Arend Torres, Fabricio et al. (2024) ‘Lagrangian Flow Networks for Conservation Laws’, in The Twelfth International Conference on Learning Representations. Vienna, Austria (The Twelfth International Conference on Learning Representations). Available at: https://openreview.net/forum?id=Nshk5YpdWE.

Hauke, Daniel J. et al. (2024) ‘Altered Perception of Environmental Volatility During Social Learning in Emerging Psychosis’, Computational Psychiatry, 8, pp. 1–22. Available at: https://doi.org/10.5334/cpsy.95.

Nagy-Huber, Monika and Roth, Volker (2024) ‘Physics-informed boundary integral networks (PIBI-Nets): A data-driven approach for solving partial differential equations’, Journal of Computational Science, 81. Available at: https://doi.org/10.1016/j.jocs.2024.102355.

Schwendinger, Fabian et al. (2024) ‘Using Machine Learning–Based Algorithms to Identify and Quantify Exercise Limitations in Clinical Practice: Are We There Yet?’, Medicine and Science in Sports and Exercise, 56, pp. 159–169. Available at: https://doi.org/10.1249/mss.0000000000003293.

Torres, Fabricio Arend et al. (2024) ‘LAGRANGIAN FLOW NETWORKS FOR CONSERVATION LAWS’.

Bedford, Peter et al. (2023) ‘The effect of lysergic acid diethylamide (LSD) on whole-brain functional and effective connectivity’, Neuropsychopharmacology, 48, pp. 1175–1183. Available at: https://doi.org/10.1038/s41386-023-01574-8.

Gschwandtner, Ute et al. (2023) ‘Prediction of cognitive decline in Parkinson’s disease (PD) patients with electroencephalography (EEG) connectivity characterized by time-between-phase-crossing (TBPC)’, Scientific Reports, 13. Available at: https://doi.org/10.1038/s41598-023-32345-6.

Hauke, Daniel J. et al. (2023) ‘Aberrant Hierarchical Prediction Errors Are Associated With Transition to Psychosis: A Computational Single-Trial Analysis of the Mismatch Negativity’, Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 8, pp. 1176–1185. Available at: https://doi.org/10.1016/j.bpsc.2023.07.011.

Negri, Marcello Massimo, Arend Torres, Fabricio and Roth, Volker (2023) ‘Conditional Matrix Flows for Gaussian Graphical Models’, in Advances in Neural Information Processing Systems. New Orleans: Curran Associates, Inc. (Advances in Neural Information Processing Systems), pp. 25095––25111. Available at: https://proceedings.neurips.cc/paper_files/paper/2023/file/4eef8829319316d0b552328715c836c3-Paper-Conference.pdf.

Negri, Marcello Massimo, Arend Torres, Fabricio and Roth, Volker (2023) ‘Conditional Matrix Flows for Gaussian Graphical Models’, in Advances in Neural Information Processing Systems. New Orleans, USA (Advances in Neural Information Processing Systems), pp. 25095––25111. Available at: https://papers.nips.cc/paper_files/paper/2023/hash/4eef8829319316d0b552328715c836c3-Abstract-Conference.html.

Steppan, Martin et al. (2023) ‘Machine Learning Facial Emotion Classifiers in Psychotherapy Research: A Proof-of-Concept Study’, Psychopathology, null. Available at: https://doi.org/10.1159/000534811.

Hauke, D.J. et al. (2022) ‘Aberrant hierarchical prediction errors are associated with transition to psychosis: A computational single-trial analysis of the mismatch negativity’. Cold Spring Harbor Laboratory. Available at: https://doi.org/10.1101/2022.12.20.22283712.

Arend Torres, Fabricio et al. (2022) ‘Mesh-free eulerian physics-informed neural networks’. Available at: https://doi.org/10.48550/arxiv.2206.01545.

Hauke, Daniel Jonas (2022) Hierarchical Bayesian inference in psychosis. . Translated by Roth Volker. Dissertation.

Hauke D.J. et al. (2022) ‘Increased Belief Instability in Psychotic Disorders Predicts Treatment Response to Metacognitive Training’, Schizophrenia Bulletin, 48, pp. 826–838. Available at: https://doi.org/10.1093/schbul/sbac029.

Hauke, D J et al. (2022) ‘Increased Belief Instability in Psychotic Disorders Predicts Treatment Response to Metacognitive Training.’, Schizophrenia bulletin, 48(4), pp. 826–838. Available at: https://doi.org/10.1093/schbul/sbac029.

Maxim Samarin, Volker Roth and David Belius (2022) ‘Feature Learning and Random Features in Standard Finite-Width Convolutional Neural Networks: An Empirical Study’, in UAI. Association for Uncertainty in Artificial Intelligence (AUAI): Association for Uncertainty in Artificial Intelligence (AUAI) (UAI). Available at: https://openreview.net/forum?id=ScIEZdIiqe5.

Nesterov, Vitali et al. (2022) ‘Learning Invariances with Generalised Input-Convex Neural Networks’. Available at: https://doi.org/10.48550/arxiv.2204.07009.

Samarin, Maxim (2022) Machine learning for informed representation learning. . Translated by Roth Volker. Dissertation.

Gschwandtner, Ute et al. (2021) ‘Dynamic Functional Connectivity of EEG: From Identifying Fingerprints to Gender Differences to a General Blueprint for the Brain’s Functional Organization.’, Frontiers in neuroscience, 15, p. 683633. Available at: https://doi.org/10.3389/fnins.2021.683633.

Gschwandtner, Ute et al. (2021) ‘Dynamic Functional Connectivity of EEG: From Identifying Fingerprints to Gender Differences to a General Blueprint for the Brain’s Functional Organization’, Frontiers in Neuroscience, 15. Available at: https://doi.org/10.3389/fnins.2021.683633.

Keller, Sebastian Mathias et al. (2021) ‘Learning Extremal Representations with Deep Archetypal Analysis’, International Journal of Computer Vision, 129(4), pp. 805–820. Available at: https://doi.org/10.1007/s11263-020-01390-3.

Samarin, Maxim et al. (2021) ‘Learning Conditional Invariance Through Cycle Consistency’, in Bauckhage, Christian and Gall, Juergen and Schwing, Alexander (ed.). Springer International Publishing: Springer International Publishing.

Samarin, Maxim et al. (2021) ‘Learning Conditional Invariance Through Cycle Consistency’, pp. 376–391. Available at: https://doi.org/10.1007/978-3-030-92659-5_24.

Wu, Mike et al. (2021) ‘Optimizing for interpretability in deep neural networks with tree regularization’, Journal of Artificial Intelligence Research, 72. Available at: https://doi.org/10.1613/jair.1.12558.

Zimmermann, Ronan et al. (2021) ‘Silence in the psychotherapy of adolescents with borderline personality pathology’, Personality disorders, 12(2), pp. 160–170. Available at: https://doi.org/10.1037/per0000402.

Keller, S.M. et al. (2020) ‘FV22 Reduced Tsallis Entropy of EEG in Patients with Parkinsons Disease – A Predictive Marker for Cognitive Decline’, Clinical Neurophysiology, 131(4), p. e233. Available at: https://doi.org/10.1016/j.clinph.2019.12.112.

Samarin, M. et al. (2020) ‘Visual Understanding in Semantic Segmentation of Soil Erosion Sites in Swiss Alpine Grasslands’. Copernicus GmbH. Available at: https://doi.org/10.5194/egusphere-egu2020-17346.

Zweifel, L. et al. (2020) ‘Identification of Soil Erosion in Alpine Grasslands on High-Resolution Aerial Images: Switching from Object-based Image Analysis to Deep Learning?’ Copernicus GmbH. Available at: https://doi.org/10.5194/egusphere-egu2020-2328.

Keller, Sebastian Mathias (2020) Interpretable machine learning for electro-encephalography. . Translated by Roth Volker. Dissertation.

Keller, Sebastian M. et al. (2020) ‘Cognitive decline in Parkinson’s disease is associated with reduced complexity of EEG at baseline’, Brain Communications, 2(2), p. fcaa207. Available at: https://doi.org/10.1093/braincomms/fcaa207.

Kozak, Vitalii V. et al. (2020) ‘EEG Slowing and Axial Motor Impairment Are Independent Predictors of Cognitive Worsening in a Three-Year Cohort of Patients With Parkinson’s Disease’, Frontiers in Aging Neuroscience, 12, p. 171. Available at: https://doi.org/10.3389/fnagi.2020.00171.

Nesterov, Vitali, Wieser, Mario and Roth, Volker (2020) ‘3DMolNet: A Generative Network for Molecular Structures’, Arxiv [Preprint]. Cornell University. Available at: https://doi.org/10.48550/arxiv.2010.06477.

Parbhoo, Sonali et al. (2020) ‘Transfer Learning from Well-Curated to Less-Resourced Populations with HIV’, in Doshi-Velez, F.; Fackler, J.; Jung, K.; Kale, D.; Ranganath, R.; Wallace, B.; Wiens, J. (ed.) Proceedings of Machine Learning Research. PMLR: PMLR (Proceedings of Machine Learning Research).

Parbhoo, Sonali et al. (2020) ‘Information Bottleneck for Estimating Treatment Effects with Systematically Missing Covariates’, Entropy, 22(4), p. 389. Available at: https://doi.org/10.3390/e22040389.

Thorball, Christian W. et al. (2020) ‘Host Genomics of the HIV-1 Reservoir Size and Its Decay Rate During Suppressive Antiretroviral Treatment’, Journal of Acquired Immune Deficiency Syndromes, 85(4), pp. 517–524. Available at: https://doi.org/10.1097/qai.0000000000002473.

Wan, Chenjie et al. (2020) ‘Heritability of the HIV-1 reservoir size and decay under long-term suppressive ART’, Nature Communications, 11(1), p. 5542. Available at: https://doi.org/10.1038/s41467-020-19198-7.

Wieczorek, Aleksander and Roth, Volker (2020) ‘On the Difference between the Information Bottleneck and the Deep Information Bottleneck’, Entropy, 22(2), p. 131. Available at: https://doi.org/10.3390/e22020131.

Wieser, Mario (2020) Learning invariant represeantaions for deep latent variable models. . Translated by Roth Volker. Dissertation.

Wieser, Mario et al. (2020) ‘Inverse Learning of Symmetries’, in Larochelle, H.; Ranzato, M.; Hadsell, R.; Balcan, M. F.; Lin, H. (ed.). Curran Associates, Inc.: Curran Associates, Inc.

Wieser, Mario et al. (2020) ‘Inverse learning of symmetries’.

Wu, Mike et al. (2020) ‘Regional Tree Regularization for Interpretability in Deep Neural Networks’, in Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Press: AAAI Press (Proceedings of the ... AAAI Conference on Artificial Intelligence). Available at: https://doi.org/10.1609/aaai.v34i04.6112.

Wu, Mike et al. (2020) ‘Regional tree regularization for interpretability in deep neural networks’, pp. 6413–6421.

Chaturvedi, M. et al. (2019) ‘FV 4 Electroencephalographic Activity as a potential prodromal marker for Parkinson’s disease’, Clinical Neurophysiology, 130(8), pp. e122–e123. Available at: https://doi.org/10.1016/j.clinph.2019.04.614.

Bachmann, Nadine et al. (2019) ‘Determinants of HIV-1 reservoir size and long-term dynamics during suppressive ART’, Nature Communications, 10. Available at: https://doi.org/10.1038/s41467-019-10884-9.

Chaturvedi, Menorca et al. (2019) ‘Phase lag index and spectral power as QEEG features for identification of patients with mild cognitive impairment in Parkinson’s disease’, Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 130(10), pp. 1937–1944. Available at: https://doi.org/10.1016/j.clinph.2019.07.017.

Keller, Sebastian Mathias, Murezzan, Damian and Roth, Volker (2019) ‘Invexity Preserving Transformations for Projection Free Optimization with Sparsity Inducing Non-convex Constraints’, pp. 682–697. Available at: https://doi.org/10.1007/978-3-030-12939-2_47.

Keller, Sebastian Mathias et al. (2019) ‘Deep Archetypal Analysis’, in Fink, Gernot A.; Frintrop, Simone; Jiang, Xiaoyi (ed.) Lecture Notes in Computer Science. Springer International Publishing: Springer International Publishing (Lecture Notes in Computer Science). Available at: https://doi.org/10.1007/978-3-030-33676-9_12.

Kortylewski, Adam et al. (2019) ‘Greedy Structure Learning of Hierarchical Compositional Models’. IEEE: IEEE. Available at: https://doi.org/10.1109/cvpr.2019.01188.

Parbhoo, Sonali (2019) Causal inference and interpretable machine learning for personalised medicine. . Translated by Roth Volker. Dissertation.

Shulga, Dmytro (2019) Tensor B-spline numerical method for PDEs: a high performance approach. . Translated by Roth Volker. Dissertation.

Wieczorek, Aleksander and Roth, Volker (2019) ‘Information Theoretic Causal Effect Quantification’, Entropy, 21(10), p. 975. Available at: https://doi.org/10.3390/e21100975.

Bogaarts, J.G. et al. (2018) ‘P30. A novel application of the Phase-lag-Index in functional connectivity research’, Clinical Neurophysiology, 129(8), p. e79. Available at: https://doi.org/10.1016/j.clinph.2018.04.671.

Chaturvedi, M. et al. (2018) ‘P78. Can Phase Lag Index (PLI) be beneficial in distinguishing Parkinsons disease Dementia (PDD) patients from Parkinsons disease (PD) patients?’, Clinical Neurophysiology, 129(8), p. e99. Available at: https://doi.org/10.1016/j.clinph.2018.04.710.

Cozac, V. et al. (2018) ‘P76. Axial impairment and EEG slowing are independent predictors of cognitive outcome in a three-year cohort of PD patients’, Clinical Neurophysiology, 129(8), p. e98. Available at: https://doi.org/10.1016/j.clinph.2018.04.708.

Chaturvedi, M. et al. (2018) ‘F67. Distinguishing Parkinson’s Disease Dementia (PDD) patients from Parkinson’s Disease (PD) patients using EEG frequency and connectivity measures’, Clinical Neurophysiology, 129, p. e92. Available at: https://doi.org/10.1016/j.clinph.2018.04.230.

Parbhoo, Sonali et al. (2018) ‘Improving counterfactual reasoning with kernelised dynamic mixing models’, PloS one, 13(11), p. e0205839. Available at: https://doi.org/10.1371/journal.pone.0205839.

Wieczorek, Aleksander et al. (2018) ‘Learning sparse latent representations with the deep copula information bottleneck’.

Wieser, Mario et al. (2018) ‘Learning Sparse Latent Representations with the Deep Copula Information Bottleneck’. ICLR: ICLR. Available at: https://openreview.net/forum?id=Hk0wHx-RW.

Wu, Mike et al. (2018) ‘Beyond Sparsity: Tree Regularization of Deep Models for Interpretability’. AAAI: AAAI. Available at: https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16285.

Wu, Mike et al. (2018) ‘Beyond sparsity: Tree regularization of deep models for interpretability’, pp. 1670–1678.

Chaturvedi, M. et al. (2017) ‘P 129 Quantitative EEG and neuropsychological tests to differentiate between Parkinson’s disease patients and healthy controls with Random Forest algorithm’, Clinical Neurophysiology, 128(10), pp. e391–e392. Available at: https://doi.org/10.1016/j.clinph.2017.06.202.

Chaturvedi, M. et al. (2017) ‘Quantitative EEG (QEEG) Measures Differentiate Parkinson`s Disease Patients from Healthy Controls’, Frontiers in Aging Neuroscience, 9(3), p. 3. Available at: https://doi.org/10.3389/fnagi.2017.00003.

Kaufmann, Dinu (2017) Semi-parametric Gaussian Copula models for machine learning. . Translated by Roth Volker. Dissertation.

Parbhoo, Sonali et al. (2017) ‘Combining Kernel and Model Based Learning for HIV Therapy Selection’, Amia Summits on Translational Science Proceedings, 2017, pp. 239–248. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5543338/.

Roth, Volker and Vetter, Thomas (2017) ‘Preface’, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10496 LNCS.

Chaturvedi, M. et al. (2016) ‘Can Quantitative EEG (QEEG) differentiate patients with Parkinson’s disease (PD) from healthy controls?’, Parkinsonism & Related Disorders, 22, p. e163. Available at: https://doi.org/10.1016/j.parkreldis.2015.10.388.

Dazert, E. et al. (2016) ‘Quantitative proteomics and phosphoproteomics on serial tumor biopsies from a sorafenib-treated HCC patient’, Proceedings of the National Academy of Sciences of the United States of America, 113(5), pp. 1381–1386. Available at: https://doi.org/10.1073/pnas.1523434113.

Kaufmann, Dinu et al. (2016) ‘Bayesian Markov Blanket Estimation’, in Proceedings of Machine Learning Research. JMLR.org: JMLR.org (Proceedings of Machine Learning Research). Available at: http://jmlr.org/proceedings/papers/v51/kaufmann16.html.

Kaufmann, Dinu et al. (2016) ‘Bayesian markov blanket estimation’, pp. 333–341.

Makowska, Zuzanna et al. (2016) ‘Gene expression analysis of biopsy samples reveals critical limitations of transcriptome-based molecular classifications of hepatocellular carcinoma’, The Journal of Pathology : Clinical Research, 2(2), pp. 80–92. Available at: https://doi.org/10.1002/cjp2.37.

Makowska, Z. et al. (2015) ‘P0309 : Gene expression profiling of hepatocellular carcinoma biopsies reveals three molecular classes with distinct clinical and biological properties’, Journal of Hepatology, 62, pp. S424–S425. Available at: https://doi.org/10.1016/s0168-8278(15)30524-9.

Adametz, David (2015) Invariances for Graussian models. . Translated by Roth Volker. Dissertation.

Adametz, David and Roth, Volker (2015) ‘Distance-based network recovery under feature correlation’, pp. 209–210.

Vogt, Julia E. et al. (2015) ‘Probabilistic clustering of time-evolving distance data’, Machine learning, 100(2-3), pp. 635–654. Available at: https://doi.org/10.1007/s10994-015-5516-x.

Kaufmann, Dinu, Keller, Sebastian and Roth, Volker (2015) ‘Copula Archetypal Analysis’, in Gall, Juergen; Gehler, Peter; Leibe, Bastian (ed.) Pattern Recognition. Cham: Springer (Lecture Notes in Computer Science), pp. 117–128. Available at: https://doi.org/10.1007/978-3-319-24947-6_10.

Bousleiman, H. et al. (2014) ‘Quantitative EEG (qEEG) as Marker for Mild Cognitive Impairment (MCI) in Patients with Parkinson’s Disease (PD) (P5.059)’, Neurology, 82(10_supplement). Available at: https://doi.org/10.1212/wnl.82.10_supplement.p5.059.

Dill, Michael T. et al. (2014) ‘Pegylated IFN-α regulates hepatic gene expression through transient Jak/STAT activation’, Journal of Clinical Investigation, 124(4), pp. 1568–81. Available at: https://doi.org/10.1172/jci70408.

Giallonardo, Francesca Di et al. (2014) ‘Full-length haplotype reconstruction to infer the structure of heterogeneous virus populations’, Nucleic Acids Research, 42(14), p. e115. Available at: https://doi.org/10.1093/nar/gku537.

Prabhakaran, Sandhya (2014) Machine learning methods for HIV/AIDS diagnostics and therapy planning. . Translated by Roth Volker. Dissertation.

Prabhakaran, Sandhya et al. (2014) ‘HIV Haplotype Inference Using a Propagating Dirichlet Process Mixture Model’, IEEE/ACM transactions on computational biology and bioinformatics, 11(1), pp. 182–191. Available at: https://doi.org/ea017b9f-ec09-4077-85ab-ded3af538c48.

Prabhakaran, Sandhya et al. (2014) ‘HIV haplotype inference using a propagating dirichlet process mixture model’, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 11, pp. 182–191. Available at: https://doi.org/10.1109/tcbb.2013.145.

Rey, Mélanie (2014) Copula models in machine learning. . Translated by Roth Volker. Dissertation.

Rey, Melani, Roth, Volker and Fuchs, Thomas (2014) ‘Sparse meta-Gaussian information bottleneck’, in Jebara,Tony;Xing,Eric P. (ed.). Curran: Curran. Available at: http://jmlr.org/proceedings/papers/v32/rey14.pdf.

Adametz, David, Rey, Melanie and Roth, Volker (2014) ‘Information Bottleneck for Pathway-Centric Gene Expression Analysis’, in Jiang, X; Hornegger, J; Koch, R (ed.) Pattern recognition: 36th German Conference. Cham: Springer International Publishing (Pattern recognition: 36th German Conference), p. S. 81–91. Available at: https://doi.org/10.1007/978-3-319-11752-2_7.

Adametz, David and Roth, Volker (2014) ‘Distance-based network recovery under feature correlation’, in Z. Ghahramani and M. Welling and C. Cortes and N.D. Lawrence and K.Q. Weinberger (ed.) Advances in neural information processing systems. Cambridge (Mass.): MIT-Press (Advances in neural information processing systems), p. S. 775–783. Available at: http://papers.nips.cc/paper/5470-distance-based-network-recovery-under-feature-correlation.pdf.

Prabhakaran, Sandhya et al. (2013) ‘Recovering networks from distance data’, Machine learning, 92(2-3), pp. 251–283. Available at: https://doi.org/10.1007/s10994-013-5370-7.

Rey, Melanie Rey and Roth, Volker (2013) ‘Meta-Gaussian information bottleneck’, in Bartlett,P; Pereira,F.C.N.;Burges,C.J.C.,Bottou,L.; Weinberger,K.Q.} (ed.). The MIT Press: The MIT Press.

Roth, V. et al. (2013) ‘Structure Preserving Embedding of Dissimilarity Data’. London: Springer London, pp. 157–177. Available at: https://doi.org/10.1007/978-1-4471-5628-4_7.

Töpfer, Armin et al. (2013) ‘Probabilistic inference of viral quasispecies subject to recombination’, Journal of computational biology, 20(2), pp. 113–23. Available at: https://doi.org/10.1089/cmb.2012.0232.

Wigger, Leonore, Vogt, Julia E and Roth, Volker (2013) ‘Malaria haplotype frequency estimation’, Statistics in medicine, 32(21), pp. 3737–51. Available at: https://doi.org/10.1002/sim.5792.

Beerenwinkel, Niko et al. (2012) ‘Challenges and opportunities in estimating viral genetic diversity from next-generation sequencing data’, Frontiers in microbiology, 3(00329), p. 329. Available at: https://doi.org/10.3389/fmicb.2012.00329.

Melanie, Rey and Roth, Volker (2012) ‘Copula mixture model for eependency-seeking clustering’. International Machine Learning Society: International Machine Learning Society. Available at: http://icml.cc/2012/papers/486.pdf.

Meyer, Stefanie et al. (2012) ‘A seven-marker signature and clinical outcome in malignant melanoma : a large-scale tissue-microarray study with two independent patient cohorts’, PLoS ONE, 7(6). Available at: https://doi.org/10.1371/journal.pone.0038222.

Prabhakaran, Sandhya et al. (2012) ‘Recovering Networks from Distance Data’, Journal of machine learning research, 25, pp. 349–364.

Prabhakaran, Sandhya et al. (2012) ‘Recovering networks from distance data’, pp. 349–364.

Prabhakaran, Sandhya et al. (2012) ‘Automatic model selection in archetype analysis’, in Pinz, Axel; Pock, Thomas; Bischof, Horst; Leberl, Franz (ed.) Lecture notes in computer science. Springer: Springer (Lecture notes in computer science). Available at: https://doi.org/10.1007/978-3-642-32717-9_46.

Raman, Sudhir and Roth, Volker (2012) ‘Sparse point estimation for Bayesian regression via simulated annealing’, in Pinz, Axel; Pock, Thomas; Bischof, Horst; Leberl, Franz (ed.) Lecture Notes in Computer Science. Springer: Springer (Lecture Notes in Computer Science). Available at: https://doi.org/10.1007/978-3-642-32717-9_32.

Rey, Mélanie and Roth, Volker (2012) ‘Meta-Gaussian information bottleneck’, pp. 1916–1924.

Rey, Mélanie and Roth, Volker (2012) ‘Copula mixture model for dependency-seeking clustering’, pp. 927–934.