ntienvu

KnownOptimum_BO

Release code for ICML2020 Knowing The What But Not The Where in Bayesian Optimization

MiniBO

Mini Bayesian Optimization package for ACML2020 Tutorial on Bayesian Optimization

abnormal_detection_video_surveillance

Source code for abnormal detection on MIT video surveillance dataset using Nonnegative Matrix Factorization

BOIL

Release code for Bayesian Optimization for Iterative Learning (BOIL) at NeurIPS2020

ICDM2017_FBO

Filtering Bayesian Optimization (FBO) in Weakly Specified Search Space

KWN

KWN Modeling for Increased Efficiency of Al-Sc Precipitation Strengthening

TW_NAS

Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search at ICML2021

NonparametricBudgetedSGD

Matlab code for Nonparametric Budgeted SGD for classification and regression (AISTATS 2016)

CoCaBO_code

Bayesian Optimisation over Multiple Continuous and Categorical Inputs (CoCaBO)

ICDM2016_OLR

Released code for ICDM 2016 One-pass Logistic Regression

ICDM2019_PVRS

awesome-rl

Reinforcement learning resources curated

bayes-non-parametric-tutorial

An interactive introduction to bayesian non-parametrics

bbo_challenge_starter_kit

Starter kit for the black box optimization challenge at Neurips 2020

handful-of-trials

Experiment code for "Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models"

ibp_vi

VI implementation for inference of the IBP

NPBCL

Bayesian Structure Adaptation for Continual Learning

ntienvu.github.io

Teaching

Stuff for educational purposes, mainly machine learning, Python and statistics

tvo

Code for the Thermodynamic Variational Objective

tvo_gp_bandit

Code for the "Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective" at NeurIPS20

awesome-github-profile-readme

😎 A curated list of awesome GitHub Profile READMEs 📝

CauseBox

Causal inference is a critical task in various fields such as healthcare,economics, marketing and education. Recently, there have beensignificant advances through the application of machine learningtechniques, especially deep neural networks. Unfortunately, to-datemany of the proposed methods are evaluated on different (data,software/hardware, hyperparameter) setups and consequently it isnearly impossible to compare the efficacy of the available methodsor reproduce results presented in original research manuscripts.In this paper, we propose a causal inference toolbox (CauseBox)that addresses the aforementioned problems. At the time of thewriting, the toolbox includes seven state of the art causal inferencemethods and two benchmark datasets. By providing convenientcommand-line and GUI-based interfaces, theCauseBoxtoolboxhelps researchers fairly compare the state of the art methods intheir chosen application context against benchmark datasets.

Deep-Learning-for-Causal-Inference

Extensive tutorials for learning how to build deep learning models for causal inference (HTE) using selection on observables in Tensorflow 2.

diffusion_models

A series of tutorial notebooks on denoising diffusion probabilistic models in PyTorch

drl_for_quantum_measurement

Deep Reinforcement Learning for Efficient Measurement of Quantum Devices

mml-book.github.io

Companion webpage to the book "Mathematics For Machine Learning"

reflected_sigmoid_lr_schedule

learning rate schedule using reflected Sigmoid -- 2nd winner at AutoML competition 2022

scientific-visualization-book

An open access book on scientific visualization using python and matplotlib

tensor-house

A collection of reference machine learning and optimization models for enterprise operations: marketing, pricing, supply chain