin, Madison)
Created:
2018-04-26
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2019-02-04
Non-parametric Sparse Additive Auto-regressive Network Models
Novel Machine Learning Methods for Extraction of Features Characterizing Complex Datasets and Models
Description:
The integration of large datasets and powerful computational capabilities has resulted in the widespread use of machine learning (ML) in science, technology, and industry. However, most of the recent ML developments focus on supervised methods which require large training tests. However, these supervised ML methods are not highly applicable to s...
Creator:
Vesselinov, Velimir (Los Alamos National Laboratory)
Created:
2018-10-25
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2019-02-04
Novel Machine Learning Methods for Extraction of Features Characterizing Complex Datasets and Models
Numerical approximation of average Markov decision processes in continuous-time
Description:
We study a numerical approximation of the optimal long-run average cost of a continuous-time Markov decision process, with Borel state and action spaces, and with bounded transition and reward rates. Our approach uses a suitable discretization of the state and action spaces to approximate the original control model. The approximation error for t...
Creator:
Dufour, Francois (Universite de Bordeaux I)
Created:
2018-05-08
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2019-02-04
Numerical approximation of average Markov decision processes in continuous-time
Numerical Approximation of Nematic Fluids
Description:
This talk focuses on the issues that arise when modeling andsimulating fluids containing rod--like molecules (nematics). The(average) orientation of these fluids is typically modeled by a unitvector field which complicates both the analysis and numericalsolution of these equations. In particular,i) The unit length constraint gives rise to topolo...
Creator:
Walkington, Noel (Carnegie Mellon University)
Created:
2018-01-19
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2019-02-04
Numerical Approximation of Nematic Fluids