PPT-Risk measure preserving piecewise linear approximation of empirical distributions

William GuevaraAlarcón 1 joint work with Philipp Arbenz 2 1 Université de Lausanne 2 SCOR 2 nd International Congress on Actuarial Science and Quantitative Finance ICASQF June 2016. Mohammed Nasser. Department of Statistics. 2. Relation between Statistics and Differentiation. Statistical Concepts/Techniques. Use of Differentiation Theory. Study of shapes of univariate pdfs. An easy application of first-order and second-order derivatives. Geetika Tewari, Harvard University. John Snyder, Microsoft Research . Pedro V. Sander, ATI Research. Steven J. Gortler, Harvard University. Hugues Hoppe, Microsoft Research. Two Scenarios. Authoring: map a texture image onto a surface. Sublinear. Statistics. Paul Valiant. Fisher’s Butterflies. Turing’s Enigma . Codewords. How many new species if I observe for another period?. Probability mass of unseen . codewords. +. +. +. +. Measure description:. The . Government will introduce a specific measure preventing the distribution of franking credits where a distribution to shareholders is funded by particular capital raising activities. . Specialization of Kernelization. Daniel Lokshtanov. We. Kernels. ∃. ¬. . Kernels. Why?. What’s Wrong with Kernels. (from a practitioners point of view). Only handles . NP-hard. problems.. Don’t combine well with . . for Binary . Pairwise. Energies. Lena . Gorelick. . joint work with. . O. . Veksler. I. Ben . Ayed. A. Delong. . Y. . Boykov. Example of Simple Binary Energy. 2. Potts Model. Binary . Pairwise. Define appropriate quantities from a situation, choose and interpret the scale and the origin for the graph, and graph the piecewise linear function.. Learning Goal . 2 . (HS.N-Q.A.1, 2, 3):. The student will be able to use units to solve multi-step contextual problems. How accurate is your estimate?. Differential Notation. The Linear Approximation to . y. = . f. (. x. ) is often written using the “differentials” . dx. and . dy. . In this notation, . dx. is used instead of . Geetika Tewari, Harvard University. John Snyder, Microsoft Research . Pedro V. Sander, ATI Research. Steven J. Gortler, Harvard University. Hugues Hoppe, Microsoft Research. Two Scenarios. Authoring: map a texture image onto a surface. machine learning. Yuchen Zhang. Stanford University. Non-convexity . in . modern machine learning. 2. State-of-the-art AI models are learnt by minimizing (often non-convex) loss functions.. T. raditional . Math 200. Week 6 - Friday. Math 200. Goals. Be able to compute the . local linear approximation. for a function of two or more variables at a given point.. Be able to use a local linear approximation to estimate a given quantity.. Define appropriate quantities from a situation, choose and interpret the scale and the origin for the graph, and graph the piecewise linear function.. Learning Goal . 2 . (HS.N-Q.A.1, 2, 3):. The student will be able to use units to solve multi-step contextual problems. What is a piecewise function?. A piecewise function . (passes vertical line test) . is a function that is made up of different graphs for different intervals.. “Frankenstein Functions”. Students will be able to Identify and Graph piecewise functions (including absolute value) as evidenced by a math journal.. Pravesh Kothari, . Divyarthi Mohan,. Ariel Schvartzman, . Sahil Singla, S. Matthew Weinberg. FOCS 2019. How to Maximize Revenue?. Selling a Single Item. ~ .  . v(. ⚽. ). v(. ⚽. )= . x. Truthful Mechanism.