Reading material for the class will be assigned from:
| 36-755, Fall 2016 Class Schedule | Date | Lecture Topic | Readings | Scribe Notes | Notes |
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| Aug 28, M | Introduction: high-dimensional statistical models |
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| Aug 30, M | Sub-Gaussian variables |
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| Sep 6, W | Sub-Gaussian variables. Hoeffding bounds and sharpening. Sub-exponential variables. |
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HW1 is out | |
| Sep 11, M | Sub-exponential variables, Bernstein inequality, expected value of maxima. |
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| Sep 13, W | The bounded difference inequality and applications. |
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| Sep 18, M | Concentration of Lipschitz functions of Gaussian vectors. Covering and Packing numbers. |
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| Sep 20, W | Discretization argument. Review of matrix alegbra. Estimation of the covriance matrix in operator norm. |
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HW2 is out | |
| Sep 25, M | Matrix Calculus and Matrix Bernetsin Inequality |
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| Sep 27, W | Matrix Bernstein Inequality and application to covariance matrix estimation. |
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| Oct 2, M | Matrix Bernstein Inequality and its use in network community recovery. Linear regression. |
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| Oct 4, W | Finite sample performance for Linear regression. Penalized regression. |
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HW3 is out | |
| Oct 9, M | Slow rate for the LASSO. The RE condition. |
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| Oct 11, W | More on ridge regression and thresholding estimation. Fast rates for the lasso. |
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| Oct 16, M | Oracle inequalities for least squares and the lasso. |
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| Oct 18, W | Persistence. Intro to PCA. |
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HW4 is out | |
| Oct 23, M | Distance between linear sub-spaces and Davis Kahan theorem. |
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| Oct 25, W | PCA in high-dimensions. Spiked covariance model. Sparse PCA. |
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| Oct 30, M | Sparse PCA. Spectral clustering for SBMs. | |||
| Nov 1, W | Uniform law of larger numbers. Symmetrization Lemma. |
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HW5 is out | |
| Nov 6, M | No Class. | |||
| Nov 8, W | Uniform law of larger numbers. Symmetrization Lemma. |
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| Nov 13, M | VC theory. |
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| Nov 15, W | VC theory for functions. |
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HW6 is out | |
| Nov 20, M | Maximal Inequalities for Sub-Gaussian Processes. |
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| Nov 27, M | Chaining and Dudley's entropy integral. |
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