Nataliya Sokolovska

49 papers A* 4A 4B 6C 1Journal 22Unranked 11

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Bottom-up Iterative Anomalous Diffusion Detector (BI-ADD). CoRR Junwoo Park, Nataliya Sokolovska, Clément Cabriel, Ignacio Izeddin, Judith Miné-Hattab
2024	J	`jnl`	Conformal novelty detection for multiple metabolic networks. BMC Bioinform. Ariane Marandon, Tabea Rebafka, Nataliya Sokolovska, Hédi Soula
2024	A*	`conf`	Learning GAI-Decomposable Utility Models for Multiattribute Decision Making. AAAI Margot Herin, Patrice Perny, Nataliya Sokolovska
2024	J	`jnl`	Learning preference representations based on Choquet integrals for multicriteria decision making. Ann. Math. Artif. Intell. Margot Herin, Patrice Perny, Nataliya Sokolovska
2024	—	`conf`	Noise-Tolerant Active Preference Learning for Multicriteria Choice Problems. ADT Margot Herin, Patrice Perny, Nataliya Sokolovska
2024	A*	`conf`	Online Learning of Capacity-Based Preference Models. IJCAI Margot Herin, Patrice Perny, Nataliya Sokolovska
2023	J	`jnl`	Data-Driven Score-Based Models for Generating Stable Structures with Adaptive Crystal Cells. CoRR Arsen Sultanov, Jean-Claude Crivello, Tabea Rebafka, Nataliya Sokolovska
2023	J	`jnl`	Data-Driven Score-Based Models for Generating Stable Structures with Adaptive Crystal Cells. J. Chem. Inf. Model. Arsen Sultanov, Jean-Claude Crivello, Tabea Rebafka, Nataliya Sokolovska
2023	J	`jnl`	Latent dirichlet allocation for double clustering (LDA-DC): discovering patients phenotypes and cell populations within a single Bayesian framework. BMC Bioinform. Elie-Julien El Hachem, Nataliya Sokolovska, Hédi Soula
2023	A*	`conf`	Learning Preference Models with Sparse Interactions of Criteria. IJCAI Margot Herin, Patrice Perny, Nataliya Sokolovska
2022	A	`conf`	Learning sparse representations of preferences within Choquet expected utility theory. UAI Margot Herin, Patrice Perny, Nataliya Sokolovska
2021	J	`jnl`	Probabilistic personalised cascade with abstention. Pattern Recognit. Lett. Tatiana Shpakova, Nataliya Sokolovska
2021	J	`jnl`	The Role of Instrumental Variables in Causal Inference Based on Independence of Cause and Mechanism. Entropy Nataliya Sokolovska, Pierre-Henri Wuillemin
2021	J	`jnl`	Vanishing boosted weights: A consistent algorithm to learn interpretable rules. Pattern Recognit. Lett. Nataliya Sokolovska, Yasser Mohseni Behbahani
2020	B	`conf`	A Principled Approach to Analyze Expressiveness and Accuracy of Graph Neural Networks. IDA Asma Atamna, Nataliya Sokolovska, Jean-Claude Crivello
2020	J	`jnl`	Latent Instrumental Variables as Priors in Causal Inference based on Independence of Cause and Mechanism. CoRR Nataliya Sokolovska, Pierre-Henri Wuillemin
2020	C	`conf`	Learning Interpretable Models using Soft Integrity Constraints. ACML Khaled Belahcène, Nataliya Sokolovska, Yann Chevaleyre, Jean-Daniel Zucker
2020	J	`jnl`	Supervised deep learning prediction of the formation enthalpy of the full set of configurations in complex phases: the σ-phase as an example. CoRR Jean-Claude Crivello, Nataliya Sokolovska, Jean-Marc Joubert
2020	J	`jnl`	Using Unlabeled Data to Discover Bivariate Causality with Deep Restricted Boltzmann Machines. IEEE ACM Trans. Comput. Biol. Bioinform. Nataliya Sokolovska, Olga Permiakova, Sofia K. Forslund, Jean-Daniel Zucker
2019	—	`conf`	CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networks. AAAI Spring Symposium: Combining Machine Learning with Knowledge Engineering Asma Nouira, Nataliya Sokolovska, Jean-Claude Crivello
2019	—	`conf`	Disease Prediction Using Synthetic Image Representations of Metagenomic Data and Convolutional Neural Networks. RIVF Thanh Hai Nguyen, Edi Prifti, Nataliya Sokolovska, Jean-Daniel Zucker
2019	A	`conf`	Interpretable Cascade Classifiers with Abstention. AISTATS Matthieu Clertant, Nataliya Sokolovska, Yann Chevaleyre, Blaise Hanczar
2019	J	`jnl`	Revealing causality between heterogeneous data sources with deep restricted Boltzmann machines. Inf. Fusion Nataliya Sokolovska, Karine Clément, Jean-Daniel Zucker
2019	J	`jnl`	Robust structure measures of metabolic networks that predict prokaryotic optimal growth temperature. BMC Bioinform. Adèle Weber Zendrera, Nataliya Sokolovska, Hédi Soula
2018	A	`conf`	A Provable Algorithm for Learning Interpretable Scoring Systems. AISTATS Nataliya Sokolovska, Yann Chevaleyre, Jean-Daniel Zucker
2018	—	`conf`	A Semi-supervised Approach to Discover Bivariate Causality in Large Biological Data. MLDM (1) Nataliya Sokolovska, Olga Permiakova, Sofia K. Forslund, Jean-Daniel Zucker
2018	—	`conf`	Consistent Spectral Methods for Dimensionality Reduction. EUSIPCO Malika Kharouf, Tabea Rebafka, Nataliya Sokolovska
2018	J	`jnl`	CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networks. CoRR Asma Nouira, Jean-Claude Crivello, Nataliya Sokolovska
2018	J	`jnl`	Disease Classification in Metagenomics with 2D Embeddings and Deep Learning. CoRR Thanh Hai Nguyen, Edi Prifti, Yann Chevaleyre, Nataliya Sokolovska, Jean-Daniel Zucker
2018	—	`conf`	Risk Scores Learned by Deep Restricted Boltzmann Machines with Trained Interval Quantization. MLDM (1) Nataliya Sokolovska, Yann Chevaleyre, Jean-Daniel Zucker
2017	J	`jnl`	Deep Learning for Metagenomic Data: using 2D Embeddings and Convolutional Neural Networks. CoRR Thanh Hai Nguyen, Yann Chevaleyre, Edi Prifti, Nataliya Sokolovska, Jean-Daniel Zucker
2017	B	`conf`	Efficient global network learning from local reconstructions. IJCNN Séverine Affeldt, Nataliya Sokolovska, Edi Prifti, Jean-Daniel Zucker
2017	B	`conf`	The fused lasso penalty for learning interpretable medical scoring systems. IJCNN Nataliya Sokolovska, Yann Chevaleyre, Karine Clément, Jean-Daniel Zucker
2016	B	`conf`	A probabilistic prior knowledge integration method: Application to generative and discriminative models. IJCNN Nataliya Sokolovska, Thierry Artières
2016	B	`conf`	Deep Self-Organising Maps for efficient heterogeneous biomedical signatures extraction. IJCNN Nataliya Sokolovska, Nguyen Thanh Hai, Karine Clément, Jean-Daniel Zucker
2016	J	`jnl`	Deep kernel dimensionality reduction for scalable data integration. Int. J. Approx. Reason. Nataliya Sokolovska, Karine Clément, Jean-Daniel Zucker
2016	J	`jnl`	Spectral consensus strategy for accurate reconstruction of large biological networks. BMC Bioinform. Séverine Affeldt, Nataliya Sokolovska, Edi Prifti, Jean-Daniel Zucker
2015	B	`conf`	Continuous and Discrete Deep Classifiers for Data Integration. IDA Nataliya Sokolovska, Salwa Rizkalla, Karine Clément, Jean-Daniel Zucker
2012	—	`conf`	Sparse Gradient-Based Direct Policy Search. ICONIP (4) Nataliya Sokolovska
2011	—	`conf`	Aspects of Semi-supervised and Active Learning in Conditional Random Fields. ECML/PKDD (3) Nataliya Sokolovska
2011	—	`conf`	Continuous Upper Confidence Trees. LION Adrien Couëtoux, Jean-Baptiste Hoock, Nataliya Sokolovska, Olivier Teytaud, Nicolas Bonnard
2011	A	`conf`	Handling expensive optimization with large noise. FOGA Rémi Coulom, Philippe Rolet, Nataliya Sokolovska, Olivier Teytaud
2011	—	`conf`	Q-Learning with Double Progressive Widening: Application to Robotics. ICONIP (3) Nataliya Sokolovska, Olivier Teytaud, Mario Milone
2010	—	`conf`	A Principled Method for Exploiting Opening Books. Computers and Games Romaric Gaudel, Jean-Baptiste Hoock, Julien Perez, Nataliya Sokolovska, Olivier Teytaud
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	J	`jnl`	Efficient Learning of Sparse Conditional Random Fields for Supervised Sequence Labeling. IEEE J. Sel. Top. Signal Process. Nataliya Sokolovska, Thomas Lavergne, Olivier Cappé, François Yvon
2009	J	`jnl`	Efficient Learning of Sparse Conditional Random Fields for Supervised Sequence Labelling CoRR Nataliya Sokolovska, Thomas Lavergne, Olivier Cappé, François Yvon
2009	J	`jnl`	Selecting features with L1 regularization in Conditional Random Fields. Trait. Autom. des Langues Nataliya Sokolovska, Olivier Cappé, François Yvon
2008	A*	`conf`	The asymptotics of semi-supervised learning in discriminative probabilistic models. ICML Nataliya Sokolovska, Olivier Cappé, François Yvon