Stats, Optimization, and Machine Learning Seminar - Mohsen Imani

Nov. 5, 2019

Mohsen Imadi; Department of Computer Science and Engineering; University of California, San Diego Towards Learning with Brain Efficiency Modern computing systems are plagued with significant issues in efficiently performing learning tasks. In this talk, I will present a new brain-inspired computing architecture. It supports a wide range of learning tasks...

Stats, Optimization, and Machine Learning Seminar - Alec Dunton

Oct. 22, 2019

Alec Dunton, Department of Applied Mathematics, University of Colorado Boulder Learning a kernel matrix for nonlinear dimensionality reduction (Weinberger et. al. 2004) We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. Noting that the kernel matrix implicitly maps...

Stats, Optimization, and Machine Learning Seminar - Purnendu

Oct. 15, 2019

Purnendu, ATLAS Institute, University of Colorado Boulder The mathematical secrets of Computational Origami Origami is the Japanese name for the centuries-old art of folding paper into representations of birds, insects, animals, plants, human figures, inanimate objects, and abstract shapes. In the purest form of origami, the figure is folded from...

Stats, Optimization, and Machine Learning Seminar - Yury Makarychev

Oct. 1, 2019

Yury Makarychev Toyota Technological Institute at Chicago (TTIC) Performance of Johnson-Lindenstrauss Transform for k-Means and k-Medians Clustering Consider an instance of Euclidean k-means or k-medians clustering. We show that the cost of the optimal solution is preserved up to a factor of (1+ε) under a projection onto a random O(log(k/ε)/ε^2)-dimensional...

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...

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