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Posts by Collection
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publications
A Constructive Approximation of Interpolating Bézier Curves on Riemannian Symmetric Spaces
Published in Journal of Optim. Theory and Appl., 2020
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Bayesian Inference on Local Distributions of Functions and Multidimensional Curves with Spherical HMC Sampling
Published in In Information Geometry and Learning, Les Houches, France, July 27-31, 2020, 2020
Recommended citation: Anis Fradi, Ines Adouani, Chafik Samir, GSI 2020. https://doi.org/10.1007/978-3-030-77957-3\_19
An Optimization Method for Accurate Nonparametric Regressions on Stiefel Manifolds
Published in In Machine Learning, Optimization and Data Science, Grasmere, UK, October 4-8, 2021, 2021
Recommended citation: Ines Adouani, Chafik Samir, LOD 2021. https://doi.org/10.1007/978-3-030-95470-3\_25
Bayesian regression and classification using Gaussian process priors indexed by probability density functions
Published in Journal of Inf. Sci., 2021
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Recommended citation: Anis Fradi, Yan Feunteun, Chafik Samir, M. Baklouti, François Bachoc, Jean-Michel Loubes, Journal of Inf. Sci., 2021.
Coordinate descent optimization for one-to-one correspondence and supervised classification of 3D shapes
Published in Journal of Appl. Math. Comput., 2021
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Recommended citation: Chafik Samir, Wen Huang, Journal of Appl. Math. Comput., 2021.
A scalable Matèrn Gaussian process for learning spatial curves distributions
Published in Journal of Inf. Sci., 2022
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Recommended citation: Tam Tran, Yan Feunteun, Chafik Samir, José Braga" Journal of Inf. Sci., 2022.
Nonparametric Bayesian Regression and Classification on Manifolds, With Applications to 3D Cochlear Shapes
Published in Journal of IEEE Trans. Image Process., 2022
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Recommended citation: Anis Fradi, Chafik Samir, José Braga, Shantanu Joshi, Jean-Michel Loubes" Journal of IEEE Trans. Image Process., 2022.
On Studying the Effect of Data Quality on Classification Performances
Published in In the proceedings of the 23rd International Conference on Intelligent Data Engineering and Automated Learning , IDEAL 2022, Manchester, UK, November 24-26, 2022, 2022
Recommended citation: Roxane Jouseau, Sébastien Salva, Chafik Samir, DEAL 2022. https://doi.org/10.1007/978-3-031-21753-1\_9
Manifold-valued data: Learning, Registering and Clustering Shapes of Curves
Published in In the proceedings of the 12th International Symposium on Information and Communication Technology, Ho Chi Minh, Vietnam, December 7-8, 2023, 2023
Recommended citation: Anis Fradi, Chafik Samir, José Braga, SOICT 2023. https://doi.org/10.1145/3628797.3628903
Learning and Regression on manifolds: Data on Grassmannian manifolds
Published in PR International Conference on Artificial Intelligence, Jakarta, Indonesia, November 15-19, 2023, 2023
Recommended citation: Anis Fradi, Chafik Samir, PRICAI 2023. https://doi.org/10.1007/978-981-99-7022-3\_6
Learning, inference, and prediction on manifolds: constrained Gaussian processes on PDFs
Published in Journal of Inf. Sci., 2023
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Recommended citation: Tien Tam Tran, Anis Fradi, Chafik Samir, " Journal of Inf. Sci., 2023.
Manifold-valued data: Regression and Fitting on PDFs
Published in Journal of Comput. Appl. Math., 2023
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Recommended citation: Ines Adouani, Chafik Samir, "Numerical algorithms for spline interpolation on space of probability density functions." Journal of Comput. Appl. Math., 2023.
Machine learning on manifolds: Classifying PDFs
Published in In the proceedings of the European Conference on Machine Learning, ECML , Turin, Italy, September 18-22, 2023, 2023
Recommended citation: Anis Fradi, Chafik Samir, ECML 2023. [doi](10.1007/978-3-031-43412-9_30)
talks
Learn, predict and infer on Probability Density Functions
Published:
A short description:
- Introduce a new manifold structure for the space of PDFs.
- Apply constrained GP to learn and infer on PDF.
- Extend to CDFs.
- Use spherical HMC sampling to solve the posterior probability on coefficients.
- A framework that can be generalized to other manifolds.
- Some applications in medical
Bayesian Optimization for Classifying Probability Density Functions
Published:
A shhort description:
- Data representation leads to space of representations
- PDFs and densities to represent data
- Some limitations to extend ML techniques to non-flat spaces
- Examples from functional data
- A new Bayesian optimization method
- Examples from applications
Tutorial: Transfer Learning on Riemannian Manifolds
Published:
A short description:
- Study the geometry structure of some Riemannian manifolds
- Definitions and characterisation of transfer learning
- Statistical models and the geometric structure of the underlying space
- Choice of appropriate metrics
- Transfer learning algorithms of statistical models with parallel transport.
- Applications and Extensions
Regression on Grassmannian Manifolds
Published:
A short description:
- Problem formulations: Example of Euclidean least-squares estimate
- Problem formulations: Example of geodesic least-squares estimate
- Problem formulation on the Grassmannian: regression model
- The geometric structure
- The Bayezian optimization with Hamiltonian dynamics
- Applications
Learning and inferring on Grassmannian Manifolds: With Applications on Schizophrenia Data
Published:
A short description:
- Motivations and applications
- Linear and Geodesic regression
- Background: Stiefel and Grassmann manifolds
- Problem formulation : The regression model
- Monte Carlo Sampling on Grassmannian manifolds (MCG)
- Experimental results
- Discussion
Learn to Transfer Statistical Models from and to Populations
Published:
A short description:
- Motivations and applications
- Background
- Problem formulation
- Constraints, hypothesises, and properties
- Experimental results
- Open problems
teaching
Artficial Intelligence
Master and Bachelor course, UCA, polytechnique, 2018
A short description: Machine/Deep Learning inro, Data (exploration, cleaning, representation, visualisation), Standard and advanced/deep Models, Learning/Validation/Testing strategies, Performance measures, Overfitting, Cost functions, Optimisation, Use case, deployment pipeline, Ethical and societal issues.
Learning with Gaussian processes
Master course, UCA, 2019
The main objective of this course is to introduce spatial and temporal stochastic processes for fitting and prediction:
- Examples and definition
- Multivariate Gaussian distribution
- Covariances and kernels
- Uncertainty, Regression, Prediction, and Classification
- MLE, MCMC, EP, MAP, etc.
- Applications: Signals/functions, times series, images, etc.
Optimization of Cost Functions for Learning
Undergraduate Course, UCA, 2023
A short description: …
Foundations of Machine Learning
Undergraduate course, UCA, 2023
A short description…
Machine learning models for decision-making
Bachelor course, UCA, 2023
- The main objective of this course is to take in charge simple and interpretable models for decision making with ML. Each model is tested on a use case with respect to the following steps: Modeling the problem, exploring data, choosing and optimizing a model, analyzing and interpreting the results before validating the decision.
- Programming language: Python and libraries
Image, Signal and Visualization
Undergraduate course, UCA, 2024
- This course is an intruduction to image and signal processing with OpenCV.
- Basic notions:
- Representation
- Transformation and extraction
- Visualization
- Convolution