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abnormal_detection_pca

Abnormality Detection using PCA approach

abnormal_detection_video_surveillance

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

acml2016_bnmc

Source code for Bayesian Nonparametric Multi-label Classification ACML 2016

awesome-github-profile-readme

😎 A curated list of awesome GitHub Profile READMEs 📝

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

boil

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

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.

cocabo_code

Bayesian Optimisation over Multiple Continuous and Categorical Inputs (CoCaBO)

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.

deep_reinforcement_learning_course

Implementations from the free course Deep Reinforcement Learning with Tensorflow

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

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

icdm2016_b3o

Released code for ICDM 2016 Budgeted Batch Bayesian Optimization

icdm2016_olr

Released code for ICDM 2016 One-pass Logistic Regression

icdm2017_fbo

Filtering Bayesian Optimization (FBO) in Weakly Specified Search Space

icdm2019_pvrs

knownoptimum_bo

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

kwn

KWN Modeling for Increased Efficiency of Al-Sc Precipitation Strengthening

largesampleasymptotic_scalablenonparametricclustering

LargeSampleAsymptotic_BNP_NIPS_AABI_2015

machine_learning_examples

A collection of machine learning examples and tutorials.

minibo

Mini Bayesian Optimization package for ACML2020 Tutorial on Bayesian Optimization

mml-book.github.io

Companion webpage to the book "Mathematics For Machine Learning"

nonparametricbudgetedsgd

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

npbcl

Bayesian Structure Adaptation for Continual Learning

ntienvu.github.io

optimviz

Visualize optimization algorithms in MATLAB.