Publications

Machine learning and inference

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• T. Heskes, K. Albers, and H.J. Kappen. In Proceedings UAI (2003) 313. Approximate inference and constraint optimisation.

• J. Mooij and H.J. Kappen. IEEE Information Theory, 53 (2007) 4422. Sufficient conditions for convergence of the sum-product algorithm. 

• D Thalmeier, M Uhlmann, HJ Kappen, RM Memmesheimer, PLoS computational biology 12 (2017) doi:10.1371/journal.pcbi.1004895. Learning universal computations with spikes.

• A. Mozeika and A.C.C. Coolen, Phys. Rev. E98 (2018) 042133. Mean-field theory of Bayesian clustering.

• C. Baldassi, F. Gerace, H.J. Kappen, C. Lucibello, L. Saglietti, E. Tartaglione and R. Zecchina, Phys Rev Lett 120 (2018)  268103. Role of synaptic stochasticity in training low-precision neural networks.

• M. Sheikh and A.C.C. Coolen, Journal of Classification  (2019), DOI: 10.1007/s00357-019-09316-6. Accurate Bayesian data classification without hyperparameter cross-validation.

• A. Mozeika and A.C.C. Coolen, J. Phys. A: Math. Theor. 53 (2020) 365001. Replica analysis of Bayesian data clustering.

• A. Mozeika, M. Sheikh, F. Aguirre-Lopez, F. Antenucci, A.C.C. Coolen, 
  Phys Rev E 103 (2021) 042142. Exact results on high-dimensional linear regression via statistical physics

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Quantum computing

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• R.C. Wiersema and H.J. Kappen, Phys. Rev. A 100 (2019) 020301. Implementing perceptron models with qubits.

• A. Kolmus, M.I. Katsnelson, A.A. Khajetoorians and H.J. Kappen, New Journal of Physics 22 (2020) 023038. Atom-by-atom construction of attractors in a tunable finite size spin array

• H.J.J. Kappen, J Phys. A: Math. Theor. 53 (2020) 214001. Learning quantum models from quantum or classical data. 

• B. Kiraly, E.J. Knol, W.M.J. van Weerdenburg, H.J. Kappen, A.A. Khajetoorians, Nature Nanotechnology 16 (4), 414-420 (2021) An atomic Boltzmann machine capable of self-adaption

• A.C.C. Coolen and T. Nikoletopoulos, preprint arXiv:2010.12334 (2020). Dynamical replica analysis of quantum annealing.

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Medical statistics

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• J.E. Barrett and A.C.C. Coolen, Statistics in Medicine (2015) DOI: 10.1002/sim.6784. Covariate dimension reduction for survival data via the Gaussian process latent variable model.

• A.C.C. Coolen, J.E.Barrett, P. Paga and C.J. Perez-Vicente, J. Phys. A: Math. Theor. 50 (2017) 375001. Replica analysis of overfitting in regression models for time-to-event data.

• C. Haggstrom et al, Int. J. Cancer  (2018), DOI: 10.1002/ijc.31587. Heterogeneity in risk of prostate cancer: a Swedish population-based cohort study of competing risks and Type 2 diabetes mellitus.

• M. Sheikh and A.C.C. Coolen, J. Phys. A: Math. Theor. 52 (2019) 384002. Analysis of overfitting in the regularized Cox model.

• P.R Barber et al, J. Natl. Cancer Inst (2020) 112(9): doi: 10.1093/jnci/djz231. HER2-HER3 heterodimer quantification by FRET-FLIM and patient subclass analysis of the COIN colorectal trial.

• A.C.C. Coolen, M. Sheikh, A. Mozeika, F. Aguirre-Lopez and F Antenucci, J. Phys A  (2020). Replica analysis of overfitting in generalized linear regression models.

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Control theory

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• H.J. Kappen, Phys Rev Lett 95 (2005) 200201. A linear theory for control of non-linear stochastic systems.

• H.J. Kappen, V. Gomez and M. Opper, Machine Learning 87 (2012) 159. Optimal control as a graphical model inference problem.

• H.J. Kappen and H.C. Ruiz, J Stat Phys 162 (2016) 1244. Adaptive importance sampling for control and inference.

• S.A. Menchon and H.J. Kappen,  Intern Journal of Control 92 (2019) 2776. Learning effective state-feedback controllers through efficient multilevel importance samplers.

• K.N. McGuire, C. De Wagter, K. Tuyls, HJ Kappen and G. de Croon, Science Robotics 4 (2019) DOI: 10.1126/scirobotics.aaw9710. Minimal navigation solution for a swarm of tiny flying robots to explore an unknown environment.

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Modelling of complex systems

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• S. Rabello, A.C.C. Coolen, C.J. Perez-Vicente and F. Fraternali, J Phys A 41 (2008) 285004. A solvable model of the genesis of amino-acid sequences via coupled dynamics of folding and slow genetic variation.

• K. Mimura and  A.C.C. Coolen, J Phys A 42 (2009) 415001. Parallel dynamics of disordered Ising spin systems on finitely connected random graphs with arbitrary degree distributions.

• A.C.C. Coolen and K. Takeda, Phil Mag 92 (2011) 64. Transfer operator analysis of the parallel dynamics of disordered Ising chains. 

• E. Agliari, A. Annibale, A. Barra, A.C.C. Coolen and D. Tantari, J Phys A 46 (2013) 415003. Immune networks: multi-tasking capabilities near saturation.

• A. Mozeika and A.C.C. Coolen, J Phys A 48 (2015) 255001. Spin systems on hyper cubic Bethe lattices: a Bethe-Peierls approach.​

• A. Mozeika and A.C.C. Coolen, J Phys A 50 (2017) 035602. Statistical mechanics of clonal expansion in lymphocyte networks modelled with slow  and fast variables.

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Networks and graphs

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• A. Annibale,  A.C.C. Coolen and G. Bianconi, J Phys A 43 (2010) 395001. Network resilience against intelligent attacks constrained by the degree-dependent node removal cost.

• A. Annibale and A.C.C. Coolen, Interface Focus  1 (2011) 536. What you see is not what you get: how sampling affects macroscopic features of biological networks.

• E. S. Roberts and A. C. C. Coolen, Phys Rev E 85 (2012) 046103. Unbiased degree-preserving randomization of directed binary networks.

• A. Annibale, A.C.C. Coolen and N. Planell-Morell, J R Soc Interface 12 (2015) 20150573. Quantifying noise in mass spectrometry and yeast two-hybrid protein interaction experiments.

• F.  Aguirre Lopez, P. Barucca, M. Fekom and A.C.C. Coolen, J. Phys. A: Math. Theor. 51 (2018) 085101. Exactly solvable random graph ensemble with extensively many short cycles.

• F. Aguirre Lopez and A.C.C. Coolen, J. Phys. A: Math. Theor. 53 (2020) 065002. Imaginary replica analysis of loopy regular random graphs.

• F. Aguirre Lopez and A.C.C. Coolen, J.Phys.Complex. 2 (2021) 035010. Transitions in random graphs of fixed degrees with many short cycles