Stats, Optimization, and Machine Learning Seminar - Jed Brown

Oct. 2, 2018

Fast Algorithms and Community Software for Physical Prediction, Inference, and Design Physically-based computational models are the foundation of modern science and engineering, providing the only path to reliable prediction and inference in the presence of sparse and indirect observations as well as deeper understanding of interacting physical processes and scales...

Stats, Optimization, and Machine Learning Seminar - Emilliano Dall'anese

Sept. 25, 2018

Online Optimization with Feedback The talk focuses on the design and analysis of running (i.e., online) algorithmic solutions to control systems or networked systems based on performance objectives and engineering constraints that may evolve over time. The time-varying convex optimization formalism is leveraged to model optimal operational trajectories of the...

Stats, Optimization, and Machine Learning Seminar - Carl Mueller and Osman Malik

Sept. 11, 2018

Carl Mueller, Department of Computer Science, University of Colorado Boulder CHOMP: Gradient Optimization Techniques for Efficient Motion Planning Existing high-dimensional motion planning algorithms are simultaneously overpowered and underpowered. Indomains sparsely populated by obstacles, the heuristics used by sampling-based planners to navigate “narrow passages” can be needlessly complex; furthermore, additional post-processing...

Stats, Optimization, and Machine Learning Seminar - Kyri Baker

Sept. 4, 2018

Chance Constraints for Smart Buildings and Smarter Grids Evolving energy systems are introducing heightened levels of stress on the electric power grid. Fluctuating renewable energy sources, dynamic electricity pricing, and new loads such as plug-in electric vehicles are transforming the operation of the grid, from the high-voltage transmission grid down...

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...

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