Quantifying & modeling multimodal interactions: An information decomposition framework
The recent explosion of interest in multimodal applications has resulted in a wide selection
of datasets and methods for representing and integrating information from different …
of datasets and methods for representing and integrating information from different …
Partial entropy decomposition reveals higher-order information structures in human brain activity
The standard approach to modeling the human brain as a complex system is with a network,
where the basic unit of interaction is a pairwise link between two brain regions. While …
where the basic unit of interaction is a pairwise link between two brain regions. While …
Multivariate information theory uncovers synergistic subsystems of the human cerebral cortex
One of the most well-established tools for modeling the brain is the functional connectivity
network, which is constructed from pairs of interacting brain regions. While powerful, the …
network, which is constructed from pairs of interacting brain regions. While powerful, the …
Synergistic information supports modality integration and flexible learning in neural networks solving multiple tasks
Striking progress has been made in understanding cognition by analyzing how the brain is
engaged in different modes of information processing. For instance, so-called synergistic …
engaged in different modes of information processing. For instance, so-called synergistic …
Gaussian partial information decomposition: Bias correction and application to high-dimensional data
P Venkatesh, C Bennett, S Gale… - Advances in …, 2024 - proceedings.neurips.cc
Recent advances in neuroscientific experimental techniques have enabled us to
simultaneously record the activity of thousands of neurons across multiple brain regions …
simultaneously record the activity of thousands of neurons across multiple brain regions …
Demystifying local and global fairness trade-offs in federated learning using partial information decomposition
In this paper, we present an information-theoretic perspective to group fairness trade-offs in
federated learning (FL) with respect to sensitive attributes, such as gender, race, etc …
federated learning (FL) with respect to sensitive attributes, such as gender, race, etc …
Estimating the unique information of continuous variables
The integration and transfer of information from multiple sources to multiple targets is a core
motive of neural systems. The emerging field of partial information decomposition (PID) …
motive of neural systems. The emerging field of partial information decomposition (PID) …
Ultra-marginal feature importance: Learning from data with causal guarantees
Scientists frequently prioritize learning from data rather than training the best possible
model; however, research in machine learning often prioritizes the latter. Marginal …
model; however, research in machine learning often prioritizes the latter. Marginal …
Partial information decomposition for continuous variables based on shared exclusions: Analytical formulation and estimation
Describing statistical dependencies is foundational to empirical scientific research. For
uncovering intricate and possibly nonlinear dependencies between a single target variable …
uncovering intricate and possibly nonlinear dependencies between a single target variable …
Self-similar growth and synergistic link prediction in technology-convergence networks: The case of intelligent transportation systems
Self-similar growth and fractality are important properties found in many real-world networks,
which could guide the modeling of network evolution and the anticipation of new links …
which could guide the modeling of network evolution and the anticipation of new links …