syadlowsky

calibration

Code implementing "A Calibration Metric for Risk Scores with Survival Data" (MLHC 2019)

cate-sensitivity-sieve

data-reopening

dotvim

my vim config

growth-rate-estimation

ihtpy

Implementation of iterative hard thresholding in python with the scipy/numpy libraries

omniauth-hackid

See https://github.com/HackBerkeley/omniauth-hackid for the official version.

revised-pooled-ascvd

Code to reproduce the results of "Clinical implications of revised pooled cohort equations for estimating atherosclerotic cardiovascular disease risk"

syadlowsky/revised-pooled-ascvd

Code to reproduce the results of "Clinical implications of revised pooled cohort equations for estimating atherosclerotic cardiovascular disease risk"

syadlowsky/calibration

Code implementing "A Calibration Metric for Risk Scores with Survival Data" (MLHC 2019)

syadlowsky/ihtpy

Implementation of iterative hard thresholding in python with the scipy/numpy libraries

syadlowsky/growth-rate-estimation

syadlowsky/cate-sensitivity-sieve

syadlowsky/data-reopening

syadlowsky/arxiv2bibtex-firefox

syadlowsky/caffe

Caffe: a fast open framework for deep learning.

syadlowsky/causalLearning

Methods for heterogeneous treatment effect estimation

syadlowsky/cifar-fun

syadlowsky/density-estimation

syadlowsky/EconML

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

syadlowsky/emacs-migration

This is a small repo with my emacs migration plan. Hopefully I will update with my success / failure as it goes.

syadlowsky/EquilibriumSolver

A user-equilibrium traffic assignment library.

syadlowsky/grf

Generalized Random Forests

syadlowsky/hte

syadlowsky/hte-prediction-rcts

Predicting treatment effects from RCTs (Circulation: CQO 2019).

syadlowsky/hypertension-classes-iv

Simulation demonstrating concerns with IV analysis in "Incremental effects of antihypertensive drugs: instrumental variable analysis"

syadlowsky/lifelines

Survival analysis in Python

syadlowsky/Memoize.jl

＠memoize macro for Julia

syadlowsky/mirror-descent

Library for simplex constrained optimization via mirror descent

syadlowsky/phi-estimation

syadlowsky/PostgreSQL.jl

PostgreSQL DBI driver

syadlowsky/pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

syadlowsky/robust_probability

syadlowsky/robustopt

syadlowsky/sfg-nejm-sprint

syadlowsky/syadlowsky.github.io

syadlowsky/synthetic-traffic

Synthetic traffic data models for networks, static routing preferences, and sensor placement

syadlowsky/v106

Conference on Machine Learning for Healthcare 2019