Stats, Optimization, and Machine Learning Seminar - Lio Horesh

Jan. 15, 2019

"Don't go with the flow -- – A new tensor algebra for Neural Networks" Multi-dimensional information often involves multi-dimensional correlations that may remain latent by virtue of traditional matrix-based learning algorithms. In this study, we propose a tensor neural network framework that offers an exciting new paradigm for supervised machine...

Stats, Optimization, and Machine Learning Seminar - Zhenhua Wang

Dec. 11, 2018

Induction of time inconsistency in optimal stopping problem Time inconsistency is a common phenomenon of optimal control and optimal stopping problems, especially in finance and economics. It says a player will change his optimal strategy over time. To deal with such problem, we usually search for some consistent plan (equilibrium)...

Stats, Optimization, and Machine Learning Seminar - Colton Grainger and Claire Savard

Nov. 27, 2018

Colton Grainger, Department of Mathematics, University of Colorado Boulder On Characterizing the Capacity of Neural Networks using Algebraic Topology The learnability of different neural architectures can be characterized directly by computable measures of data complexity. In this paper, we reframe the problem of architecture selection as understanding how data determines...

Stats, Optimization, and Machine Learning Seminar - Nicholas Landry

Nov. 6, 2018

Music Data Mining: Finding structure in song An introduction to basic music data mining techniques

Stats, Optimization, and Machine Learning Seminar - Jeffrey Hokanson

Oct. 16, 2018

Exploiting Low-Dimensional Structure in Optimization Under Uncertainty In computational science, optimization under uncertainty (OUU) provides a new methodology for building designs reliable under a variety of conditions with improved efficiency over a traditional, safety factor based approach. However, the resulting optimization problems can be challenging. For example, chance constraints bounding...

Stats, Optimization, and Machine Learning Seminar - Nishant Mehta

Oct. 9, 2018

Fast Rates for Unbounded Losses: from ERM to Generalized Bayes I will present new excess risk bounds for randomized and deterministic estimators, discarding boundedness assumptions to handle general unbounded loss functions like log loss and squared loss under heavy tails. These bounds have a PAC-Bayesian flavor in both derivation and...

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...

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