Interpretability and explainability: A machine learning zoo mini-tour
R Marcinkevičs, JE Vogt - arXiv preprint arXiv:2012.01805, 2020 - arxiv.org
In this review, we examine the problem of designing interpretable and explainable machine
learning models. Interpretability and explainability lie at the core of many machine learning …
learning models. Interpretability and explainability lie at the core of many machine learning …
Causal machine learning: A survey and open problems
Causal Machine Learning (CausalML) is an umbrella term for machine learning methods
that formalize the data-generation process as a structural causal model (SCM). This …
that formalize the data-generation process as a structural causal model (SCM). This …
Amortized causal discovery: Learning to infer causal graphs from time-series data
On time-series data, most causal discovery methods fit a new model whenever they
encounter samples from a new underlying causal graph. However, these samples often …
encounter samples from a new underlying causal graph. However, these samples often …
Interpretable models for granger causality using self-explaining neural networks
R Marcinkevičs, JE Vogt - arXiv preprint arXiv:2101.07600, 2021 - arxiv.org
Exploratory analysis of time series data can yield a better understanding of complex
dynamical systems. Granger causality is a practical framework for analysing interactions in …
dynamical systems. Granger causality is a practical framework for analysing interactions in …
Rhino: Deep causal temporal relationship learning with history-dependent noise
Discovering causal relationships between different variables from time series data has been
a long-standing challenge for many domains such as climate science, finance, and …
a long-standing challenge for many domains such as climate science, finance, and …
A Review and Roadmap of Deep Causal Model from Different Causal Structures and Representations
The fusion of causal models with deep learning introducing increasingly intricate data sets,
such as the causal associations within images or between textual components, has surfaced …
such as the causal associations within images or between textual components, has surfaced …
Learning interaction rules from multi-animal trajectories via augmented behavioral models
Extracting the interaction rules of biological agents from movement sequences pose
challenges in various domains. Granger causality is a practical framework for analyzing the …
challenges in various domains. Granger causality is a practical framework for analyzing the …
Large-scale kernelized granger causality (lskgc) for inferring topology of directed graphs in brain networks
MA Vosoughi, A Wismüller - Medical Imaging 2022 …, 2022 - spiedigitallibrary.org
Graph topology inference in networks with co-evolving and interacting time-series is crucial
for network studies. Vector autoregressive models (VAR) are popular approaches for …
for network studies. Vector autoregressive models (VAR) are popular approaches for …
Relation discovery in nonlinearly related large-scale settings
Causal inquiries provide crucial insight into the advancement of scientific discoveries. In real-
world studies like climatology, sensory data acquired from nodal measurements are …
world studies like climatology, sensory data acquired from nodal measurements are …
Large-scale kernelized Granger causality to infer topology of directed graphs with applications to brain networks
MA Vosoughi, A Wismuller - arXiv preprint arXiv:2011.08261, 2020 - arxiv.org
Graph topology inference of network processes with co-evolving and interacting time-series
is crucial for network studies. Vector autoregressive models (VAR) are popular approaches …
is crucial for network studies. Vector autoregressive models (VAR) are popular approaches …