10 citations · 21 across the 10 of their papers we have counts for
10 papers
Reinforcement Learning to Discover a North-East Monsoon Index for Rainfall Prediction in Thailand
Kiattikun Chobtham
Accurately predicting long-term rainfall is challenging. Global climate indices, such as the El Niño-Southern Oscillation, are standard input features for machine learning. However…
Leveraging Teleconnections with Physics-Informed Graph Attention Networks for Long-Range Extreme Rainfall Forecasting in Thailand
Kiattikun Chobtham, Kanoksri Sarinnapakorn, Kritanai Torsri +2
Accurate rainfall forecasting, particularly for extreme events, remains a significant challenge in climatology and the Earth system. This paper presents novel physics-informed Grap…
Tuning structure learning algorithms with out-of-sample and resampling strategies
Kiattikun Chobtham, Anthony C. Constantinou
One of the challenges practitioners face when applying structure learning algorithms to their data involves determining a set of hyperparameters; otherwise, a set of hyperparameter…
Open problems in causal structure learning: A case study of COVID-19 in the UK
Anthony Constantinou, Neville K. Kitson, Yang Liu +5
Causal machine learning (ML) algorithms recover graphical structures that tell us something about cause-and-effect relationships. The causal representation praovided by these algor…
Discovery and density estimation of latent confounders in Bayesian networks with evidence lower bound
Kiattikun Chobtham, Anthony C. Constantinou
Discovering and parameterising latent confounders represent important and challenging problems in causal structure learning and density estimation respectively. In this paper, we f…
Hybrid Bayesian network discovery with latent variables by scoring multiple interventions
Kiattikun Chobtham, Anthony C. Constantinou, Neville K. Kitson
In Bayesian Networks (BNs), the direction of edges is crucial for causal reasoning and inference. However, Markov equivalence class considerations mean it is not always possible to…
Effective and efficient structure learning with pruning and model averaging strategies
Anthony C. Constantinou, Yang Liu, Neville K. Kitson +2
Learning the structure of a Bayesian Network (BN) with score-based solutions involves exploring the search space of possible graphs and moving towards the graph that maximises a gi…
A survey of Bayesian Network structure learning
Neville K. Kitson, Anthony C. Constantinou, Zhigao Guo +2
Bayesian Networks (BNs) have become increasingly popular over the last few decades as a tool for reasoning under uncertainty in fields as diverse as medicine, biology, epidemiology…
Bayesian network structure learning with causal effects in the presence of latent variables
Kiattikun Chobtham, Anthony C. Constantinou
Latent variables may lead to spurious relationships that can be misinterpreted as causal relationships. In Bayesian Networks (BNs), this challenge is known as learning under causal…
Large-scale empirical validation of Bayesian Network structure learning algorithms with noisy data
Anthony C. Constantinou, Yang Liu, Kiattikun Chobtham +2
Numerous Bayesian Network (BN) structure learning algorithms have been proposed in the literature over the past few decades. Each publication makes an empirical or theoretical case…