On the Graph of the Pedigree Polytope. - Abdullah Makkeh; Mozhgan Pourmoradnasseri; Dirk Oliver Theis (CoRR, 2016)

The Graph of the Pedigree Polytope is Asymptotically Almost Complete (Extended Abstract). - Abdullah Makkeh; Mozhgan Pourmoradnasseri; Dirk Oliver Theis (CALDAM, 2017)

Bivariate Partial Information Decomposition: The Optimization Perspective. - Abdullah Makkeh; Dirk Oliver Theis; Raul Vicente (Entropy, 2017)

BROJA-2PID: A Robust Estimator for Bivariate Partial Information Decomposition. - Abdullah Makkeh; Dirk Oliver Theis; Raul Vicente (Entropy, 2018)

MAXENT3D_PID: An Estimator for the Maximum-Entropy Trivariate Partial Information Decomposition. - Abdullah Makkeh; Daniel Chicharro; Dirk Oliver Theis; Raul Vicente (Entropy, 2019)

A differentiable measure of pointwise shared information. - Abdullah Makkeh; Aaron J. Gutknecht; Michael Wibral (CoRR, 2020)

Bits and Pieces: Understanding Information Decomposition from Part-whole Relationships and Formal Logic. - Aaron J. Gutknecht; Michael Wibral; Abdullah Makkeh (CoRR, 2020)

Partial Information Decomposition of Boolean Functions: a Fourier Analysis perspective. - Abdullah Makkeh; Dirk Oliver Theis; Raul Vicente (CoRR, 2020)

Estimating the Unique Information of Continuous Variables. - Ari Pakman; Amin Nejatbakhsh; Dar Gilboa; Abdullah Makkeh; Luca Mazzucato; Michael Wibral; Elad Schneidman (NeurIPS, 2021)

A partial information decomposition for discrete and continuous variables. - Kyle Schick-Poland; Abdullah Makkeh; Aaron J. Gutknecht; Patricia Wollstadt; Anja Sturm; Michael Wibral (CoRR, 2021)

Partial Information Decomposition Reveals the Structure of Neural Representations. - David A. Ehrlich; Andreas C. Schneider; Michael Wibral; Viola Priesemann; Abdullah Makkeh (CoRR, 2022)

Quantifying Reinforcement-Learning Agent's Autonomy, Reliance on Memory and Internalisation of the Environment. - Anti Ingel; Abdullah Makkeh; Oriol Corcoll; Raul Vicente (Entropy, 2022)

From Babel to Boole: The Logical Organization of Information Decompositions. - Aaron J. Gutknecht; Abdullah Makkeh; Michael Wibral (CoRR, 2023)

Infomorphic networks: Locally learning neural networks derived from partial information decomposition. - Marcel Graetz; Abdullah Makkeh; Andreas C. Schneider; David A. Ehrlich; Viola Priesemann; Michael Wibral (CoRR, 2023)

Partial Information Decomposition for Continuous Variables based on Shared Exclusions: Analytical Formulation and Estimation. - David A. Ehrlich; Kyle Schick-Poland; Abdullah Makkeh; Felix Lanfermann; Patricia Wollstadt; Michael Wibral (CoRR, 2023)

A Measure of the Complexity of Neural Representations based on Partial Information Decomposition. - David A. Ehrlich; Andreas C. Schneider; Viola Priesemann; Michael Wibral; Abdullah Makkeh (Trans. Mach. Learn. Res., 2023)

A General Framework for Interpretable Neural Learning based on Local Information-Theoretic Goal Functions. - Abdullah Makkeh; Marcel Graetz; Andreas C. Schneider; David A. Ehrlich; Viola Priesemann; Michael Wibral (GRO.data, 2024)

What should a neuron aim for? Designing local objective functions based on information theory. - Andreas Christian Schneider; Valentin Neuhaus; David Alexander Ehrlich; Abdullah Makkeh; Alexander S. Ecker; Viola Priesemann; Michael Wibral (ICLR, 2025)

Shannon invariants: A scalable approach to information decomposition. - Aaron J. Gutknecht; Fernando E. Rosas; David A. Ehrlich; Abdullah Makkeh; Pedro A. M. Mediano; Michael Wibral (CoRR, 2025)