biaohe Goto Github PK

ksim

max-value-entropy-search

Max-value Entropy Search for Efficient Bayesian Optimization

max_entropy_evolution_policy_search

Implementation of max-entropy evolution policy search.

mediumarticles

A repository for all the code that is mentioned in my Medium articles

mesmo

Matlab implementation of the Max-value Entropy Search for Multi-Objective Bayesian Optimization

mesmoc

Python implementation for MESMOC the paper "Max-value Entropy Search for Multi-Objective Bayesian Optimization with Constraints".

mf-dkl-python-implementation

mf2

Collection of Multi-Fidelity benchmark functions

mfes

Multi-Fidelity Entropy Search (MF-ES) -- A Matlab toolbox for including the robot simulator in the learning loop

mobo

constrained/unconstrained multi-objective bayesian optimization package.

mobopt

Multi-objective Bayesian optimization

moead-py

A Python implementation of the decomposition based multi-objective evolutionary algorithm (MOEA/D)

monn

MONN: a Multi-Objective Neural Network for Predicting Pairwise Non-Covalent Interactions and Binding Affinities between Compounds and Proteins

multiobj-rationale

Multi-Objective Molecule Generation using Interpretable Substructures (ICML 2020)

multiobjectiveoptimization

Source code for Neural Information Processing Systems (NeurIPS) 2018 paper "Multi-Task Learning as Multi-Objective Optimization"

multitask-learning

Awesome Multitask Learning Resources

mygpyopt

Implementing Probability of Feasibility (PF) handling of black-box c_i(x)<=0. Based on GPyopt

myopt

a lib for optimization

nasbot

Neural Architecture Search with Bayesian Optimisation and Optimal Transport

neural-network

Multilayer neural network framework implementation, used for classification and regression task. Can use multiple activation functions with backpropagation based on autograd library. Contains polynomial activation function for regression task.

nni

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

noisyinputentropysearch

This is the companion code for the paper Noisy-Input Entropy Search for Efficient Robust Bayesian Optimization by Lukas P. Fröhlich et al., AISTATS 2020

open-box

Generalized and Efficient Blackbox Optimization System.

parallelsparsematrixfactorization

Sparse Matrix Factorization (SMF) is a key component in many machine learning problems and there exist a verity a applications in real-world problems such as recommendation systems, estimating missing values, gene expression modeling, intelligent tutoring systems (ITSs), etc. There are different approaches to tackle with SMF rooted in linear algebra and probability theory. In this project, given an incomplete binary matrix of students’ performances over a set of questions, estimating the probability of success or fail over unanswered questions is of interest. This problem is formulated using Maximum Likelihood Estimation (MLE) which leads to a biconvex optimization problem (this formulation is based on SPARFA [4]). The resulting optimization problem is a hard problem to deal with due to the existence of many local minima. On the other hand, when the size of the matrix of students’ performances increase, the existing algorithms are not successful; therefore, an efficient algorithm is required to solve this problem for large matrices. In this project, a parallel algorithm (i.e., a parallel version of SPARFA) is developed to solve the biconvex optimization problem and tested via a number of generated matrices. Keywords: parallel non-convex optimization, matrix factorization, sparse factor analysis 1 Introduction Educational systems have witnessed a substantial transition from traditional educational methods mainly using text books, lectures, etc. to newly developed systems which are artificial intelligent- based systems and personally tailored to the learners [4]. Personalized Learning Systems (PLSs) and Intelligent Tutoring Systems (ITSs) are two more well-known instances of such recently developed educational systems. PLSs take into account learners’ individual characteristics then customize the learning experience to the learners’ current situation and needs [2]. As computerized learning environments, ITSs model and track student learning states [1, 6, 7]. Latent Factor Model and Bayesian Knowledge Tracing are main classes in ITSs [3]. These new approaches encompass computational models from different disciplines including cognitive and learning sciences, education, 1 computational linguistics, artificial intelligence, operations research, and other fields. More details can be found in [1, 4–6]. Recently, [4] developed a new machine learning-based model for learning analytics, which approximate a students knowledge of the concepts underlying a domain, and content analytics, which estimate the relationships among a collection of questions and those concepts. This model calculates the probability that a learner provides the correct response to a question in terms of three factors: their understanding of a set of underlying concepts, the concepts involved in each question, and each questions intrinsic difficulty [4]. They proposed a bi-convex maximum-likelihood-based solution to the resulting SPARse Factor Analysis (SPARFA) problem. However, the scalability of SPARFA when the number of questions and students significantly increase has not been studied yet.

platypus

A Free and Open Source Python Library for Multiobjective Optimization

prada_bo

Multi-objective Bayesian optimisation framework.

predictive_entropy_search

prml

PRML algorithms implemented in Python

pytorch-book

PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation (《深度学习框架PyTorch：入门与实战》)

reinforcement-learning-algorithms-with-python

Reinforcement Learning Algorithms with Python, Published by Packt