Stats, Optimization, and Machine Learning Seminar - Zhishen Huang and Antony Pearson

Sept. 23, 2019

Zhishen Huang Department of Applied Mathematics, University of Colorado Boulder Finding local minimizers in nonconvex and non-smooth optimization We consider the problem of finding local minimizers in nonconvex and non-smooth optimization. The objective function we consider is in the form of the sum of a nonconvex function and a l1-penalty...

Stats, Optimization, and Machine Learning Seminar - Ashutosh Trivedi

Sept. 17, 2019

Ashutosh Trivedi Department of Computer Science, University of Colorado Boulder Reinforcement Learning and Formal Requirements Reinforcement learning is an approach to controller synthesis where agents rely on reward signals to choose actions in order to satisfy the requirements implicit in reward signals. Oftentimes non-experts have to come up with the...

Stats, Optimization, and Machine Learning Seminar - Jorge Poveda

Sept. 10, 2019

Jorge Poveda Department of Electrical, Computer, and Energy Engineering; University of Colorado Boulder Real-Time Optimization with Robustness and Acceleration via Hybrid Dynamical Systems and Averaging Theory In this talk we will discuss robust and accelerated zero-order algorithms for the solution of black-box optimization problems in dynamical systems. In particular, we...

Stats, Optimization, and Machine Learning Seminar - Bo Waggoner

Sept. 3, 2019

Bo Waggoner Department of Computer Science, University of Colorado Boulder Toward a Characterization of Loss Functions for Distribution Learning A common machine-learning task is to learn a probability distribution over a very large domain. Examples include natural language processing and generative adversarial networks. But how should the learned distribution be...

Stats, Optimization, and Machine Learning Seminar - Sidney D'Mello

April 23, 2019

Project Tesserae: Longitudinal Multimodal Modeling of Individuals in Naturalistic Contexts I will describe our team’s efforts on a two-year Intelligence Advanced Research Projects Activity (IARPA) program called MOSAIC - Multimodal Objective Sensing to Assess Individuals with Context. The program’s ambitious aims are to “advance multimodal sensing to measure personnel and...

Stats, Optimization, and Machine Learning Seminar - John Pearson

April 16, 2019

Modeling Real Behavior in Two-Person Differential Games In the behavioral sciences, games and game theory have long been the tools of choice for studying strategic behavior. However, the most commonly studied games involve only small numbers of discrete choices and well-defined rounds, while real-world strategic behaviors are continuous and extended...

Stats, Optimization, and Machine Learning Seminar - Anshumali Shrivastava

March 12, 2019

Hashing Algorithms for Extreme Scale Machine Learning In this talk, I will discuss some of my recent and surprising findings on the use of hashing algorithms for large-scale estimations. Locality Sensitive Hashing (LSH) is a hugely popular algorithm for sub-linear near neighbor search. However, it turns out that fundamentally LSH...

Stats, Optimization, and Machine Learning Seminar - Shuang Li

March 5, 2019

Optimization for High-dimensional Analysis and Estimation High-dimensional signal analysis and estimation appear in many signal processing applications, including modal analysis, and parameter estimation in the spectrally sparse signals. The underlying low-dimensional structure in these high-dimensional signals inspires us to develop optimization-based techniques and theoretical guarantees for the above fundamental problems...

Stats, Optimization, and Machine Learning Seminar - Philip Kragel

Feb. 26, 2019

Detecting emotional situations using convolutional neural networks and distributed models of human brain activity Emotions are thought to be canonical responses to situations ancestrally linked to survival or the well-being of an organism. Although sensory elements do not fully determine the nature of emotional responses, they should be sufficient to...

Stats, Optimization, and Machine Learning Seminar - Osman Malik and Ann-Casey Hughes

Feb. 5, 2019

Osman Malik - Fast Randomized Matrix and Tensor Interpolative Decomposition Using CountSketch In this talk I will present our recently developed fast randomized algorithm for matrix interpolative decomposition. If time permits, I will also say a few words about how our method can be applied to the tensor interpolative decomposition...

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