Publications

  Peer-Reviewed Journal Articles

1. M. Hérin, P. Perny, N. Sokolovska. Learning Preference Representations based on Choquet Integrals for Multicriteria Decision Making. Annals of Mathematics and Artificial Intelligence (AMAI), accepted, 2024.

2. A. Sultanov, J.-C. Crivello, T. Rebafka, N. Sokolovska. Data-driven score-based models for generating stable structures with adaptive crystal cells. Journal of Chemical Information and Modeling, 63, 22, 6986 - 6997, 2023.

3. E.-J. El Hachem, N. Sokolovska, H. Soula. Latent Dirichlet allocation for double clustering (LDA-DC): discovering patients phenotypes and cell populations within a single Bayesian framework. BMC Bioinformics, 24(1): 61, 2023.

4. Belda E, Voland L, Tremaroli V, Falony G, Adriouch S, Assmann KE, Prifti E,Aron-Wisnewsky J, Debédat J, Le Roy T, Nielsen T, Amouyal C, André S,Andreelli F, Blüher M, Chakaroun R, Chilloux J, Coelho LP, Dao MC, Das P, Fellahi S, Forslund S, Galleron N, Hansen TH, Holmes B, Ji B, Krogh Pedersen H, Le P, Le Chatelier E, Lewinter C, Mannerås-Holm L, Marquet F, Myridakis A, Pelloux V, Pons N, Quinquis B, Rouault C, Roume H, Salem JE, Sokolovska N, Søndertoft NB, Touch S, Vieira-Silva S; MetaCardis Consortium; Galan P, Holst J, Gøtze JP, Køber L, Vestergaard H, Hansen T, Hercberg S, Oppert JM, Nielsen J, Letunic I, Dumas ME, Stumvoll M, Pedersen OB, Bork P, Ehrlich SD, Zucker JD, Bäckhed F, Raes J, Clément K. Impairment of gut microbial biotin metabolism and host biotin status in severe obesity: effect of biotin and prebiotic supplementation on improved metabolism. Gut 71, 12, 2022.

5. S. Lecoutre, F. Merabtene, E.-J. El Hachem, C. Gamblin, C. Rouault, N. Sokolovska, H. Soula, W. S.Lai, P. J.Blackshear, K. Clément, I. Dugail. Beta-hydroxybutyrate dampens adipose progenitors’ profibrotic activation through canonical Tgfβ signaling and non-canonical ZFP36-dependent mechanisms. Molecular Metabolism, doi: 10.1016/j.molmet.2022.101512, 2022. 

6. N. Sokolovska, Y. Mohseni Behbahani. Vanishing boosted weights: a consistent algorithm to learn interpretable rules. Pattern Recognition Letters, 152, 63 - 69, 2021. 

7. N. Sokolovska, P.-H. Wuillemin. The role of instrumental variables in causal inference based on independence of cause and mechanism. Entropy, MDPI, 23(8), 928, 2021. 

8. S. K Forslund, R. Chakaroun, M. Zimmermann-Kogadeeva, L. Markó, J. Aron-Wisnewsky, T. Nielsen, L. Moitinho-Silva, T. S. B. Schmidt, G. Falony, S. Vieira-Silva, S. Adriouch, R. J Alves, K. Assmann, J.-P. Bastard, T. Birkner, R. Caesar, J. Chilloux, L. P. Coelho, L. Fezeu, N.  Galleron, G. Helft, R. Isnard, B. Ji, M. Kuhn, E. Le Chatelier, A. Myridakis, L. Olsson, N. Pons, E. Prifti, B. Quinquis, H. Roume, J.-E. Salem, N.  Sokolovska, V. Tremaroli, M. Valles-Colomer, C. Lewinter, N. B Søndertoft, H. K. Pedersen, T. H. Hansen, J. P. Gøtze, L. Køber, H.  Vestergaard, T. Hansen, J.-D. Zucker, S. Hercberg, J.-M. Oppert, I. Letunic, J. Nielsen, F. Bäckhed, S. D. Ehrlich, M.-E. Dumas, J. Raes, O.  Pedersen, K. Clément, M. Stumvoll, P. Bork. Combinatorial, additive and dose-dependent drug–microbiome associations. Nature, 2021. 

9. J.-C. Crivello, J.-M. Joubert, N. Sokolovska. Supervised deep learning prediction of the formation enthalpy of complex phases using a DFT database: the sigma-phase as an example. Computational Materials Science, 201, 110864, 2021.

10. T. Shpakova, N. Sokolovska. Probabilistic personalised cascade with abstention. Pattern Recognition Letters, 147, 8 - 15, 2021.

11. A. Weber Zendrera, N. Sokolovska, H. Soula. Functional prediction of environmental variables using metabolic networks. Scientific Reports, 11, 12192 2021. 

12. E. Belda, L. Voland, V. Tremaroli, G. Falony, S. Adriouch, K. E. Assmann, E. Prifti, J. Aron-Wisnewsky, J. Debédat, T. Le Roy, T. Nielsen, C. Amouyal, S. André, F. Andreelli, M. Blüher, R. Chakaroun, J. Chilloux, L. P. Coelho, M. C. Dao, P. Das, S. Fellahi, S. Forslund, N. Galleron, T. H. Hansen, B. Holmes, B. Ji, H. K. Pedersen, P. Le, E. Le Chatelier, C. Lewinter, L. Mannerås-Holm, F. Marquet, A. Myridakis, V. Pelloux, N. Pons, B. Quinquis, C. Rouault, H. Roume, J.-E. Salem, N. Sokolovska, Nadja B Søndertoft, S. Touch, S. Vieira-Silva, P. Galan, J. Holst, J. P. Gøtze, L. Køber, H. Vestergaard, T. Hansen, S. Hercberg, J.-M. Oppert, J. Nielsen, I. Letunic, M.-E. Dumas, M. Stumvoll, O. B. Pedersen, P. Bork, S. D. Ehrlich, J.-D. Zucker, F. Bäckhed, J. Raes, K. Clément, the MetaCardis Consortium. Impairment of gut microbial biotin metabolism and host biotin status in severe obesity: effect of biotin and prebiotic supplementation on improved metabolism. Gut, doi:10.1136/gutjnl-2021-325753, 2021. 

13. N. Sokolovska, O. Permiakova, S. K. Forslund, J.-D. Zucker. Using Unlabeled Data to Discover Bivariate Causality with Deep Restricted Boltzmann Machines. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(1) : 358 – 365, 2020.

14. G. Marcelin, C. Da Cunha, C. Gamblin, N. Suffee, C. Rouault, A. Leclerc, A. Lacombe, N. Sokolovska, E.L. Gautier, K. Clément, I. Dugail. Autophagy inhibition blunts PDG-FRA adipose progenitors’ cell-autonomous fibrogenic response to high-fat diet. Autophagy, https://doi.org/10.1080/15548627.2020.1717129, 2020.

15. A. Weber Zendrera, N. Sokolovska, H. Soula. Robust structure measures of metabolic networks that predict prokaryotic optimal growth temperature. BMC Bioinformatics, 20(1), 499 :1 – 499 :13, 2019.

16. N. Sokolovska, K. Clément, J.-D. Zucker. Revealing causality between heterogeneous data sources with deep restricted Boltzmann machines. Information Fusion, 50, 139 – 147, 2019.

17. M. C. Dao, N. Sokolovska, R. Brazeilles, S. Affeldt, V. Pelloux, E. Prifti, J. Chilloux, E. O. Verger, B. Kayser, J. Aron-Wisnewsky, F. Ichou, E. Pujos-Guillot, L. Hoyles, C. Juste, J. Doré, M.-E. Dumas, S. W. Rizkalla, B. A. Holmes, J.-D. Zucker, K.Clément. A data integration multi-omics approach to study calorie restriction-induced changes in insulin sensitivity. Frontiers in Physiology, doi : 10.3389/fphys.2018.01958. eCollection, 2018.

18. D. Dicker, R. Golan, J. Aron-Wisnewsky, J.-D. Zucker, N. Sokolovska, D. S. Comaneshter, R. Yahalom, S. Vinker, K. Clément, A. Rudich. Prediction of long-term diabetes remission after RYGB, sleeve gastrectomy and adjustable gastric banding using DiaRem and Advanced-DiaRem scores. Obesity Surgery, doi : 10.1007/s11695-018-3583-3, 2018.

19. J. Debédat, N. Sokolovska, M. Coupaye, L. Genser, G. de Turenne, J.-L. Bouillot, C. Poitou, J.-M. Oppert, S. Ledoux, J.-D. Zucker, K. Clément, J. Aron-Wisnewsky. Long-term relapse of type 2 diabetes after Roux-en-Y gastric bypass: prediction and clinical relevance. Diabetes Care, 41(10) : 2086 – 2095, 2018.

20. J. Aron-Wisnewsky, N. Sokolovska, Y. Liu, D.-S. Comaneshter, S. Vinker, T. Pecht, C. Poitou, J.-M. Oppert, J.-L. Bouillot, L. Genser, D. Dicker, J.-D. Zucker, A. Rudich, K. Clément. The Advanced DiaRem Score improves prediction of diabetes remission one-year post-roux-en-y gastric bypass, Diabetologia, 60(10), 1892 – 1902, 2017.

21. P. Bel Lassen, J. Aron-Wisnewsky, Y. Liu, P. Bedossa, G. le Naour, J. Tordjmann, C. Poitou, J.-L. Bouillot, L. Genser, J.-D. Zucker, F. Charlotte, N. Sokolovska, K. Clément. The FAT score, a fibrosis score of adipose tissue: predicting weight loss outcome after gastric bypbass. The Journal of Clinical Endocrinology and Metabolism, 102(7): 2442 – 2453, 2017.

22. S. Affeldt, N. Sokolovska, E. Prifti, and J.-D. Zucker. Spectral Consensus Strategy for Accurate Reconstruction of Large Biological Networks. BMC Bioinformatics, 17 (S-16): 85 – 97, 2016.

23. N. Sokolovska, K. Clément, J.-D. Zucker. Deep Kernel Dimensionality Reduction for Scalable Data Integration. International Journal of Approximate Reasoning, 74: 121 – 132, 2016.

24. M.-C. Dao, A. Everard, J. Aron-Wisnewsky, N. Sokolovska, E. Prifti, E. O. Verger, B. Kayser, F. Levenez, J. Chilloux, L. Hoyles, MICRO-Obes Consortium, M.-E. Dumas, S. W. Rizkalla, J. Doré, P. D. Cani, K. Clément. Akkermansia muciniphila and improved metabolic health during a dietary intervention in obesity: relationship with gut microbiome richness and ecology. Gut, 65(3), 426 – 436, 2016.

25. N. Sokolovska, O. Teytaud, S. Rizkalla, MicroObese Consortium, K. Clément, J.-D. Zucker. Sparse Zero-Sum Games as Stable Functional Feature Selection. PloS One, doi: 10.1371/journal.pone.0134683. eCollection 2015, 2015.

26. M.-C. Dao, A. Everard, J. Aron-Wisnewsky, N. Sokolovska, E. O. Verger, S. W. Rizkalla, J. Doré, P. D. Cani, K. Clément. Akkermansia Muciniphila and Gut Microbiota Richness are Associated with Improved Metabolic Status after Calorie Restriction. The FASED Journal, https://doi.org/10.1096/fasebj.29.1 supplement.601.3 , 2015.

27. K. Anjani, M. Lhomme, N. Sokolovska, C. Poitou, J.-L. Bouillot, P. Lesnik, P. Bedossa, A. Kontush, K. Clément, I. Dugail, J. Tordjman. Circulating phospholipid profiling identifies portal contribution to NASH signature in obesity, Journal of Hepatology, 62(4): 905 – 912, 2015.

28. L. Kong, P.-H. Wuillemin, J.-P. Bastard, N. Sokolovska, S. Gougis, S. Fellahi, F. Darakhshan, D. Bonnefont- Rousselot, R. Bittar, J. Doré, J.-D. Zucker, K. Clément, S. Rizkalla. Insulin resistance and inflammation predict kinetic body weight changes in response to dietary weight loss and maintenance in overweight and obese subjects using a Bayesian network approach. American Journal of Clinical Nutrition, doi:10.3945/ajcn.113.058099. Epub 2013, 2013.

29. N. Sokolovska, T. Lavergne, O. Cappé, F. Yvon. Efficient learning of sparse conditional random fields for supervised sequence labelling. IEEE J. Sel. Topics Signal Process., 4(6): 953-964, December 2010.

30. N. Sokolovska, O. Cappé, F. Yvon. Sélection de caractéristiques pour les champs aléatoires conditionnels par pénalisation L1. Traitement Automatique des langues, 50(3): 139-171. Numéro spécial, Apprentissage automatique pour le TAL, 2009

International Peer-Reviewed Conferences

1. M. Hérin, P. Perny, N. Sokolovska. Online Learning of Capacity-Based Preference Models. International Joint Conference on Artificial Intelligence (IJCAI), 2024, accepted.

2. M. Hérin, P. Perny, N. Sokolovska. Learning GAI-decomposable Utility Models for Multiattribute Decision Making. AAAI Conference on Artificial Intelligence (AAAI), 2024.

3. M. Hérin, P. Perny, N. Sokolovska. Learning Preference Models with Sparse Interactions of Criteria. International Joint Conference on Artificial Intelligence (IJCAI), 2023. 

4. M. Hérin, P. Perny, N. Sokolovska. Learning Sparse Representations of Preferences within Choquet Expected Utility Theory. Conference on Uncertainty in Artificial Intelligence (UAI), 2022.

5. K. Belahcène, N. Sokolovska, Y. Chevaleyre, J.-D. Zucker. Learning Interpretable Models using Soft Integrity Constraints. Asian Conference on Machine Learning (ACML), 529 – 544, 2020.

6. A. Atamna, N. Sokolovska, J.-C. Crivello. A Principled Approach to Analyze Expressiveness and Accuracy of Graph Neural Networks. International Symposium on Intelligent Data Analysis (IDA), 27 – 39, 2020.

7. M. Clertant, N. Sokolovska, Y. Chevaleyre, B. Hanczar. Interpretable Cascade Classifiers with Abstention, International Conference on Artificial Intelligence and Statistics (AISTATS), 2312 – 2320, 2019.

8. A. Nouira, N. Sokolovska, J.-C. Crivello. CrystalGAN : Learning to Discover Crystallographic Structures with Generative Adversarial Networks. AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE), 2019.

9. M. Kharouf, T. Rebafka, N. Sokolovska. Consistent Spectral Methods for Dimensionality Reduction, European Signal Processing Conference (EUSIPCO), 286 – 290, 2018.

10. N. Sokolovska, Y. Chevaleyre, J.-D. Zucker. A Provable Algorithm for Learning Interpretable Scoring Systems, International Conference on Artificial Intelligence and Statistics (AISTATS), 566 – 574, 2018.

11. N. Sokolovska, Y. Chevaleyre, J.-D. Zucker. Risk Scores Learned by Deep Restricted Boltzmann Machines with Trained Interval Quantization, International Conference on Machine Learning and Data Mining (MLDM), 421 – 435, 2018.

12. N. Sokolovska. O. Permiakova, S. K. Forslund, J.-D. Zucker. A Semi-supervised Approach to Discover Bivariate Causality in Large Biological Data, International Conference on Machine Learning and Data Mining (MLDM), 406 – 420, 2018.

13. N. Sokolovska, Y. Chevaleyre, J.-D. Zucker. The Fused Lasso Penalty for Learning Interpretable Medical Scoring Systems. International Joint Conference on Neural Networks (IJCNN), 4504 – 4511, 2017.

14. S. Affeldt, N. Sokolovska, E. Prifti, and J.-D. Zucker. Efficient Global Network Learning from Local Reconstructions. International Joint Conference on Neural Networks (IJCNN), 1117 – 1124, 2017.

15. N. Sokolovska, T. Artières. A Probabilistic Prior Knowledge Integration Method : Application to Gene- rative and Discriminative Models. International Joint Conference on Neural Networks (IJCNN), 4496 – 4503, 2016.

16. N. Sokolovska, H. T. Nguyen, K. Clément, J.-D. Zucker. Deep Self-Organising Maps for Efficient Hetero- geneous Biomedical Signatures Extraction. International Joint Conference on Neural Networks (IJCNN), 5079 – 5086, 2016.

17. N. Sokolovska, S. Rizkalla, K. Clément, J.-D. Zucker. Continuous and Discrete Deep Classifiers for Data Integration. International Symposium on Intelligent Data Analysis (IDA), 264 – 274, 2015

14. N. Sokolovska. Sparse Gradient-Based Direct Policy Search. International Conference on Neural Infor- mation Processing (ICONIP), pages 212-221, 2012

15. N. Sokolovska. Aspects of Semi-Supervised and Active Learning in Conditional Random Fields. In D. Gunopulos et al., editors, European Conference on Machine Learning (ECML) PKDD, pages 273-288, Springer-Verlag, 2011

16. N. Sokolovska, O. Teytaud, M. Milone. Q-Learning with Double Progressive Widening : Application to Robotics. In B.-L. Lu, L. Zhang, and J. Kwok, editors, International Conference on Neural Information Processing (ICONIP), pages 103-112, Springer-Verlag, 2011.

17. A. Couetoux, J.-B. Hoock, N. Sokolovska, O. Teytaud, N. Bonnard. Continuous Upper Confidence Trees. LION, 5th International Conference on Learning and Intelligent Optimization, 433 – 445, 2011.

18. R. Coulom, P. Rolet, N. Sokolovska, O. Teytaud. Handling Expensive Optimization with Large Noise. Foundations of Genetic Algorithms (FOGA), 61 – 68, 2011

19. R. Gaudel, J.-B. Hoock, J. Pérez, N. Sokolovska, O. Teytaud. A Principled Method for Exploiting Opening Books. In Proc. of International Conference on Computers and Games, 136 – 144, 2010.

20. N. Sokolovska, O. Cappé, and F. Yvon. The asymptotics of semi-supervised learning in discriminative probabilistic models. In A. McCallum and S. Roweis, editors, Proc. Int. Conf. Machine Learning (ICML), pages 984-991. Omnipress, 2008

National Peer-Reviewed Conferences

1. M. Hérin, P. Perny, N. Sokolovska. A Unified Approach to Learn Decision Models with Interactions. In 25ème congrès annuel de la société française de recherche opérationnelle et d'aide à la décision (ROADEF-24), 2024. Best Student paper.

2. T.H. Nguyen, E. Prifti, N. Sokolovska, J.-D. Zucker. Disease Prediction using Synthetic Image Representations of Metagenomic data and Convolutional Neural Networks, RIVF (Research, Innovation and Vision for the Future), 2019.

3. T.H. Nguyen, E. Prifti, Y. Chevaleyre, N. Sokolovska, J.-D. Zucker. Disease Classification in Metagenomics with 2D Embeddings and Deep Learning. CAp, 2018.

4. P. Bel Lassen, J. Aron-Wisnewsky, F. Charlotte, G. Le Naour, J. M. Oppert, J.L. Bouillot, J.-D. Zucker, C. Poitou, N. Sokolovska, K. Clément. Score semi-quantitatif de la fibrose du tissu adipeux sous cutan ́e humain : un nouvel outil pouraméliorer la prédiction de la réponse pondèrale au bypass gastrique. Diabetes et Metabolism. Congrès Annuel de la SFD et de la SFD Paramédical, 2017.

5. N. Sokolovska, O.Cappé, and F. Yvon. Analyse asymptotique de l’apprentissage semi-supervisé pour les modèles probabilistes discriminants. In Proceedings of Conf ́erence d’Apprentissage (CAP), Porquerolles, France, 2008.

International Peer-Reviewed Workshops

1. M. Hérin, P. Perny, N. Sokolovska. A dual approach for learning sparse representations of Choquet integrals. DA2PL (From Multiple Criteria Decision Aid to Preference Learning), 2022.

2. M. Hérin, P. Perny, N. Sokolovska. Learning Utilities and Sparse Representations of Capacities for Multicriteria Decision Making with the Bipolar Choquet Integral. IJCAI M-PREF, 2022.

3. M.V. Ruiz Cuevas, N. Sokolovska, P. H. Wuillemin, J.-D. Zucker. Detecting low-complexity confounders from data. CausalML: ICML/IJCAI/AAMAS Workshop on Machine Learning for Causal Inference, Counterfactual Prediction, and Autonomous Action, 2018.

4. T. H. Nguyen, Y. Chevaleyre, E. Prifti, N. Sokolovska, J.-D. Zucker. Deep Learning for Metagenomic Data: using 2D Embeddings and Convolutional Neural Networks. NIPS 2017 Workshop on Machine Learning for Healthcare.

5. N. Sokolovska, Y. Chevaleyre, J.-D. Zucker. Interpretable Score Learning by Fused Lasso and Integer Linear Programming, DA2PL (From Multiple Criteria Decision Aid to Preference Learning), 2016.

6. S. Affeldt, N. Sokolovska, E. Prifti, and J.-D. Zucker. Spectral Consensus Strategy for Accurate Reconstruction of Large Biological Networks. International Workshop on Machine Learning in Systems Biology, 2016.

7. N. Sokolovska, O. Teytaud, S. Rizkalla, K. Clément, J.-D. Zucker. Sparse Bandits for Functional Feature Selection. NIPS 2015 Workshop on Machine Learning in Computational Biology.

8. N. Sokolovska, M.-C. Dao, K. Clément, J.-D. Zucker. Probabilistic Causality for Exploration in Biomedical Heterogeneous Data. NIPS 2015 Workshop on Machine Learning for Healthcare.

Theses

1. Contributions to Probabilistic Machine Learning Methods: Interpretable Models, Network Reconstruction, and Causal Inference. HDR (Habilitation), 2019, pdf . Presentation slides pdf 

2. Contributions to the estimation of probabilistic discriminative models: semi-supervised learning and feature selection. PhD Thesis, 2010, pdf . Presentation slides, pdf  

3. Semantic Description of Text Clusters. Master Thesis, Vienna University of Technology, Department of Software Technology and Interactive Systems, 2006, pdf