Edgar Dobriban Claim this profile

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.

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

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

Resources for education in statistics and machine learning: from advanced undergraduate to research level

Materials for my lecture on Spatial Data Analysis with R

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

Experiments with Two-sided Gaussian tests for BH

Slides from my talks

Experiments with high-dimensional predictive risk

Deterministic Parallel Analysis

Distributed linear regression

Experiments in data augmentation paper

Distributed ridge

Permutation methods for factor analysis and PCA

P-value Weighting R Package

Robust Inference Under Heteroskedasticity via the Hadamard Estimator

Pvalue Weighting Package for Matlab

Stat 431 final project - Stats for covid-19

Spectrode in R

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

R notebooks for the code samples of the book "Deep Learning with R"

Working group for IDEAL special semester on the foundations of deep learning

resources related to pca

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Student version of Assignment 1 for Stanford CS336 - Language Modeling From Scratch

LivePI: More Realistic Benchmarking of Agents Against Indirect Prompt Injection

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A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training

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Generic curve fitting package with nonlinear mixed effects model

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FACT method for multiple testing

Python library for Multi-Armed Bandits

Python implementations of contextual bandits algorithms

Theories of Deep Learning

PDF copies of some of my published papers