The asymptotics of semi-supervised learning in discriminative probabilistic models. - Nataliya Sokolovska; Olivier Cappé; François Yvon (ICML, 2008)

Efficient Learning of Sparse Conditional Random Fields for Supervised Sequence Labelling - Nataliya Sokolovska; Thomas Lavergne; Olivier Cappé; François Yvon (CoRR, 2009)

Selecting features with L1 regularization in Conditional Random Fields. - Nataliya Sokolovska; Olivier Cappé; François Yvon (Trait. Autom. des Langues, 2009)

A Principled Method for Exploiting Opening Books. - Romaric Gaudel; Jean-Baptiste Hoock; Julien Perez; Nataliya Sokolovska; Olivier Teytaud (Computers and Games, 2010)

Efficient Learning of Sparse Conditional Random Fields for Supervised Sequence Labeling. - Nataliya Sokolovska; Thomas Lavergne; Olivier Cappé; François Yvon (IEEE J. Sel. Top. Signal Process., 2010)

Contributions to the estimation of probabilistic discriminative models: semi-supervised learning and feature selection. (Contributions à l'estimation de modèles probabilistes discriminants: apprentissage semi-supervisé et sélection de caractéristiques). - Nataliya Sokolovska (2010)

Handling expensive optimization with large noise. - Rémi Coulom; Philippe Rolet; Nataliya Sokolovska; Olivier Teytaud (FOGA, 2011)

Q-Learning with Double Progressive Widening: Application to Robotics. - Nataliya Sokolovska; Olivier Teytaud; Mario Milone (ICONIP (3), 2011)

Continuous Upper Confidence Trees. - Adrien Couëtoux; Jean-Baptiste Hoock; Nataliya Sokolovska; Olivier Teytaud; Nicolas Bonnard (LION, 2011)

Aspects of Semi-supervised and Active Learning in Conditional Random Fields. - Nataliya Sokolovska (ECML/PKDD (3), 2011)

Sparse Gradient-Based Direct Policy Search. - Nataliya Sokolovska (ICONIP (4), 2012)

Continuous and Discrete Deep Classifiers for Data Integration. - Nataliya Sokolovska; Salwa Rizkalla; Karine Clément; Jean-Daniel Zucker (IDA, 2015)

A probabilistic prior knowledge integration method: Application to generative and discriminative models. - Nataliya Sokolovska; Thierry Artières (IJCNN, 2016)

Deep Self-Organising Maps for efficient heterogeneous biomedical signatures extraction. - Nataliya Sokolovska; Nguyen Thanh Hai 001; Karine Clément; Jean-Daniel Zucker (IJCNN, 2016)

Spectral consensus strategy for accurate reconstruction of large biological networks. - Séverine Affeldt; Nataliya Sokolovska; Edi Prifti; Jean-Daniel Zucker (BMC Bioinform., 2016)

Deep kernel dimensionality reduction for scalable data integration. - Nataliya Sokolovska; Karine Clément; Jean-Daniel Zucker (Int. J. Approx. Reason., 2016)

Efficient global network learning from local reconstructions. - Séverine Affeldt; Nataliya Sokolovska; Edi Prifti; Jean-Daniel Zucker (IJCNN, 2017)

The fused lasso penalty for learning interpretable medical scoring systems. - Nataliya Sokolovska; Yann Chevaleyre; Karine Clément; Jean-Daniel Zucker (IJCNN, 2017)

Deep Learning for Metagenomic Data: using 2D Embeddings and Convolutional Neural Networks. - Thanh Hai Nguyen 001; Yann Chevaleyre; Edi Prifti; Nataliya Sokolovska; Jean-Daniel Zucker (CoRR, 2017)

A Provable Algorithm for Learning Interpretable Scoring Systems. - Nataliya Sokolovska; Yann Chevaleyre; Jean-Daniel Zucker (AISTATS, 2018)

Consistent Spectral Methods for Dimensionality Reduction. - Malika Kharouf; Tabea Rebafka; Nataliya Sokolovska (EUSIPCO, 2018)

Risk Scores Learned by Deep Restricted Boltzmann Machines with Trained Interval Quantization. - Nataliya Sokolovska; Yann Chevaleyre; Jean-Daniel Zucker (MLDM (1), 2018)

A Semi-supervised Approach to Discover Bivariate Causality in Large Biological Data. - Nataliya Sokolovska; Olga Permiakova; Sofia K. Forslund; Jean-Daniel Zucker (MLDM (1), 2018)

Disease Classification in Metagenomics with 2D Embeddings and Deep Learning. - Thanh Hai Nguyen 001; Edi Prifti; Yann Chevaleyre; Nataliya Sokolovska; Jean-Daniel Zucker (CoRR, 2018)

CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networks. - Asma Nouira; Nataliya Sokolovska; Jean-Claude Crivello (AAAI Spring Symposium: Combining Machine Learning with Knowledge Engineering, 2019)

Interpretable Cascade Classifiers with Abstention. - Matthieu Clertant; Nataliya Sokolovska; Yann Chevaleyre; Blaise Hanczar (AISTATS, 2019)

Disease Prediction Using Synthetic Image Representations of Metagenomic Data and Convolutional Neural Networks. - Thanh Hai Nguyen 001; Edi Prifti; Nataliya Sokolovska; Jean-Daniel Zucker (RIVF, 2019)

Robust structure measures of metabolic networks that predict prokaryotic optimal growth temperature. - Adèle Weber Zendrera; Nataliya Sokolovska; Hédi Soula (BMC Bioinform., 2019)

Revealing causality between heterogeneous data sources with deep restricted Boltzmann machines. - Nataliya Sokolovska; Karine Clément; Jean-Daniel Zucker (Inf. Fusion, 2019)

Learning Interpretable Models using Soft Integrity Constraints. - Khaled Belahcène; Nataliya Sokolovska; Yann Chevaleyre; Jean-Daniel Zucker (ACML, 2020)

A Principled Approach to Analyze Expressiveness and Accuracy of Graph Neural Networks. - Asma Atamna; Nataliya Sokolovska; Jean-Claude Crivello (IDA, 2020)

Latent Instrumental Variables as Priors in Causal Inference based on Independence of Cause and Mechanism. - Nataliya Sokolovska; Pierre-Henri Wuillemin (CoRR, 2020)

Supervised deep learning prediction of the formation enthalpy of the full set of configurations in complex phases: the σ-phase as an example. - Jean-Claude Crivello; Nataliya Sokolovska; Jean-Marc Joubert (CoRR, 2020)

Using Unlabeled Data to Discover Bivariate Causality with Deep Restricted Boltzmann Machines. - Nataliya Sokolovska; Olga Permiakova; Sofia K. Forslund; Jean-Daniel Zucker (IEEE ACM Trans. Comput. Biol. Bioinform., 2020)

The Role of Instrumental Variables in Causal Inference Based on Independence of Cause and Mechanism. - Nataliya Sokolovska; Pierre-Henri Wuillemin (Entropy, 2021)

Probabilistic personalised cascade with abstention. - Tatiana Shpakova; Nataliya Sokolovska (Pattern Recognit. Lett., 2021)

Vanishing boosted weights: A consistent algorithm to learn interpretable rules. - Nataliya Sokolovska; Yasser Mohseni Behbahani (Pattern Recognit. Lett., 2021)

Learning sparse representations of preferences within Choquet expected utility theory. - Margot Herin; Patrice Perny; Nataliya Sokolovska (UAI, 2022)

Learning Preference Models with Sparse Interactions of Criteria. - Margot Herin; Patrice Perny; Nataliya Sokolovska (IJCAI, 2023)

Data-Driven Score-Based Models for Generating Stable Structures with Adaptive Crystal Cells. - Arsen Sultanov; Jean-Claude Crivello; Tabea Rebafka; Nataliya Sokolovska (J. Chem. Inf. Model., 2023)

Latent dirichlet allocation for double clustering (LDA-DC): discovering patients phenotypes and cell populations within a single Bayesian framework. - Elie-Julien El Hachem; Nataliya Sokolovska; Hédi Soula (BMC Bioinform., 2023)

Learning GAI-Decomposable Utility Models for Multiattribute Decision Making. - Margot Herin; Patrice Perny; Nataliya Sokolovska (AAAI, 2024)

Noise-Tolerant Active Preference Learning for Multicriteria Choice Problems. - Margot Herin; Patrice Perny; Nataliya Sokolovska (ADT, 2024)

Online Learning of Capacity-Based Preference Models. - Margot Herin; Patrice Perny; Nataliya Sokolovska (IJCAI, 2024)

Learning preference representations based on Choquet integrals for multicriteria decision making. - Margot Herin; Patrice Perny; Nataliya Sokolovska (Ann. Math. Artif. Intell., 2024)

Conformal novelty detection for multiple metabolic networks. - Ariane Marandon; Tabea Rebafka; Nataliya Sokolovska; Hédi Soula (BMC Bioinform., 2024)

Bottom-up Iterative Anomalous Diffusion Detector (BI-ADD). - Junwoo Park; Nataliya Sokolovska; Clément Cabriel; Ignacio Izeddin; Judith Miné-Hattab (CoRR, 2025)