Sep
1
Quasi-Monte Carlo Software
Speaker: Fred Hickernell, professor of mathematics, Illinois Institute of Technology Description: Quasi-Monte Carlo (QMC) methods achieve substantial efficiency gains by replacing independent and...
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Nov
8
High-dimensional Black-box Optimization Under Uncertainty
Speaker: Hadis Anahideh, Research Assistant Professor of Mechanical and Industrial Engineering, UIC Description: limited informative data remains the primary challenge for the optimization expensive...
Nov
1
New Particle-Based Variational Inferences Methods by a Discrete Energetic Variational Approach
Speaker: Jiuhai Chen, Ph.D. candidate (AMAT) Illinois Tech Description: In this work, we present a new particle-based VI methods for Bayesian inference, inspired by a discrete energetic variational...
Oct
11
Fast Automatic Bayesian Cubature Using Matching Kernels and Designs
Speaker: Jagadeeswaran Rathinavel, Illinois Tech Ph.D. candidate (AMAT) Description: Automatic cubatures approximate integrals to user-specified error tolerances. For high dimensional problems, it is...
Oct
4
An Idea of How to Improve the Random Trees of Leo Breiman
Speaker: Romà Domènech Masana, Illinois Tech Ph.D. candidate (AMAT) Description: The idea focuses on making more complex splits. A Random Tree is grown by several steps. A Random Tree needs a dataset...
Sep
27
Adaptive Quasi-Monte Carlo Methods for Bayesian Inference and Uncertainty Quantification
Speaker: Kan Zhang, Illinois Tech Ph.D. candidate (AMAT) Description: Computing the expected value of a parameter via Bayesian inference involves the numerical approximation of the quotient of two...
Sep
20
A Discrepancy-Based Design for A/B Testing Experiments
Host Department of Applied Mathematics Speaker Yiou Li Department of Mathematical Sciences, Depaul University https://csh.depaul.edu/faculty-staff/faculty-a-z/Pages/mathematical-sciences/yiou-li.aspx...
Sep
6
Gaussian Process Models for Large-Scale Experiments
Host Department of Applied Mathematics Speaker Lulu Kang Department of Applied Mathematics, Illinois Institute of Technology http://math.iit.edu/~lkang2/ Description Gaussian process regression is a...
Aug
30
The Challenges of Approximating Functions of Many Variables
Host Department of Applied Mathematics Speaker Fred Hickernell Department of Applied Mathematics, Illinois Institute of Technology Description Function approximation is relatively simple compared to...
Apr
30
Gaussian Process Model Assisted Active Learning of Physical Laws
Host Department of Applied Mathematics Speaker Jiuhai Chen Department of Applied Mathematics, Illinois Institute of Technology www.iit.edu/applied-math/about/phd-students Description PDE and ODE are...