Edgar Dobriban

Topics-In-Modern-Statistical-Learning. Materials for STAT 991: Topics In Modern Statistical Learning (UPenn, 2022 Spring) - uncertainty quantification, conformal prediction, calibration, etc

stat-ml-edu. Resources for education in statistics and machine learning: from advanced undergraduate to research level

Topics-in-deep-learning. Materials for class on topics in deep learning (STAT 991, UPenn/Wharton)

Principles-of-AI-LLMs. Materials for the course Principles of AI: LLMs at UPenn (Stat 9911, Spring 2025). LLM architectures, training paradigms (pre- and post-training, alignment), test-time computation, reasoning, safety and robustness (jailbreaking, oversight, uncertainty), representations, interpretability (circuits), etc.

spatial-data-with-r. Materials for my lecture on Spatial Data Analysis with R

EigenEdge. Computing with Eigenvalue Distributions of Large Random Matrices of the Covariance Type

Talks. Slides from my talks

DPA. Deterministic Parallel Analysis

high-dim-risk-experiments. Experiments with high-dimensional predictive risk

Dist. Distributed linear regression

data_aug. Experiments in data augmentation paper

dist_ridge. Distributed ridge

PA. Permutation methods for factor analysis and PCA

pweight. P-value Weighting R Package

pvalue_weighting_matlab. Pvalue Weighting Package for Matlab

Hadamard. Robust Inference Under Heteroskedasticity via the Hadamard Estimator

stat-431-project. Stat 431 final project - Stats for covid-19

pca. resources related to pca

ideal-working-group. Working group for IDEAL special semester on the foundations of deep learning

Spectrode-R. Spectrode in R

diagonally_reduced. Experiments from the paper "PCA from noisy, linearly reduced data: the diagonal case"