Behrad Moniri

  • Bounding the Variance of a Sub-Gaussian Random Variable

    In this note, we will study the variance of sub-gaussian random variables.

    variance.pdfDownload

  • Maximum of Sub-Gaussian Random Variables

    In this note, we will first prove a bound on the expected maxima of a sequence of weighted sub-gaussian random variables. Next, we show an upper bound for the expected value of the maximum of a finite number of sub-gaussian random variables. Finally, we prove a high probability version of these results.
    maximal.pdfDownload

  • Old Slides

    Here is a list of some of my old slides. Most of these were made when I was an undergraduate student or during Covid.

    • Information-Theoretic Analysis of Learning Algorithms [Slides]
    • Non-Parametric Least Squares [Slides]
    • LASSO is not Fully Bayesian [Slides]
    • Online Learning: What is Learnable? [for high school students] [Slides]
    • Subjective Theory of Probability: Dutch Book (de Finetti) Theorem [Slides]
    • Algorithmic Causal Inference [Slides]
    • Online Learning and Online Convex Optimization [with Mahdi Sabbaghi] [Slides]
    • Blind Separation of Nonlinear Mixtures of Stochastic Processes [Part 1][Part 2]
    • Nonlinear ICA of Temporally Dependent Stationary Sources [Slides]

  • Hello World!

    Hello! This is my first blog post. I will be posting about topics in ML theory.

    \mathbb{E} [H_{k_1}(Z_1) H_{k_2}(Z_2)] = k_1! \rho^{k_1} \delta_{k_1, k_2}.


5th Year Ph.D. Student


University of Pennsylvania
Amy Gutmann Hall
Center for Responsible AI
Philadelphia, PA.

Email:

bemoniri@seas.upenn.edu