![]() Moreover, we will talk about the Nystrom method for generating low-rank approximations of kernel matrices that arise in many machine learning problems. ![]() Specifically, we will present a randomized algorithm for K-means clustering in the one-pass streaming setting that does not require incoherence or distributional assumptions on the data. In this talk, we will focus on two important topics in modern data analysis: (1) K-means clustering and (2) low-rank approximation of kernel matrices for analyzing datasets with highly complex and nonlinear structures. ![]() However, such methods require strong theoretical understanding to ensure that the key properties of original data are preserved. In particular, randomized dimensionality reduction techniques are effective in modern data settings since they provide a non-adaptive data-independent mapping of high-dimensional datasets into a lower dimensional space. Randomization and probabilistic techniques have become fundamental tools in modern data science and machine learning for analyzing large-scale datasets. The need to process large-scale datasets by memory and computation efficient algorithms arises in all fields of science and engineering. 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. TITLE: New Directions in Randomized Dimension Reduction for Modern Data Analysis SPEAKER: Farhad Pourkamali-Anaraki Postdoctoral Research Associate of Applied Mathematics at the University of Colorado-Boulder May 1, Anna Broido (CU-Boulder), "Scale-free networks are rare".Apr 24, Tracy Babb (CU-Boulder), Paper presentation of "Practical sketching algorithms for low-rank matrix approximation".Apr 17, NO TALK (New Stat Major Open House at our usual time (3:30 PM Newton Lab)). ![]() Apr 10, Nathaniel Mathews (CU-Boulder), Discussion of "Constrained Global Optimization of Expensive Black Box Functions Using Radial Basis Functions".Apr 3, Jean-Gabriel Young (Universite Laval), "Network archeology: phase transition in the recoverability of network history".Mar 21, Luca Trevisan (Berkeley), Bonus talk: "A Theory of Spectral Clustering".Mar 20, Ali Mousavi (Rice), "Data-Driven Computational Sensing".Mar 13, Mark Bun (Princeton), "Finding Structure in the Landscape of Differential Privacy".Mar 6, Antonio Blanca (Georgia Tech), "Efficient Sampling for Probabilistic Models".Feb 27, Michael Hughes (Harvard), "Discovering Disease Subtypes that Improve Treatment Predictions: Interpretable Machine Learning for Personalized Medicine".Feb 22, Genevieve Patterson (Microsoft Research), "Uncommon Sense: Using Neural Networks for Exploration and Creativity".Feb 13, Peter Shaffery (CU-Boulder), Presenting Simmons, Nelson, and Simonsohn's 2011 article "False Positive Psychology".Feb 6, Dan Zhang (CU-Boulder), Some Recent Results on Linear Programming Based Approximate Dynamic Programming.Jan 30, David Kozak (CO School of Mines), "Global Convergence of Online Limited Memory BFGS".Jan 23, Farhad Pourkamali-Anaraki (CU-Boulder), "New Directions in Randomized Dimension Reduction for Modern Data Analysis".This paper is based on the fundamental claim that one of the major roles of new media is not to deliver predigested information to individuals, but to provide the opportunity and resources for social debate and discussion. #Dlc collaboratory cu boulder software#įor most design problems (ranging from urban design to graphics design and software design) that we have studied over many years, the knowledge to understand, frame, and solve these problems does not exist, but is constructed and evolved during the process of solving them, exploiting the power of the "symmetry of ignorance" and "breakdowns." From this perspective, access to existing information and knowledge (often seen as the major advance of new media) is a very limiting concept. Many social and technological innovations are limited to provide primarily better access, leading to "consumer" cultures. Our approach focuses and creates support for lifelong learning activities grounded in informed participation and empowerment, allowing learners to incrementally acquire ownership in problems and contribute actively to their solution.To illustrate our approach, we present the Envisionment and Discovery Collaboratory (EDC), an integrated physical and computational environment supporting informed participation through new forms of knowledge creation, integration, and dissemination. The EDC empowers users to act as designers in situated learning and collaborative problem-solving activities. #Dlc collaboratory cu boulder software#.
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