Stochastics Seminar - Nils Detering

Aug. 30, 2018

Managing Default Contagion In Inhomogeneous Financial Networks The aim of this paper is to quantify and manage systemic risk caused by default contagion in the interbank market. Our results allow us to determine the impact of local shocks to the entire system and the wider economy. As a central application,...

Stats, Optimization, and Machine Learning Seminar - Rose Yu

Aug. 19, 2018

Learning from Large-Scale Spatiotemporal Data In many real-world applications, such as internet of things (IoT), transportation and physics, machine learning is applied to large-scale spatiotemporal data. Such data is often nonlinear, high-dimensional, and demonstrates complex spatial and temporal correlations. In this talk, I will demonstrate how to efficiently learn from...

Stats, Optimization, and Machine Learning Seminar - Tracy Babb

April 24, 2018

Paper presentation of “Practical sketching algorithms for low-rank matrix approximation” by Tropp, et al. We will present the following paper: “Practical sketching algorithms for low-rank matrix approximation” by J. A. Tropp, A. Yurtsever, M. Udell, and V. Cevher http://users.cms.caltech.edu/~jtropp/papers/TYUC17-Practical-Sketching-S... The paper extends previous work on the randomized SVD by Halko,...

Stats, Optimization, and Machine Learning Seminar - Nathaniel Mathews

April 10, 2018

discussion of paper "Constrained Global Optimization of Expensive Black Box Functions Using Radial Basis Functions" The authors (Regis et al.) propose an iterative response-surface model for optimization which is well suited to nonlinear constraints on nonconvex objectives, and is meant to allow relatively fast optimization for high-dimensional problems. We will...

Stats, Optimization, and Machine Learning Seminar - The Reproducibility Crisis, pt 1

Feb. 13, 2018

First part of our series on the reproducibility crisis: Peter Shaffery will present Simmons, Nelson, and Simonsohn's seminal 2011 article "False Positive Psychology" ( http://journals.sagepub.com/doi/abs/10.1177/0956797611417632 ) Here's a quick blog post that treads some similar ground, people may want to look at that ahead of time if they're curious (...

Stats, Optimization, and Machine Learning Seminar - Dan Zhang

Feb. 6, 2018

Some Recent Results on Linear Programming Based Approximate Dynamic Programming The linear programming based approximate dynamic programming has received considerable attention in the recent literature. In this approach, high dimensional dynamic programs are solved approximately as large-scale linear programs to tackle the curse of dimensionality. The linear programming formulations are...

Stats, Optimization, and Machine Learning Seminar - Farhad Pourkamali-Anaraki

Jan. 23, 2018

With the growing scale and complexity of datasets in scientific disciplines, traditional data analysis methods are no longer practical to extract meaningful information and patterns. The need to process large-scale datasets by memory and computation efficient algorithms arises in all fields of science and engineering. Randomization and probabilistic techniques have...

Stats, Optimization, and Machine Learning Seminar - Abtin Rahimian

March 14, 2017

Event Description: Abtin Rahimian, Courant Institute of Mathematical Sciences, New York University Fast algorithms for structured matrices in simulations of physical systems Real-world complex phenomena are typically characterized by interacting physical processes, uncertain parameters, dynamic boundaries, and close coupling over a wide span of spatial and temporal scales. Predictive computational...

Stats, Optimization, and Machine Learning Seminar - Alexandra Kolla

March 7, 2017

Event Description: ALTERNATE LOCATION! The seminar will meet in DLC 170 for this week! Alexandra Kolla, Assistant Professor of Computer Science, University of Illinois Urbana-Champaign The Sound of Graphs In this talk, Kolla will discuss major implications that linear algebraic techniques have in understanding and resolving hard computational and graph...

Stats, Optimization, and Machine Learning Seminar

Feb. 28, 2017

Event Description: Topics in Statistics, Optimization, and Machine Learning Location Information: Main Campus - Engineering Classroom Wing ( View Map ) 1111 Engineering DR Boulder, CO Room: 257: Newton Lab Contact Information: Name: Ian Cunningham Phone: 303-492-4668 Email: amassist@colorado.edu

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