ntienvu

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

BOIL

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

ICDM2016_B3O

Released code for ICDM 2016 Budgeted Batch Bayesian Optimization

ICDM2017_FBO

Filtering Bayesian Optimization (FBO) in Weakly Specified Search Space

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

MiniBO

Mini Bayesian Optimization package for ACML2020 Tutorial on Bayesian Optimization

NonparametricBudgetedSGD

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

TW_NAS

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

ntienvu/KnownOptimum_BO

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

ntienvu/MiniBO

Mini Bayesian Optimization package for ACML2020 Tutorial on Bayesian Optimization

ntienvu/abnormal_detection_video_surveillance

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

ntienvu/BOIL

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

ntienvu/ICDM2017_FBO

Filtering Bayesian Optimization (FBO) in Weakly Specified Search Space

ntienvu/KWN

KWN Modeling for Increased Efficiency of Al-Sc Precipitation Strengthening

ntienvu/TW_NAS

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

ntienvu/NonparametricBudgetedSGD

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

ntienvu/CoCaBO_code

Bayesian Optimisation over Multiple Continuous and Categorical Inputs (CoCaBO)

ntienvu/ICDM2016_OLR

Released code for ICDM 2016 One-pass Logistic Regression

ntienvu/ICDM2019_PVRS

ntienvu/awesome-rl

Reinforcement learning resources curated

ntienvu/bayes-non-parametric-tutorial

An interactive introduction to bayesian non-parametrics

ntienvu/bbo_challenge_starter_kit

Starter kit for the black box optimization challenge at Neurips 2020

ntienvu/handful-of-trials

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

ntienvu/ibp_vi

VI implementation for inference of the IBP

ntienvu/NPBCL

Bayesian Structure Adaptation for Continual Learning

ntienvu/ntienvu.github.io

ntienvu/Teaching

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

ntienvu/tvo

Code for the Thermodynamic Variational Objective

ntienvu/tvo_gp_bandit

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

ntienvu/awesome-github-profile-readme

😎 A curated list of awesome GitHub Profile READMEs 📝

ntienvu/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.

ntienvu/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.

ntienvu/diffusion_models

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

ntienvu/drl_for_quantum_measurement

Deep Reinforcement Learning for Efficient Measurement of Quantum Devices

ntienvu/mml-book.github.io

Companion webpage to the book "Mathematics For Machine Learning"

ntienvu/reflected_sigmoid_lr_schedule

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

ntienvu/scientific-visualization-book

An open access book on scientific visualization using python and matplotlib

ntienvu/tensor-house

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