Symbolic Logic Graph data processing algorithms and their application to Machine Learning and Artificial Intelligence tasks
Theory, implementation, and use of algorithms for analyzing graph data.
最適化:Optimization
Symbolic Logic オンライン学習 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.
オンライン学習 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
オンライン学習 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).
オンライン学習 Protected: Online Convex Optimization (2) Complementing FTL Strategies with Regularization
Complementing the FTL strategy by introducing regularization techniques (L2 norm) in online prediction for digital transformation , artificial intelligence , and machine learning tasks.
オンライン学習 Protected: Online Convex Optimization(1) FTL strategy and BTL supplement
Online Convex Optimization and FTL Strategies with Online Prediction for Digital Transformation , Artificial Intelligence , and Machine Learning Tasks with BTL Supplement
オンライン学習 Protected: New Developments in Reinforcement Learning (2) – Approaches Using Deep Learning
On seven methods for improving deep reinforcement learning used in digital transformation , artificial intelligence , and machine learning tasks (first generation DQN, dual Q learning (dual DQN method), prioritized experience replay, collision Q networks, distributed reinforcement learning (categorical DQN method) noise networks, n-step cutting returns) and alpha zero
オンライン学習 Protected: New Developments in Reinforcement Learning (1) – Reinforcement Learning with Risk Indicators
Different approaches (regular process TD learning, RDPS methods) and implementations (Monte Carlo, analytical methods) in risk-aware reinforcement learning methods for digital transformation , artificial intelligence , and machine learning tasks.
オンライン学習 Protected: Partially Observed Markov Decision Processes (2) Planning POMDPs
Reinforcement learning for digital transformation , artificial intelligence , and machine learning tasks; obtaining optimal strategies using partial observation Markov decision process planning methods.
オンライン学習 Protected: Partially Observed Markov Decision Processes (1) On POMDPs and Belief MDPs
Belief MDPs, more flexible reinforcement learning using partially observed Markov decision processes (POMDPs) for digital transformation , artificial intelligence , and machine learning tasks.