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Tuesday, September 5, 2017Seminar 217, Risk Management: Sparse Low Rank Dictionary LearningSeminar: Risk Seminar  September 5  11 a.m.1 p.m.  639 Evans Hall Speaker: Robert Anderson, UC Berkeley Center for Risk Management Research Sparse Dictionary Learning (SDL) can be used to extract narrow factors driving stock returns from a stock returns matrix, provided the returns are generated by sparse factors alone. We describe progress on a variant called Sparse Low Rank Dictionary Learning (SLRDL), designed to simultaneously extract broad and narrow factors for the returns matrix, when the returns are generated by both types... More > Wednesday, September 6, 2017Sharp threshold for K4percolationSeminar: Probability Seminar  September 6  3:104 p.m.  1011 Evans Hall Brett Kolesnik, UC Berkeley Graph bootstrap percolation is a cellular automaton introduced by Bollobas. Let H be a graph. Edges are added to an initial graph G=(V,E) if they are in a copy of H minus an edge, until no further edges can be added. If eventually the complete graph on V is obtained, G is said to Hpercolate. We identify the sharp threshold for K4percolation on the ErdosRenyi graph G(n,p)... More > Stochastic FirstOrder Methods in Data Analysis and Reinforcement LearningSeminar: Neyman Seminar  September 6  45 p.m.  1011 Evans Hall Mengdi Wang, Princeton University Stochastic firstorder methods provide a basic algorithmic tool for online learning and data analysis. In this talk, we survey several innovative applications including riskaverse optimization, online principal component analysis, dynamic network partition, Markov decision problems and reinforcement learning. We will show that convergence analysis of the stochastic optimization algorithms... More > Center for Computational Biology Seminar: Dr. Robert Murphy, Professor and Head of Computational Biology and Professor of Biological Sciences, Biomedical Engineering and Machine Learning, Carnegie Mellon UniversitySeminar: Other Related Seminars  September 6  4:305:30 p.m.  125 Li Ka Shing Center Center for Computational Biology Integrating Information from Diverse Microscope Images: Learning and Using Generative Models of Cell Organization Thursday, September 7, 2017LaTeX: Creating Tables, Figures, and BibliographiesWorkshop: Other Related Seminars  September 7  45 p.m.  Bechtel Engineering Center, Kresge Engineering Library Training Room 110MD Samantha Teplitzky, Earth and Physical Sciences Librarian, Kresge Engineering Library This workshop will focus on how to add elements to a LaTeX document. Attendees will learn about various packages and syntax that enables the creation of tables, figures, and bibliographies. Registration opens August 4. Register online, or by calling Samantha Teplitzky at 5106447158, or by emailing Samantha Teplitzky at steplitz@berkeley.edu. Monday, September 11, 2017Yong Zeng — NSF Funding Opportunities Related to Data ScienceSeminar: News and Events  September 11  3:305 p.m.  3108 Etcheverry Hall Yong Zeng, National Science Foundation Industrial Engineering & Operations Research This presentation will provide an overview of the funding opportunities related to data science in National Science Foundation. The funding opportunities will include those in the directorates of Computer & Information Science & Engineering (CISE), Engineering (ENG), and Mathematical and Physical Sciences (MPS), and the focus will be those supported by Division of Mathematical Sciences (DMS) in MPS. Tuesday, September 12, 2017Seminar 217, Risk Management: Social Finance and the Postmodern Portfolio: Theory and PracticeSeminar: Risk Seminar  September 12  11 a.m.1 p.m.  639 Evans Hall Speaker: Jeremy Evnine, Evnine & Associates Center for Risk Management Research We formulate the portfolio construction problem as a mean/variance problem which includes a linear term representing an investor’s preference for expected “social return”, in addition to her expected “financial return” of the classical theory. By making various assumptions, we are able to exploit the heterogeneous expectations version of the CAPM to derive an equilibrium model which is an... More > Wednesday, September 13, 2017Matrix Concentration for Expander WalksSeminar: Probability Seminar  September 13  3:104 p.m.  1011 Evans Hall Nikhil Srivastava, UC Berkeley We prove a Chernofftype bound for sums of matrixvalued random variables sampled via a random walk on a Markov chain with spectral gap, confirming a conjecture of Wigderson and Xiao up to logarithmic factors in the deviation parameter. Our proof is based on a recent multimatrix extension of the GoldenThompson inequality due to Sutter et al. discovered in the context of quantum information... More > Phase transitions in random constraint satisfaction problemsSeminar: Neyman Seminar  September 13  45 p.m.  1011 Evans Hall Nike Sun, University of California, Berkeley We will discuss a class of random constraint satisfaction problems (CSPs), including the boolean ksatisfiability (kSAT) problem. For numerous random CSP models, heuristic methods from statistical physics yield detailed predictions on phase transitions and other phenomena. We will survey some of these predictions and describe some progress in the development of mathematical theory for these... More > Tuesday, September 19, 2017Seminar 217, Risk Management: Changepoint detection for stochastic processesSeminar: Risk Seminar  September 19  11 a.m.1 p.m.  639 Evans Hall Speaker: Sveinn Olafsson, Visiting Assistant Professor, UC Santa Barbara Center for Risk Management Research Since the work of Page in the 1950s, the problem of detecting an abrupt change in the distribution of stochastic processes has received a great deal of attention. There are two main formulations of such problems: A Bayesian approach where the changepoint is assumed to be random, and a minmax approach under which the changepoint is assumed to be fixed but unknown. In both cases, a deep... More > Wednesday, September 20, 2017ParkingSeminar: Probability Seminar  September 20  3:104 p.m.  1011 Evans Hall Matthew Junge, Duke University Parking functions were introduced by combinatorialists in the 1960s, and have recently been studied by probabilists. When the parking lot is an infinite graph and cars drive around at random, we will look at how many parking spots are needed for every car to eventually find a spot. Joint work with Michael Damron, Janko Gravner, Hanbeck Lyu, and David Sivakoff. Adaptation via convex optimization in two nonparametric estimation problemsSeminar: Neyman Seminar  September 20  45 p.m.  1011 Evans Hall Adityanand Guntuboyina, University of California, Berkeley We study two convex optimization based procedures for nonparametric function estimation: trend filtering (or higher order total variation denoising) and the KieferWolfowitz MLE for Gaussian location mixtures. Trend filtering can be seen as a technique for fitting splinelike functions for nonparametric regression with adaptive knot selection. It can also be seen as a special case of LASSO for a... More > Friday, September 22, 2017Jacobs Design Conversations: Eric Rodenbeck, "Telling Stories with Data"Lecture: Other Related Seminars  September 22  121 p.m.  310 Jacobs Hall Jacobs Institute for Design Innovation Stamen founder, CEO, and creative director Eric Rodenbeck will speak at Jacobs Hall as part of the Jacobs Design Conversations series. All Audiences All Audiences Tuesday, September 26, 2017Seminar 217, Risk Management:Seminar: Risk Seminar  September 26  11 a.m.1 p.m.  639 Evans Hall Speaker: Ben Gum, AXA Rosenberg Center for Risk Management Research Life 3.0: Being Human in the Age of Artificial Intelligence: A talk by Max TegmarkLecture: Other Related Seminars  September 26  3:304:30 p.m.  Soda Hall, 4308 Wozniak Lounge Max Tegmark, Massachusetts Institute of Technology Electrical Engineering and Computer Sciences (EECS) How can we grow our prosperity through automation without leaving people lacking income or purpose? What career advice should we give today’s kids? How can we make future AI systems more robust, so that they do what we want without crashing, malfunctioning or getting hacked? Should we fear an arms race in lethal autonomous weapons? Will machines eventually outsmart us at all tasks, replacing... More > Staff, Students  Graduate, Students  Undergraduate All Audiences Wednesday, September 27, 2017Negative Dependence and Sampling in Machine LearningSeminar: Neyman Seminar  September 27  45 p.m.  1011 Evans Hall Stefanie Jegelka, Massachusetts Institute of Technology Discrete Probability distributions with strong negative dependencies (negative association) occur in a wide range of settings in Machine Learning, from probabilistic modeling to randomized algorithms for accelerating a variety of popular ML models. In addition, these distributions enjoy rich theoretical connections and properties. A prominent example are Determinantal Point Processes. 

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