Causal discovery from observational and interventional data across multiple environments

A Li, A Jaber, E Bareinboim - Advances in Neural …, 2023 - proceedings.neurips.cc
A fundamental problem in many sciences is the learning of causal structure underlying a
system, typically through observation and experimentation. Commonly, one even collects …

Diagnosing epilepsy with normal interictal EEG using dynamic network models

P Myers, KM Gunnarsdottir, A Li… - Annals of …, 2023 - Wiley Online Library
Objective Whereas a scalp electroencephalogram (EEG) is important for diagnosing
epilepsy, a single routine EEG is limited in its diagnostic value. Only a small percentage of …

The Effects of Negative Regulation on the Dynamical Transition in Epileptic Network

S Hou, H Wang, D Fan, Y Yu, Q Wang - International Journal of …, 2024 - World Scientific
The transiting mechanism of abnormal brain functional activities, such as the epileptic
seizures, has not been fully elucidated. In this study, we employ a probabilistic neural …

Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks

A Li, R Perry, C Huynh, TM Tomita, R Mehta… - arXiv preprint arXiv …, 2019 - arxiv.org
Decision forests (Forests), in particular random forests and gradient boosting trees, have
demonstrated state-of-the-art accuracy compared to other methods in many supervised …

[PDF][PDF] Disentangled Representation Learning in Non-Markovian Causal Systems

A Li, Y Pan, E Bareinboim - causalai.net
Considering various data modalities, such as images, videos, and text, humans perform
causal reasoning using high-level causal variables, as opposed to operating at the low …

[PDF][PDF] Characterizing and learning multi-domain causal structures from observational and experimental data

A Li, A Jaber, E Bareinboim - 2023 - causalai.net
A fundamental problem throughout the sciences is the learning of causal structure
underlying a system, by combining passive observations and active experimentation …

Manifold oblique random forests: Towards closing the gap on convolutional deep networks

A Li, R Perry, C Huynh, TM Tomita, R Mehta… - SIAM Journal on …, 2023 - SIAM
Decision forests, in particular random forests and gradient boosting trees have
demonstrated state-of-the-art accuracy compared to other methods in many supervised …