dobriban Goto Github PK

adversarial-ml-tutorial-copied

bandits

Python library for Multi-Armed Bandits

contextualbandits

Python implementations of contextual bandits algorithms

curvefit

Generic curve fitting package with nonlinear mixed effects model

data_aug

Experiments in data augmentation paper

deep-learning-with-r-notebooks

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

diagonally_reduced

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

dist

Distributed linear regression

dist_ridge

Distributed ridge

dld

Theories of Deep Learning

dpa

Deterministic Parallel Analysis

eigenedge

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

fact

FACT method for multiple testing

hadamard

Robust Inference Under Heteroskedasticity via the Hadamard Estimator

high-dim-risk-experiments

Experiments with high-dimensional predictive risk

ideal-working-group

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

mingpt

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

pa

Permutation methods for factor analysis and PCA

papers

PDF copies of some of my published papers

pca

resources related to pca

pvalue_weighting_matlab

Pvalue Weighting Package for Matlab

pweight

P-value Weighting R Package

randomization_test

spatial-data-with-r

Materials for my lecture on Spatial Data Analysis with R

spectrode-r

Spectrode in R

stat-431-project

Stat 431 final project - Stats for covid-19

stat-ml-edu

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

talks

Slides from my talks

topics-in-deep-learning

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

topics-in-modern-statistical-learning

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