machine learning

Protected: Fundamentals of statistical causal search (3) Causal Markov conditions, faithfulness, PC algorithm, GES algorithm

Causal Markov conditions, fidelity and constraint-based approaches and score-based approaches in the foundations of statistical causal search for digital transformation , artificial intelligence and machine learning tasks.

2022.03.04

Symbolic Logicグラフ理論セマンテックウェブ技術:Semantic web Technology推論技術:inference Technology最適化:Optimization検索技術:Search Technology機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: Fundamentals of Statistical Causal Search (2) Three Approaches Identifiability

Identifiability of three approaches for the basis of statistical causal search for digital transformation, artificial intelligence, and machine learning tasks (matrix representation of structural equation models and directed acyclic graphs, average causal effects).

2022.03.03

グラフ理論推論技術:inference Technology最適化:Optimization検索技術:Search Technology機械学習:Machine Learning確率・統計:Probability and Statistics

Visualization of knowledge graph (relational data) using D3 and React

2D and 3D visualization of knowledge graphs (relational data) and relational heat maps using D3 and React for digital transformation , artificial intelligence , and machine learning tasks.

2022.03.02

Uncategorized

Protected: Fundamentals of statistical causal search (1) Framework of causal search and three approaches to basic problems

A framework for the foundation of statistical causal search for digital transformation , artificial intelligence , and machine learning tasks and three approaches to the basic problem (nonparametric, parametric, and semiparametric approaches).

2022.03.01

推論技術:inference Technology最適化:Optimization確率・統計:Probability and Statistics

Protected: Fundamentals of Statistical Causal Inference (2) – Structural Causal Models and Randomized Experiments

Structural causal models and randomized experiments as a basis for statistical causal inference for digital transformation, artificial intelligence, and machine learning tasks.

2022.02.28

グラフ理論ベイズ推定推論技術:inference Technology最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: Fundamentals of statistical causal inference (1) – Definition of causality by counterfactual model and structural equation model

Foundations of Statistical Causal Inference for Digital Transformation , Artificial Intelligence and Machine Learning Tasks: Defining Causality in Counterfactual Models and Structural Equation Models

2022.02.25

推論技術:inference Technology機械学習:Machine Learning確率・統計:Probability and Statistics

Graph data processing algorithms and their application to Machine Learning and Artificial Intelligence tasks

Theory, implementation, and use of algorithms for analyzing graph data.

2022.02.22

Symbolic Logicアルゴリズム:Algorithmsグラフ理論最適化:Optimization

Protected: Combinatorial online prediction

Combinatorial, online prediction problems with a set of discrete structures as the decision space for digital transformation , artificial intelligence , and machine learning task utilization.

2022.02.15

オンライン学習幾何学:Geometry強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: Online prediction based on randomness

Randomness-based FPL(Follow the Perturbed Leader) Strategy and Gumbel Distribution for Improving Online Predictive Performance for Digital Transformation , Artificial Intelligence and Machine Learning Tasks

2022.02.14

オンライン学習幾何学:Geometry強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: Online convex optimization (3) exp concavity and ONS

Convex optimization for online prediction for digital transformation , artificial intelligence , and machine learning tasks (the case of exp concavity and ONS).

2022.02.10

オンライン学習幾何学:Geometry強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics